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import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = ''''''
UpperCamelCase__ = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
UpperCamelCase__ = None # compression type in fsspec. ex: "gzip"
UpperCamelCase__ = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self :str , __magic_name__ :str = "" , __magic_name__ :Optional[str] = None , __magic_name__ :Optional[dict] = None , **__magic_name__ :Union[str, Any] ):
'''simple docstring'''
super().__init__(self , **__magic_name__ )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
a = fsspec.open(
__magic_name__ , mode="""rb""" , protocol=__magic_name__ , compression=self.compression , client_kwargs={
"""requote_redirect_url""": False, # see https://github.com/huggingface/datasets/pull/5459
"""trust_env""": True, # Enable reading proxy env variables.
**(target_options or {}).pop("""client_kwargs""" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
a = os.path.basename(self.file.path.split("""::""" )[0] )
a = (
self.compressed_name[: self.compressed_name.rindex(""".""" )]
if """.""" in self.compressed_name
else self.compressed_name
)
a = None
@classmethod
def lowerCamelCase__ ( cls :Dict , __magic_name__ :Union[str, Any] ):
'''simple docstring'''
return super()._strip_protocol(__magic_name__ ).lstrip("""/""" )
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
if self.dir_cache is None:
a = {**self.file.fs.info(self.file.path ), """name""": self.uncompressed_name}
a = {f["""name"""]: f}
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :str ):
'''simple docstring'''
return self.file.open().read()
def lowerCamelCase__ ( self :Optional[Any] , __magic_name__ :str , __magic_name__ :str = "rb" , __magic_name__ :List[str]=None , __magic_name__ :Optional[Any]=True , __magic_name__ :Dict=None , **__magic_name__ :Tuple , ):
'''simple docstring'''
a = self._strip_protocol(__magic_name__ )
if mode != "rb":
raise ValueError(F'Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'' )
return self.file.open()
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''bz2'''
UpperCamelCase__ = '''bz2'''
UpperCamelCase__ = '''.bz2'''
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''gzip'''
UpperCamelCase__ = '''gzip'''
UpperCamelCase__ = '''.gz'''
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''lz4'''
UpperCamelCase__ = '''lz4'''
UpperCamelCase__ = '''.lz4'''
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''xz'''
UpperCamelCase__ = '''xz'''
UpperCamelCase__ = '''.xz'''
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''zstd'''
UpperCamelCase__ = '''zstd'''
UpperCamelCase__ = '''.zst'''
def __init__( self :List[str] , __magic_name__ :str , __magic_name__ :str = "rb" , __magic_name__ :Optional[str] = None , __magic_name__ :Optional[dict] = None , __magic_name__ :int = DEFAULT_BLOCK_SIZE , **__magic_name__ :str , ):
'''simple docstring'''
super().__init__(
fo=__magic_name__ , mode=__magic_name__ , target_protocol=__magic_name__ , target_options=__magic_name__ , block_size=__magic_name__ , **__magic_name__ , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
a = self.file.__enter__
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :Union[str, Any] ):
'''simple docstring'''
a = file_
def __enter__( self :Optional[Any] ):
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self :str , *__magic_name__ :int , **__magic_name__ :Union[str, Any] ):
'''simple docstring'''
self._file.__exit__(*__magic_name__ , **__magic_name__ )
def __iter__( self :Optional[int] ):
'''simple docstring'''
return iter(self._file )
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return next(self._file )
def __getattr__( self :Optional[int] , __magic_name__ :Optional[Any] ):
'''simple docstring'''
return getattr(self._file , __magic_name__ )
def fixed_enter(*__magic_name__ :str , **__magic_name__ :Optional[int] ):
return WrappedFile(_enter(*__magic_name__ , **__magic_name__ ) )
a = fixed_enter
| 361 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = CanineTokenizer
UpperCamelCase__ = False
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
super().setUp()
a = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def lowerCamelCase__ ( self :Tuple , **__magic_name__ :Dict ):
'''simple docstring'''
a = self.tokenizer_class.from_pretrained(self.tmpdirname , **__magic_name__ )
a = 1024
return tokenizer
@require_torch
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.canine_tokenizer
a = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
a = [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
# fmt: on
a = tokenizer(__magic_name__ , padding=__magic_name__ , return_tensors="""pt""" )
self.assertIsInstance(__magic_name__ , __magic_name__ )
a = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__magic_name__ , __magic_name__ )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.canine_tokenizer
a = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
a = tokenizer(__magic_name__ , padding=__magic_name__ , return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" , __magic_name__ )
self.assertIn("""attention_mask""" , __magic_name__ )
self.assertIn("""token_type_ids""" , __magic_name__ )
@require_torch
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.canine_tokenizer
a = [
"""What's the weater?""",
"""It's about 25 degrees.""",
]
a = tokenizer(
text_target=__magic_name__ , max_length=32 , padding="""max_length""" , truncation=__magic_name__ , return_tensors="""pt""" )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
a = tempfile.mkdtemp()
a = """ He is very happy, UNwant\u00E9d,running"""
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
tokenizer.save_pretrained(__magic_name__ )
a = tokenizer.__class__.from_pretrained(__magic_name__ )
a = after_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
shutil.rmtree(__magic_name__ )
a = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
a = tempfile.mkdtemp()
a = """ He is very happy, UNwant\u00E9d,running"""
a = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
a = chr(0Xe_0_0_7 )
additional_special_tokens.append(__magic_name__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
tokenizer.save_pretrained(__magic_name__ )
a = tokenizer.__class__.from_pretrained(__magic_name__ )
a = after_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
self.assertIn(__magic_name__ , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
a = tokenizer.__class__.from_pretrained(__magic_name__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(__magic_name__ )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a , a = self.get_clean_sequence(__magic_name__ )
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_5
a = chr(__magic_name__ )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(len(__magic_name__ ) , 1 )
a = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(__magic_name__ , input_encoded + special_token_id )
a = tokenizer.decode(__magic_name__ , skip_special_tokens=__magic_name__ )
self.assertTrue(special_token not in decoded )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = chr(0Xe_0_0_5 )
a = chr(0Xe_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=__magic_name__ )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]} )
a = tokenizer.tokenize(__magic_name__ )
a = tokenizer.tokenize(__magic_name__ )
self.assertEqual(len(__magic_name__ ) , 1 )
self.assertEqual(len(__magic_name__ ) , 1 )
self.assertEqual(token_a[0] , __magic_name__ )
self.assertEqual(token_a[0] , __magic_name__ )
@require_tokenizers
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_6
a = chr(__magic_name__ )
a = AddedToken(__magic_name__ , lstrip=__magic_name__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(__magic_name__ )
tokenizer.from_pretrained(__magic_name__ )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__magic_name__ )
with open(os.path.join(__magic_name__ , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
a = json.load(__magic_name__ )
with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
a = json.load(__magic_name__ )
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_6
a = chr(__magic_name__ )
a = [new_token_a]
a = [new_token_a]
with open(os.path.join(__magic_name__ , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__magic_name__ , __magic_name__ )
with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__magic_name__ , __magic_name__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
a = tokenizer_class.from_pretrained(__magic_name__ , extra_ids=0 )
self.assertIn(__magic_name__ , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
a = 0Xe_0_0_7
a = chr(__magic_name__ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
a = [AddedToken(__magic_name__ , lstrip=__magic_name__ )]
a = tokenizer_class.from_pretrained(
__magic_name__ , additional_special_tokens=__magic_name__ , extra_ids=0 )
self.assertIn(__magic_name__ , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = """hello world"""
if self.space_between_special_tokens:
a = """[CLS] hello world [SEP]"""
else:
a = input
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.decode(__magic_name__ , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(__magic_name__ , [output, output.lower()] )
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
a = """a"""
a = ord(__magic_name__ )
for attr in attributes_list:
setattr(__magic_name__ , attr + """_id""" , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , __magic_name__ ) , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , attr + """_id""" ) , __magic_name__ )
setattr(__magic_name__ , attr + """_id""" , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , __magic_name__ ) , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , attr + """_id""" ) , __magic_name__ )
setattr(__magic_name__ , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens_ids""" ) , [] )
a = 0Xe_0_0_6
a = chr(__magic_name__ )
setattr(__magic_name__ , """additional_special_tokens_ids""" , [additional_special_token_id] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens""" ) , [additional_special_token] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens_ids""" ) , [additional_special_token_id] )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
| 347 | 0 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
a = """encrypt"""
a = encrypt_message(__lowerCamelCase , __lowerCamelCase )
elif mode.lower().startswith("""d""" ):
a = """decrypt"""
a = decrypt_message(__lowerCamelCase , __lowerCamelCase )
print(f'\n{mode.title()}ed message:' )
print(__lowerCamelCase )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """encrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """decrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
a = []
a = 0
a = key.upper()
for symbol in message:
a = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(__lowerCamelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(__lowerCamelCase ):
a = 0
else:
translated.append(__lowerCamelCase )
return "".join(__lowerCamelCase )
if __name__ == "__main__":
main() | 362 |
def __A ( __lowerCamelCase ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = 42
@flax_register_to_config
class __lowerCAmelCase ( nn.Module , __magic_name__ , __magic_name__ ):
UpperCamelCase__ = 32
UpperCamelCase__ = 4
UpperCamelCase__ = 4
UpperCamelCase__ = (
'''CrossAttnDownBlock2D''',
'''CrossAttnDownBlock2D''',
'''CrossAttnDownBlock2D''',
'''DownBlock2D''',
)
UpperCamelCase__ = ('''UpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''')
UpperCamelCase__ = False
UpperCamelCase__ = (320, 640, 1280, 1280)
UpperCamelCase__ = 2
UpperCamelCase__ = 8
UpperCamelCase__ = None
UpperCamelCase__ = 1280
UpperCamelCase__ = 0.0
UpperCamelCase__ = False
UpperCamelCase__ = jnp.floataa
UpperCamelCase__ = True
UpperCamelCase__ = 0
UpperCamelCase__ = False
def lowerCamelCase__ ( self :List[str] , __magic_name__ :jax.random.KeyArray ):
'''simple docstring'''
a = (1, self.in_channels, self.sample_size, self.sample_size)
a = jnp.zeros(__magic_name__ , dtype=jnp.floataa )
a = jnp.ones((1,) , dtype=jnp.intaa )
a = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
a , a = jax.random.split(__magic_name__ )
a = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )["params"]
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.block_out_channels
a = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
a = self.num_attention_heads or self.attention_head_dim
# input
a = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
a = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
a = FlaxTimestepEmbedding(__magic_name__ , dtype=self.dtype )
a = self.only_cross_attention
if isinstance(__magic_name__ , __magic_name__ ):
a = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__magic_name__ , __magic_name__ ):
a = (num_attention_heads,) * len(self.down_block_types )
# down
a = []
a = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
a = output_channel
a = block_out_channels[i]
a = i == len(__magic_name__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
a = FlaxCrossAttnDownBlockaD(
in_channels=__magic_name__ , out_channels=__magic_name__ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
a = FlaxDownBlockaD(
in_channels=__magic_name__ , out_channels=__magic_name__ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__magic_name__ )
a = down_blocks
# mid
a = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
a = []
a = list(reversed(__magic_name__ ) )
a = list(reversed(__magic_name__ ) )
a = list(reversed(__magic_name__ ) )
a = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
a = output_channel
a = reversed_block_out_channels[i]
a = reversed_block_out_channels[min(i + 1 , len(__magic_name__ ) - 1 )]
a = i == len(__magic_name__ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
a = FlaxCrossAttnUpBlockaD(
in_channels=__magic_name__ , out_channels=__magic_name__ , prev_output_channel=__magic_name__ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
a = FlaxUpBlockaD(
in_channels=__magic_name__ , out_channels=__magic_name__ , prev_output_channel=__magic_name__ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__magic_name__ )
a = output_channel
a = up_blocks
# out
a = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
a = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self :List[str] , __magic_name__ :Union[str, Any] , __magic_name__ :Optional[Any] , __magic_name__ :str , __magic_name__ :Tuple=None , __magic_name__ :List[str]=None , __magic_name__ :bool = True , __magic_name__ :bool = False , ):
'''simple docstring'''
if not isinstance(__magic_name__ , jnp.ndarray ):
a = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__magic_name__ , jnp.ndarray ) and len(timesteps.shape ) == 0:
a = timesteps.astype(dtype=jnp.floataa )
a = jnp.expand_dims(__magic_name__ , 0 )
a = self.time_proj(__magic_name__ )
a = self.time_embedding(__magic_name__ )
# 2. pre-process
a = jnp.transpose(__magic_name__ , (0, 2, 3, 1) )
a = self.conv_in(__magic_name__ )
# 3. down
a = (sample,)
for down_block in self.down_blocks:
if isinstance(__magic_name__ , __magic_name__ ):
a , a = down_block(__magic_name__ , __magic_name__ , __magic_name__ , deterministic=not train )
else:
a , a = down_block(__magic_name__ , __magic_name__ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
a = ()
for down_block_res_sample, down_block_additional_residual in zip(
__magic_name__ , __magic_name__ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
a = new_down_block_res_samples
# 4. mid
a = self.mid_block(__magic_name__ , __magic_name__ , __magic_name__ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
a = down_block_res_samples[-(self.layers_per_block + 1) :]
a = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__magic_name__ , __magic_name__ ):
a = up_block(
__magic_name__ , temb=__magic_name__ , encoder_hidden_states=__magic_name__ , res_hidden_states_tuple=__magic_name__ , deterministic=not train , )
else:
a = up_block(__magic_name__ , temb=__magic_name__ , res_hidden_states_tuple=__magic_name__ , deterministic=not train )
# 6. post-process
a = self.conv_norm_out(__magic_name__ )
a = nn.silu(__magic_name__ )
a = self.conv_out(__magic_name__ )
a = jnp.transpose(__magic_name__ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__magic_name__ )
| 363 |
def __A ( __lowerCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
a = a = a = numbers[0]
for i in range(1 , len(__lowerCamelCase ) ):
# update the maximum and minimum subarray products
a = numbers[i]
if number < 0:
a , a = min_till_now, max_till_now
a = max(__lowerCamelCase , max_till_now * number )
a = min(__lowerCamelCase , min_till_now * number )
# update the maximum product found till now
a = max(__lowerCamelCase , __lowerCamelCase )
return max_prod
| 347 | 0 |
def __A ( __lowerCamelCase ) -> "list[int]":
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
a = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
a = 1
if upper_limit > 0:
a = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(__lowerCamelCase ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
__UpperCamelCase : Union[str, Any] = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(F'The Catalan numbers from 0 through {N} are:')
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod()
| 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
"processing_wav2vec2": ["Wav2Vec2Processor"],
"tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = [
"WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
"Wav2Vec2Model",
"Wav2Vec2PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
"TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFWav2Vec2ForCTC",
"TFWav2Vec2Model",
"TFWav2Vec2PreTrainedModel",
"TFWav2Vec2ForSequenceClassification",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = [
"FlaxWav2Vec2ForCTC",
"FlaxWav2Vec2ForPreTraining",
"FlaxWav2Vec2Model",
"FlaxWav2Vec2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 347 | 0 |
import cmath
import math
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> complex:
a = math.radians(__lowerCamelCase )
a = math.radians(__lowerCamelCase )
# Convert voltage and current to rectangular form
a = cmath.rect(__lowerCamelCase , __lowerCamelCase )
a = cmath.rect(__lowerCamelCase , __lowerCamelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Any:
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , __lowerCamelCase )
a = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
a = dataset_size < in_memory_max_size
else:
a = False
a = is_small_dataset(__lowerCamelCase )
assert result == expected
| 347 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__UpperCamelCase : int = random.Random()
def __A ( __lowerCamelCase , __lowerCamelCase=1.0 , __lowerCamelCase=None , __lowerCamelCase=None ) -> Dict:
if rng is None:
a = global_rng
a = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :Optional[int] , __magic_name__ :Tuple , __magic_name__ :str=7 , __magic_name__ :str=400 , __magic_name__ :Any=2000 , __magic_name__ :Optional[Any]=10 , __magic_name__ :Tuple=160 , __magic_name__ :Tuple=8 , __magic_name__ :Union[str, Any]=0.0 , __magic_name__ :Optional[Any]=4000 , __magic_name__ :Dict=False , __magic_name__ :int=True , ):
'''simple docstring'''
a = parent
a = batch_size
a = min_seq_length
a = max_seq_length
a = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a = padding_value
a = sampling_rate
a = return_attention_mask
a = do_normalize
a = feature_size
a = chunk_length
a = hop_length
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :Tuple=False , __magic_name__ :Dict=False ):
'''simple docstring'''
def _flatten(__magic_name__ :List[Any] ):
return list(itertools.chain(*__magic_name__ ) )
if equal_length:
a = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
a = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
a = [np.asarray(__magic_name__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = WhisperFeatureExtractor if is_speech_available() else None
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = WhisperFeatureExtractionTester(self )
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
a = self.feature_extraction_class.from_pretrained(__magic_name__ )
a = feat_extract_first.to_dict()
a = feat_extract_second.to_dict()
a = feat_extract_first.mel_filters
a = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ ) )
self.assertEqual(__magic_name__ , __magic_name__ )
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a = os.path.join(__magic_name__ , """feat_extract.json""" )
feat_extract_first.to_json_file(__magic_name__ )
a = self.feature_extraction_class.from_json_file(__magic_name__ )
a = feat_extract_first.to_dict()
a = feat_extract_second.to_dict()
a = feat_extract_first.mel_filters
a = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ ) )
self.assertEqual(__magic_name__ , __magic_name__ )
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
a = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
a = [np.asarray(__magic_name__ ) for speech_input in speech_inputs]
# Test feature size
a = feature_extractor(__magic_name__ , padding="""max_length""" , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
a = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
a = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1E-3 ) )
# Test batched
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__magic_name__ , __magic_name__ ):
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
a = [floats_list((1, x) )[0] for x in (800, 800, 800)]
a = np.asarray(__magic_name__ )
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__magic_name__ , __magic_name__ ):
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1E-3 ) )
# Test truncation required
a = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
a = [np.asarray(__magic_name__ ) for speech_input in speech_inputs]
a = [x[: feature_extractor.n_samples] for x in speech_inputs]
a = [np.asarray(__magic_name__ ) for speech_input in speech_inputs_truncated]
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
a = feature_extractor(__magic_name__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__magic_name__ , __magic_name__ ):
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1E-3 ) )
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
import torch
a = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a = np.random.rand(100 , 32 ).astype(np.floataa )
a = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
a = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCamelCase__ ( self :List[str] , __magic_name__ :Any ):
'''simple docstring'''
a = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
a = ds.sort("""id""" ).select(range(__magic_name__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
a = self._load_datasamples(1 )
a = WhisperFeatureExtractor()
a = feature_extractor(__magic_name__ , return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __magic_name__ , atol=1E-4 ) )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a = self._load_datasamples(1 )[0]
a = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535 # Rescale to [0, 65535] to show issue
a = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__magic_name__ )[0]
self.assertTrue(np.all(np.mean(__magic_name__ ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(__magic_name__ ) - 1 ) < 1E-3 ) )
| 366 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> tuple[int, int]:
a = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
a = x_den * y_den * z_den
a = gcd(__lowerCamelCase , __lowerCamelCase )
top //= hcf
bottom //= hcf
return top, bottom
def __A ( __lowerCamelCase = 35 ) -> int:
a = set()
a = 42
a = Fraction(0 )
a = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
a = x_num * y_den + x_den * y_num
a = x_den * y_den
a = gcd(__lowerCamelCase , __lowerCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
a = add_three(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
unique_s.add(__lowerCamelCase )
# n=2
a = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
a = x_den * x_den * y_den * y_den
if is_sq(__lowerCamelCase ) and is_sq(__lowerCamelCase ):
a = int(sqrt(__lowerCamelCase ) )
a = int(sqrt(__lowerCamelCase ) )
a = gcd(__lowerCamelCase , __lowerCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
a = add_three(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
unique_s.add(__lowerCamelCase )
# n=-1
a = x_num * y_num
a = x_den * y_num + x_num * y_den
a = gcd(__lowerCamelCase , __lowerCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
a = add_three(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
unique_s.add(__lowerCamelCase )
# n=2
a = x_num * x_num * y_num * y_num
a = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__lowerCamelCase ) and is_sq(__lowerCamelCase ):
a = int(sqrt(__lowerCamelCase ) )
a = int(sqrt(__lowerCamelCase ) )
a = gcd(__lowerCamelCase , __lowerCamelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
a = add_three(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
unique_s.add(__lowerCamelCase )
for num, den in unique_s:
total += Fraction(__lowerCamelCase , __lowerCamelCase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'{solution() = }')
| 347 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Union[str, Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = XLMRobertaTokenizer
UpperCamelCase__ = XLMRobertaTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a = XLMRobertaTokenizer(__magic_name__ , keep_accents=__magic_name__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = """<pad>"""
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__ ) , __magic_name__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__ ) , __magic_name__ )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(__magic_name__ ) , 1002 )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = XLMRobertaTokenizer(__magic_name__ , keep_accents=__magic_name__ )
a = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__magic_name__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__magic_name__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
a = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__magic_name__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
a = tokenizer.convert_tokens_to_ids(__magic_name__ )
self.assertListEqual(
__magic_name__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
a = tokenizer.convert_ids_to_tokens(__magic_name__ )
self.assertListEqual(
__magic_name__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
a = (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
a = self.rust_tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ )
a = self.tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ )
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__magic_name__ )
a = tokenizer_p.save_pretrained(__magic_name__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
a = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(__magic_name__ , __magic_name__ )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__magic_name__ )
a = tokenizer_p.from_pretrained(__magic_name__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__magic_name__ , __magic_name__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__magic_name__ )
# Save tokenizer rust, legacy_format=True
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__magic_name__ , legacy_format=__magic_name__ )
a = tokenizer_p.save_pretrained(__magic_name__ )
# Checks it save with the same files
self.assertSequenceEqual(__magic_name__ , __magic_name__ )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__magic_name__ )
a = tokenizer_p.from_pretrained(__magic_name__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__magic_name__ , __magic_name__ ) )
shutil.rmtree(__magic_name__ )
# Save tokenizer rust, legacy_format=False
a = tempfile.mkdtemp()
a = tokenizer_r.save_pretrained(__magic_name__ , legacy_format=__magic_name__ )
a = tokenizer_p.save_pretrained(__magic_name__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
a = tokenizer_r.from_pretrained(__magic_name__ )
a = tokenizer_p.from_pretrained(__magic_name__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__magic_name__ , __magic_name__ ) )
shutil.rmtree(__magic_name__ )
@cached_property
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""" )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__magic_name__ , f.name )
a = XLMRobertaTokenizer(f.name , keep_accents=__magic_name__ )
a = pickle.dumps(__magic_name__ )
pickle.loads(__magic_name__ )
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
a = self.get_tokenizer()
a = self.get_rust_tokenizer()
a = """I was born in 92000, and this is falsé."""
a = tokenizer.tokenize(__magic_name__ )
a = rust_tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = rust_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
a = self.get_rust_tokenizer()
a = tokenizer.encode(__magic_name__ )
a = rust_tokenizer.encode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
@slow
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = """Hello World!"""
a = [0, 3_5378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__magic_name__ , self.big_tokenizer.encode(__magic_name__ ) )
@slow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
a = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
17_9459,
12_4850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
1_0114,
711,
152,
20,
6,
5,
2_2376,
642,
1221,
1_5190,
3_4153,
450,
5608,
959,
1119,
5_7702,
136,
186,
47,
1098,
2_9367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
5_0901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(__magic_name__ , self.big_tokenizer.encode(__magic_name__ ) )
@slow
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = {"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__magic_name__ , model_name="""xlm-roberta-base""" , revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" , )
| 367 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :List[str] , __magic_name__ :List[str] , __magic_name__ :List[Any]=13 , __magic_name__ :Any=7 , __magic_name__ :Optional[int]=True , __magic_name__ :List[Any]=True , __magic_name__ :Optional[int]=True , __magic_name__ :Union[str, Any]=True , __magic_name__ :Any=99 , __magic_name__ :List[str]=32 , __magic_name__ :List[str]=5 , __magic_name__ :str=4 , __magic_name__ :str=37 , __magic_name__ :Optional[int]="gelu" , __magic_name__ :int=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :List[str]=512 , __magic_name__ :Tuple=16 , __magic_name__ :Tuple=2 , __magic_name__ :List[str]=0.02 , __magic_name__ :Any=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_choices
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = FlaxRoFormerModelTester(self )
@slow
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=__magic_name__ )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
a = jnp.array([[0, 1, 2, 3, 4, 5]] )
a = model(__magic_name__ )[0]
a = 5_0000
a = (1, 6, vocab_size)
self.assertEqual(output.shape , __magic_name__ )
a = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __magic_name__ , atol=1E-4 ) )
| 347 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__UpperCamelCase : str = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
UpperCamelCase__ = 10000
UpperCamelCase__ = None
UpperCamelCase__ = None
class __lowerCAmelCase ( datasets.ArrowBasedBuilder ):
UpperCamelCase__ = ParquetConfig
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self :List[Any] , __magic_name__ :List[str] ):
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__magic_name__ , (str, list, tuple) ):
a = data_files
if isinstance(__magic_name__ , __magic_name__ ):
a = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a = [dl_manager.iter_files(__magic_name__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
a = []
for split_name, files in data_files.items():
if isinstance(__magic_name__ , __magic_name__ ):
a = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a = [dl_manager.iter_files(__magic_name__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(__magic_name__ ):
with open(__magic_name__ , """rb""" ) as f:
a = datasets.Features.from_arrow_schema(pq.read_schema(__magic_name__ ) )
break
splits.append(datasets.SplitGenerator(name=__magic_name__ , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase__ ( self :Dict , __magic_name__ :pa.Table ):
'''simple docstring'''
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
a = table_cast(__magic_name__ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase__ ( self :str , __magic_name__ :List[str] ):
'''simple docstring'''
a = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__ ) ):
with open(__magic_name__ , """rb""" ) as f:
a = pq.ParquetFile(__magic_name__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
a = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F'{file_idx}_{batch_idx}', self._cast_table(__magic_name__ )
except ValueError as e:
logger.error(F'Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}' )
raise
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
__UpperCamelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 347 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
def __init__( self :List[Any] , __magic_name__ :Tuple , __magic_name__ :List[str]=14 , __magic_name__ :List[Any]=7 , __magic_name__ :Optional[Any]=True , __magic_name__ :str=True , __magic_name__ :List[str]=False , __magic_name__ :Any=True , __magic_name__ :Optional[int]=99 , __magic_name__ :Dict=32 , __magic_name__ :int=4 , __magic_name__ :int=4 , __magic_name__ :str=4 , __magic_name__ :Any=37 , __magic_name__ :List[Any]="gelu" , __magic_name__ :Any=0.1 , __magic_name__ :Optional[int]=0.1 , __magic_name__ :List[str]=512 , __magic_name__ :Any=0.02 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = rotary_dim
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = initializer_range
a = None
a = vocab_size - 1
a = vocab_size - 1
a = vocab_size - 1
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=__magic_name__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ ( self :int , __magic_name__ :int , __magic_name__ :str , __magic_name__ :str , __magic_name__ :Dict ):
'''simple docstring'''
a = 20
a = model_class_name(__magic_name__ )
a = model.init_cache(input_ids.shape[0] , __magic_name__ )
a = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
a = model(
input_ids[:, :-1] , attention_mask=__magic_name__ , past_key_values=__magic_name__ , position_ids=__magic_name__ , )
a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
a = model(
input_ids[:, -1:] , attention_mask=__magic_name__ , past_key_values=outputs_cache.past_key_values , position_ids=__magic_name__ , )
a = model(__magic_name__ )
a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
def lowerCamelCase__ ( self :Any , __magic_name__ :Tuple , __magic_name__ :List[str] , __magic_name__ :int , __magic_name__ :Any ):
'''simple docstring'''
a = 20
a = model_class_name(__magic_name__ )
a = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
a = model.init_cache(input_ids.shape[0] , __magic_name__ )
a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
a = model(
input_ids[:, :-1] , attention_mask=__magic_name__ , past_key_values=__magic_name__ , position_ids=__magic_name__ , )
a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
a = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=__magic_name__ , position_ids=__magic_name__ , )
a = model(__magic_name__ , attention_mask=__magic_name__ )
a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
@require_flax
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
UpperCamelCase__ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = FlaxGPTJModelTester(self )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a , a , a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a , a , a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
@tooslow
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
a = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=__magic_name__ , truncation=__magic_name__ )
a = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
a = False
a = model.config.eos_token_id
a = jax.jit(model.generate )
a = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
a = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )
a = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(__magic_name__ , __magic_name__ )
@is_pt_flax_cross_test
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
a = self._prepare_for_class(__magic_name__ , __magic_name__ )
a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
a = model_class.__name__[4:] # Skip the "Flax" at the beginning
a = getattr(__magic_name__ , __magic_name__ )
a , a = pt_inputs["""input_ids"""].shape
a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__magic_name__ ):
a = 0
a = 1
a = 0
a = 1
a = pt_model_class(__magic_name__ ).eval()
a = model_class(__magic_name__ , dtype=jnp.floataa )
a = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __magic_name__ )
a = fx_state
with torch.no_grad():
a = pt_model(**__magic_name__ ).to_tuple()
a = fx_model(**__magic_name__ ).to_tuple()
self.assertEqual(len(__magic_name__ ) , len(__magic_name__ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__magic_name__ , __magic_name__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__magic_name__ )
a = model_class.from_pretrained(__magic_name__ , from_pt=__magic_name__ )
a = fx_model_loaded(**__magic_name__ ).to_tuple()
self.assertEqual(
len(__magic_name__ ) , len(__magic_name__ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(__magic_name__ , __magic_name__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
a = self._prepare_for_class(__magic_name__ , __magic_name__ )
a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
a = model_class.__name__[4:] # Skip the "Flax" at the beginning
a = getattr(__magic_name__ , __magic_name__ )
a = pt_model_class(__magic_name__ ).eval()
a = model_class(__magic_name__ , dtype=jnp.floataa )
a = load_flax_weights_in_pytorch_model(__magic_name__ , fx_model.params )
a , a = pt_inputs["""input_ids"""].shape
a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__magic_name__ ):
a = 0
a = 1
a = 0
a = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
a = pt_model(**__magic_name__ ).to_tuple()
a = fx_model(**__magic_name__ ).to_tuple()
self.assertEqual(len(__magic_name__ ) , len(__magic_name__ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__magic_name__ , __magic_name__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__magic_name__ )
a = pt_model_class.from_pretrained(__magic_name__ , from_flax=__magic_name__ )
with torch.no_grad():
a = pt_model_loaded(**__magic_name__ ).to_tuple()
self.assertEqual(
len(__magic_name__ ) , len(__magic_name__ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__magic_name__ , __magic_name__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
| 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
def lowerCamelCase__ ( self :Any , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
a = {"""num_train_timesteps""": 1000}
config.update(**__magic_name__ )
return config
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :Tuple=0 , **__magic_name__ :Optional[int] ):
'''simple docstring'''
a = dict(self.forward_default_kwargs )
a = kwargs.pop("""num_inference_steps""" , __magic_name__ )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__magic_name__ )
a = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(__magic_name__ )
# copy over dummy past residuals
a = dummy_past_residuals[:]
if time_step is None:
a = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__magic_name__ )
a = scheduler_class.from_pretrained(__magic_name__ )
new_scheduler.set_timesteps(__magic_name__ )
# copy over dummy past residuals
a = dummy_past_residuals[:]
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = new_scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = new_scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[Any] , __magic_name__ :List[Any]=0 , **__magic_name__ :Any ):
'''simple docstring'''
a = dict(self.forward_default_kwargs )
a = kwargs.pop("""num_inference_steps""" , __magic_name__ )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(__magic_name__ )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[:]
if time_step is None:
a = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__magic_name__ )
a = scheduler_class.from_pretrained(__magic_name__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(__magic_name__ )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[:]
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = new_scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = new_scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self :Optional[Any] , **__magic_name__ :Optional[int] ):
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__magic_name__ )
a = scheduler_class(**__magic_name__ )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__magic_name__ )
for i, t in enumerate(scheduler.timesteps ):
a = model(__magic_name__ , __magic_name__ )
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
a = model(__magic_name__ , __magic_name__ )
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ ).prev_sample
return sample
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = dict(self.forward_default_kwargs )
a = kwargs.pop("""num_inference_steps""" , __magic_name__ )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__magic_name__ )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__magic_name__ , """set_timesteps""" ):
scheduler.set_timesteps(__magic_name__ )
elif num_inference_steps is not None and not hasattr(__magic_name__ , """set_timesteps""" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
a = dummy_past_residuals[:]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
a = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=__magic_name__ , time_step=__magic_name__ )
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=__magic_name__ , time_step=__magic_name__ )
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = self.full_loop()
a = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_mean.item() - 254_0529 ) < 10
| 347 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__UpperCamelCase : int = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
def __init__( self :Optional[Any] , *__magic_name__ :Dict , **__magic_name__ :Tuple ):
'''simple docstring'''
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , __magic_name__ , )
super().__init__(*__magic_name__ , **__magic_name__ )
| 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
a = """encrypt"""
a = encrypt_message(__lowerCamelCase , __lowerCamelCase )
elif mode.lower().startswith("""d""" ):
a = """decrypt"""
a = decrypt_message(__lowerCamelCase , __lowerCamelCase )
print(f'\n{mode.title()}ed message:' )
print(__lowerCamelCase )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """encrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
return translate_message(__lowerCamelCase , __lowerCamelCase , """decrypt""" )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
a = []
a = 0
a = key.upper()
for symbol in message:
a = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(__lowerCamelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(__lowerCamelCase ):
a = 0
else:
translated.append(__lowerCamelCase )
return "".join(__lowerCamelCase )
if __name__ == "__main__":
main()
| 347 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :Dict , __magic_name__ :int , __magic_name__ :Optional[int]=7 , __magic_name__ :Optional[Any]=3 , __magic_name__ :Union[str, Any]=30 , __magic_name__ :List[Any]=400 , __magic_name__ :Optional[int]=True , __magic_name__ :Dict=None , __magic_name__ :List[Any]=True , __magic_name__ :int=[0.5, 0.5, 0.5] , __magic_name__ :Optional[Any]=[0.5, 0.5, 0.5] , __magic_name__ :Optional[Any]=True , __magic_name__ :str=1 / 255 , __magic_name__ :Union[str, Any]=True , ):
'''simple docstring'''
a = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
a = parent
a = batch_size
a = num_channels
a = min_resolution
a = max_resolution
a = do_resize
a = size
a = do_normalize
a = image_mean
a = image_std
a = do_rescale
a = rescale_factor
a = do_pad
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCamelCase__ ( self :str , __magic_name__ :str , __magic_name__ :Tuple=False ):
'''simple docstring'''
if not batched:
a = image_inputs[0]
if isinstance(__magic_name__ , Image.Image ):
a , a = image.size
else:
a , a = image.shape[1], image.shape[2]
if w < h:
a = int(self.size["""shortest_edge"""] * h / w )
a = self.size["""shortest_edge"""]
elif w > h:
a = self.size["""shortest_edge"""]
a = int(self.size["""shortest_edge"""] * w / h )
else:
a = self.size["""shortest_edge"""]
a = self.size["""shortest_edge"""]
else:
a = []
for image in image_inputs:
a , a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
a = max(__magic_name__ , key=lambda __magic_name__ : item[0] )[0]
a = max(__magic_name__ , key=lambda __magic_name__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = YolosImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a = YolosImageProcessingTester(self )
@property
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , """image_mean""" ) )
self.assertTrue(hasattr(__magic_name__ , """image_std""" ) )
self.assertTrue(hasattr(__magic_name__ , """do_normalize""" ) )
self.assertTrue(hasattr(__magic_name__ , """do_resize""" ) )
self.assertTrue(hasattr(__magic_name__ , """size""" ) )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , __magic_name__ )
a = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__magic_name__ )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , __magic_name__ )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__magic_name__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a , a = self.image_processor_tester.get_expected_values(__magic_name__ , batched=__magic_name__ )
a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__magic_name__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__magic_name__ , batched=__magic_name__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__magic_name__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__magic_name__ , batched=__magic_name__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
a = self.image_processing_class(do_resize=__magic_name__ , do_normalize=__magic_name__ , do_rescale=__magic_name__ )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
a = image_processing_a.pad(__magic_name__ , return_tensors="""pt""" )
a = image_processing_a(__magic_name__ , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1E-4 ) )
@slow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
a = json.loads(f.read() )
a = {"""image_id""": 3_9769, """annotations""": target}
# encode them
a = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
a = image_processing(images=__magic_name__ , annotations=__magic_name__ , return_tensors="""pt""" )
# verify pixel values
a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , __magic_name__ )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __magic_name__ , atol=1E-4 ) )
# verify area
a = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __magic_name__ ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __magic_name__ )
a = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __magic_name__ , atol=1E-3 ) )
# verify image_id
a = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __magic_name__ ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __magic_name__ ) )
# verify class_labels
a = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __magic_name__ ) )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __magic_name__ ) )
# verify size
a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __magic_name__ ) )
@slow
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
a = json.loads(f.read() )
a = {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}
a = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
a = YolosImageProcessor(format="""coco_panoptic""" )
a = image_processing(images=__magic_name__ , annotations=__magic_name__ , masks_path=__magic_name__ , return_tensors="""pt""" )
# verify pixel values
a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , __magic_name__ )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __magic_name__ , atol=1E-4 ) )
# verify area
a = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __magic_name__ ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __magic_name__ )
a = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __magic_name__ , atol=1E-3 ) )
# verify image_id
a = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __magic_name__ ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __magic_name__ ) )
# verify class_labels
a = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __magic_name__ ) )
# verify masks
a = 82_2873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , __magic_name__ )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __magic_name__ ) )
# verify size
a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __magic_name__ ) )
| 371 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :Optional[int] , __magic_name__ :Tuple , __magic_name__ :Tuple=13 , __magic_name__ :List[Any]=7 , __magic_name__ :Optional[Any]=True , __magic_name__ :List[Any]=True , __magic_name__ :Union[str, Any]=True , __magic_name__ :List[str]=True , __magic_name__ :str=99 , __magic_name__ :Optional[Any]=32 , __magic_name__ :Union[str, Any]=5 , __magic_name__ :Any=4 , __magic_name__ :int=37 , __magic_name__ :Tuple="gelu" , __magic_name__ :List[str]=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :Tuple=512 , __magic_name__ :Dict=16 , __magic_name__ :Optional[int]=2 , __magic_name__ :Optional[int]=0.02 , __magic_name__ :Optional[Any]=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_choices
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = True
a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = FlaxRobertaModelTester(self )
@slow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a = model_class_name.from_pretrained("""roberta-base""" , from_pt=__magic_name__ )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
| 347 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ) -> float:
"""simple docstring"""
__lowerCamelCase = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__ )] )
__lowerCamelCase = np.array(UpperCamelCase__ )
__lowerCamelCase = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , UpperCamelCase__ ) ) , x.transpose() ) , UpperCamelCase__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ) -> float:
"""simple docstring"""
__lowerCamelCase = (1, 2, 1)
__lowerCamelCase = (1, 1, 0, 7)
__lowerCamelCase = SARIMAX(
UpperCamelCase__ , exog=UpperCamelCase__ , order=UpperCamelCase__ , seasonal_order=UpperCamelCase__ )
__lowerCamelCase = model.fit(disp=UpperCamelCase__ , maxiter=600 , method='nm' )
__lowerCamelCase = model_fit.predict(1 , len(UpperCamelCase__ ) , exog=[test_match] )
return result[0]
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ) -> float:
"""simple docstring"""
__lowerCamelCase = SVR(kernel='rbf' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = regressor.predict(UpperCamelCase__ )
return y_pred[0]
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> float:
"""simple docstring"""
train_user.sort()
__lowerCamelCase = np.percentile(UpperCamelCase__ , 25 )
__lowerCamelCase = np.percentile(UpperCamelCase__ , 75 )
__lowerCamelCase = qa - qa
__lowerCamelCase = qa - (iqr * 0.1)
return low_lim
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : float ) -> bool:
"""simple docstring"""
__lowerCamelCase = 0
__lowerCamelCase = 0
for i in list_vote:
if i > actual_result:
__lowerCamelCase = not_safe + 1
else:
if abs(abs(UpperCamelCase__ ) - abs(UpperCamelCase__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__A = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
__A = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
__A = Normalizer().fit_transform(data_input_df.values)
# split data
__A = normalize_df[:, 2].tolist()
__A = normalize_df[:, 0].tolist()
__A = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__A = normalize_df[:, [1, 2]].tolist()
__A = x[: len(x) - 1]
__A = x[len(x) - 1 :]
# for linear regression & sarimax
__A = total_date[: len(total_date) - 1]
__A = total_user[: len(total_user) - 1]
__A = total_match[: len(total_match) - 1]
__A = total_date[len(total_date) - 1 :]
__A = total_user[len(total_user) - 1 :]
__A = total_match[len(total_match) - 1 :]
# voting system with forecasting
__A = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__A = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__A = logging.get_logger(__name__)
__A = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''whisper'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=51_865 , lowerCamelCase__=80 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=1_536 , lowerCamelCase__=1_536 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=50_257 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="gelu" , lowerCamelCase__=256 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=False , lowerCamelCase__=1_500 , lowerCamelCase__=448 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=None , lowerCamelCase__=[220, 50_256] , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=False , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__=7 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = num_mel_bins
__lowerCamelCase = d_model
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = max_source_positions
__lowerCamelCase = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
__lowerCamelCase = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
__lowerCamelCase = median_filter_width
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , suppress_tokens=lowerCamelCase__ , begin_suppress_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__lowerCamelCase = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
__lowerCamelCase = {0: 'batch'}
else:
__lowerCamelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ , direction='inputs' )
return common_inputs
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = -1 , lowerCamelCase__ = -1 , lowerCamelCase__ = False , lowerCamelCase__ = None , lowerCamelCase__ = 22_050 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
__lowerCamelCase = OrderedDict()
__lowerCamelCase = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowerCamelCase__ , framework=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , time_duration=lowerCamelCase__ , frequency=lowerCamelCase__ , )
__lowerCamelCase = encoder_inputs['input_features'].shape[2]
__lowerCamelCase = encoder_sequence_length // 2 if self.use_past else seq_length
__lowerCamelCase = super().generate_dummy_inputs(
preprocessor.tokenizer , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = encoder_inputs.pop('input_features' )
__lowerCamelCase = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
__lowerCamelCase = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-3
| 348 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = XGLMTokenizer
snake_case_ = XGLMTokenizerFast
snake_case_ = True
snake_case_ = True
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase = XGLMTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = '<pad>'
__lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(len(lowerCamelCase__ ) , 1_008 )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_008 )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = XGLMTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
__lowerCamelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowerCamelCase__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
return XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
def lowercase_ ( self ) -> int:
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
__lowerCamelCase = XGLMTokenizer(f.name , keep_accents=lowerCamelCase__ )
__lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = 'I was born in 92000, and this is falsé.'
__lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
__lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = 'Hello World!'
__lowerCamelCase = [2, 31_227, 4_447, 35]
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
)
# fmt: off
__lowerCamelCase = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# fmt: off
__lowerCamelCase = {
'input_ids': [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='facebook/xglm-564M' , padding=lowerCamelCase__ , )
| 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=14 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=0.02 , ) -> int:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = rotary_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = None
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=lowerCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , attention_mask=lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
snake_case_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = FlaxGPTJModelTester(self )
def lowercase_ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@tooslow
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
__lowerCamelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )
__lowerCamelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = False
__lowerCamelCase = model.config.eos_token_id
__lowerCamelCase = jax.jit(model.generate )
__lowerCamelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
__lowerCamelCase = tokenizer.batch_decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
__lowerCamelCase = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCamelCase__ )
__lowerCamelCase = fx_state
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = model_class.from_pretrained(lowerCamelCase__ , from_pt=lowerCamelCase__ )
__lowerCamelCase = fx_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = load_flax_weights_in_pytorch_model(lowerCamelCase__ , fx_model.params )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = pt_model_class.from_pretrained(lowerCamelCase__ , from_flax=lowerCamelCase__ )
with torch.no_grad():
__lowerCamelCase = pt_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''nllb-moe'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=128_112 , lowerCamelCase__=1_024 , lowerCamelCase__=12 , lowerCamelCase__=4_096 , lowerCamelCase__=16 , lowerCamelCase__=12 , lowerCamelCase__=4_096 , lowerCamelCase__=16 , lowerCamelCase__=0.05 , lowerCamelCase__=0.05 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=1_024 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__="float32" , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=64 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=0.0_01 , lowerCamelCase__=0.0_01 , lowerCamelCase__="all" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=1.0 , lowerCamelCase__=0.2 , lowerCamelCase__=1 , lowerCamelCase__=0 , lowerCamelCase__=2 , lowerCamelCase__=False , **lowerCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = d_model
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = router_z_loss_coef
__lowerCamelCase = router_aux_loss_coef
__lowerCamelCase = decoder_sparse_step
__lowerCamelCase = encoder_sparse_step
__lowerCamelCase = num_experts
__lowerCamelCase = expert_capacity
__lowerCamelCase = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
__lowerCamelCase = router_dtype
__lowerCamelCase = router_ignore_padding_tokens
__lowerCamelCase = batch_prioritized_routing
__lowerCamelCase = second_expert_policy
__lowerCamelCase = normalize_router_prob_before_dropping
__lowerCamelCase = moe_eval_capacity_token_fraction
__lowerCamelCase = moe_token_dropout
__lowerCamelCase = output_router_logits
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , **lowerCamelCase__ , )
| 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 12
__lowerCamelCase = 12
__lowerCamelCase = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__lowerCamelCase = TransformeraDModel(**lowerCamelCase__ )
return model
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase__ )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCamelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__lowerCamelCase = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__lowerCamelCase = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 | 1 |
import math
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError('This should never happen' )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
__A = "Enter the base and the power separated by a comma: "
__A , __A = map(int, input(prompt).split(","))
__A , __A = map(int, input(prompt).split(","))
# We find the log of each number, using the function res(), which takes two
# arguments.
__A = res(xa, ya)
__A = res(xa, ya)
# We check for the largest number
if resa > resa:
print("Largest number is", xa, "^", ya)
elif resa > resa:
print("Largest number is", xa, "^", ya)
else:
print("Both are equal")
| 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = (1 - _cos) / 2
__lowerCamelCase = 1 - _cos
__lowerCamelCase = 1 + alpha
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 - alpha
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = (1 + _cos) / 2
__lowerCamelCase = -1 - _cos
__lowerCamelCase = 1 + alpha
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 - alpha
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = _sin / 2
__lowerCamelCase = 0
__lowerCamelCase = -ba
__lowerCamelCase = 1 + alpha
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 - alpha
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = 1 - alpha
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 + alpha
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = 10 ** (gain_db / 40)
__lowerCamelCase = 1 + alpha * big_a
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 - alpha * big_a
__lowerCamelCase = 1 + alpha / big_a
__lowerCamelCase = -2 * _cos
__lowerCamelCase = 1 - alpha / big_a
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = 10 ** (gain_db / 40)
__lowerCamelCase = (big_a + 1) - (big_a - 1) * _cos
__lowerCamelCase = (big_a + 1) + (big_a - 1) * _cos
__lowerCamelCase = (big_a - 1) - (big_a + 1) * _cos
__lowerCamelCase = (big_a - 1) + (big_a + 1) * _cos
__lowerCamelCase = 2 * sqrt(UpperCamelCase__ ) * alpha
__lowerCamelCase = big_a * (pmc + aaa)
__lowerCamelCase = 2 * big_a * mpc
__lowerCamelCase = big_a * (pmc - aaa)
__lowerCamelCase = ppmc + aaa
__lowerCamelCase = -2 * pmpc
__lowerCamelCase = ppmc - aaa
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__lowerCamelCase = tau * frequency / samplerate
__lowerCamelCase = sin(UpperCamelCase__ )
__lowerCamelCase = cos(UpperCamelCase__ )
__lowerCamelCase = _sin / (2 * q_factor)
__lowerCamelCase = 10 ** (gain_db / 40)
__lowerCamelCase = (big_a + 1) - (big_a - 1) * _cos
__lowerCamelCase = (big_a + 1) + (big_a - 1) * _cos
__lowerCamelCase = (big_a - 1) - (big_a + 1) * _cos
__lowerCamelCase = (big_a - 1) + (big_a + 1) * _cos
__lowerCamelCase = 2 * sqrt(UpperCamelCase__ ) * alpha
__lowerCamelCase = big_a * (ppmc + aaa)
__lowerCamelCase = -2 * big_a * pmpc
__lowerCamelCase = big_a * (ppmc - aaa)
__lowerCamelCase = pmc + aaa
__lowerCamelCase = 2 * mpc
__lowerCamelCase = pmc - aaa
__lowerCamelCase = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mask2former'''
snake_case_ = ['''swin''']
snake_case_ = {'''hidden_size''': '''hidden_dim'''}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 1_024 , lowerCamelCase__ = "relu" , lowerCamelCase__ = 6 , lowerCamelCase__ = 10 , lowerCamelCase__ = 8 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 2_048 , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 4 , lowerCamelCase__ = 255 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 12_544 , lowerCamelCase__ = 3.0 , lowerCamelCase__ = 0.75 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = True , lowerCamelCase__ = [4, 8, 16, 32] , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Tuple:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowerCamelCase = CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = backbone_config.pop('model_type' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(lowerCamelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**lowerCamelCase__ )
@classmethod
def lowercase_ ( cls , lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return cls(
backbone_config=lowerCamelCase__ , **lowerCamelCase__ , )
def lowercase_ ( self ) -> Dict[str, any]:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 348 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A = 20_48
__A = 40_96
__A = 42
__A = os.environ.pop("PROCESS_TRAIN", "false")
__A = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> str:
"""simple docstring"""
def choose_first(UpperCamelCase__ : int , UpperCamelCase__ : List[Any]=False ):
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
if len(UpperCamelCase__ ) == 1:
__lowerCamelCase = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__lowerCamelCase = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
__lowerCamelCase = {'id': example['id']}
__lowerCamelCase = example['annotations']
__lowerCamelCase = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
__lowerCamelCase = ['yes'] if 1 in yes_no_answer else ['no']
__lowerCamelCase = __lowerCamelCase = []
__lowerCamelCase = __lowerCamelCase = []
__lowerCamelCase = ['<cls>']
else:
__lowerCamelCase = ['short']
__lowerCamelCase = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
__lowerCamelCase = ['long']
__lowerCamelCase = choose_first(annotation['long_answer'] , is_long_answer=UpperCamelCase__ )
__lowerCamelCase = []
answer.update(UpperCamelCase__ )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
__lowerCamelCase = True
else:
__lowerCamelCase = False
__lowerCamelCase = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] , UpperCamelCase__ ) for k in cols ):
raise ValueError('Issue in ID' , example['id'] )
return answer
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict=False ) -> int:
"""simple docstring"""
__lowerCamelCase = _get_single_answer(UpperCamelCase__ )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__lowerCamelCase = example['document']['tokens']
__lowerCamelCase = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(UpperCamelCase__ ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__lowerCamelCase = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__lowerCamelCase = example['document']['tokens']
__lowerCamelCase = answer['start_token']
__lowerCamelCase = answer['end_token']
__lowerCamelCase = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__lowerCamelCase = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
__lowerCamelCase = doc['is_html'][answer['start_token'] : answer['end_token']]
__lowerCamelCase = doc['token'][answer['start_token'] : answer['end_token']]
__lowerCamelCase = ' '.join([old[i] for i in range(len(UpperCamelCase__ ) ) if not is_html[i]] )
if new != old:
print('ID:' , example['id'] )
print('New:' , UpperCamelCase__ , end='\n' )
print('Old:' , UpperCamelCase__ , end='\n\n' )
return {
"context": " ".join(UpperCamelCase__ ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any=2048 , UpperCamelCase__ : List[Any]=4096 , UpperCamelCase__ : List[str]=True ) -> Dict:
"""simple docstring"""
__lowerCamelCase = get_context_and_ans(UpperCamelCase__ , assertion=UpperCamelCase__ )
__lowerCamelCase = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__lowerCamelCase = tokenizer(example['question']['text'] , out['context'] ).input_ids
__lowerCamelCase = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = input_ids[:q_len]
__lowerCamelCase = range(UpperCamelCase__ , len(UpperCamelCase__ ) , max_length - doc_stride )
for i in doc_start_indices:
__lowerCamelCase = i + max_length - q_len
__lowerCamelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(UpperCamelCase__ ),
"end_token": [-100] * len(UpperCamelCase__ ),
"category": category,
},
}
__lowerCamelCase = out['context'].split()
__lowerCamelCase = splitted_context[answer['end_token']]
__lowerCamelCase = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) , add_special_tokens=UpperCamelCase__ , ).input_ids )
__lowerCamelCase = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) , add_special_tokens=UpperCamelCase__ ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__lowerCamelCase = len(tokenizer(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__lowerCamelCase = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
__lowerCamelCase = answer['start_token']
__lowerCamelCase = answer['end_token']
if assertion:
__lowerCamelCase = tokenizer.decode(UpperCamelCase__ )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' , answer['span'] )
print('NEW:' , UpperCamelCase__ , end='\n\n' )
if len(UpperCamelCase__ ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__lowerCamelCase = input_ids[:q_len]
__lowerCamelCase = range(UpperCamelCase__ , len(UpperCamelCase__ ) , max_length - doc_stride )
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = [] # null, yes, no, long, short
for i in doc_start_indices:
__lowerCamelCase = i + max_length - q_len
__lowerCamelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__lowerCamelCase = start_token - i + q_len
__lowerCamelCase = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
__lowerCamelCase = -100
__lowerCamelCase = -100
answers_category.append('null' )
__lowerCamelCase = inputs[-1][start_token : end_token + 1]
answers_start_token.append(UpperCamelCase__ )
answers_end_token.append(UpperCamelCase__ )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' , example['id'] )
print('New:' , tokenizer.decode(UpperCamelCase__ ) )
print('Old:' , tokenizer.decode(UpperCamelCase__ ) , end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : str=2048 , UpperCamelCase__ : int=4096 , UpperCamelCase__ : str=False ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = get_strided_contexts_and_ans(
UpperCamelCase__ , UpperCamelCase__ , doc_stride=UpperCamelCase__ , max_length=UpperCamelCase__ , assertion=UpperCamelCase__ , )
return example
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
with jsonlines.open(UpperCamelCase__ , 'a' ) as writer:
for example in tqdm(UpperCamelCase__ , total=len(UpperCamelCase__ ) , desc='Saving samples ... ' ):
__lowerCamelCase = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] , labels['start_token'] , labels['end_token'] , labels['category'] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__A = load_dataset("natural_questions")
__A = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
__A = data["train" if PROCESS_TRAIN == "true" else "validation"]
__A = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
__A = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__A = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
__A = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''gptsan-japanese'''
snake_case_ = [
'''past_key_values''',
]
snake_case_ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , lowerCamelCase__=36_000 , lowerCamelCase__=1_280 , lowerCamelCase__=1_024 , lowerCamelCase__=8_192 , lowerCamelCase__=4_096 , lowerCamelCase__=128 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__=16 , lowerCamelCase__=16 , lowerCamelCase__=128 , lowerCamelCase__=0.0 , lowerCamelCase__=1e-5 , lowerCamelCase__=False , lowerCamelCase__=0.0 , lowerCamelCase__="float32" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=0.0_02 , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=35_998 , lowerCamelCase__=35_995 , lowerCamelCase__=35_999 , **lowerCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = d_model
__lowerCamelCase = d_ff
__lowerCamelCase = d_ext
__lowerCamelCase = d_spout
__lowerCamelCase = num_switch_layers
__lowerCamelCase = num_ext_layers
__lowerCamelCase = num_switch_layers + num_ext_layers
__lowerCamelCase = num_heads
__lowerCamelCase = num_experts
__lowerCamelCase = expert_capacity
__lowerCamelCase = dropout_rate
__lowerCamelCase = layer_norm_epsilon
__lowerCamelCase = router_bias
__lowerCamelCase = router_jitter_noise
__lowerCamelCase = router_dtype
__lowerCamelCase = router_ignore_padding_tokens
__lowerCamelCase = output_hidden_states
__lowerCamelCase = output_attentions
__lowerCamelCase = initializer_factor
__lowerCamelCase = output_router_logits
__lowerCamelCase = use_cache
super().__init__(
separator_token_id=lowerCamelCase__ , pad_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , **lowerCamelCase__ , )
| 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.node_position[vertex]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = pos
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowerCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowerCamelCase = 2 * start + 1
else:
__lowerCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowerCamelCase , __lowerCamelCase = heap[smallest_child], positions[smallest_child]
__lowerCamelCase , __lowerCamelCase = (
heap[start],
positions[start],
)
__lowerCamelCase , __lowerCamelCase = temp, tempa
__lowerCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase__ )
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = position[index]
while index != 0:
__lowerCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowerCamelCase = heap[parent]
__lowerCamelCase = position[parent]
self.set_position(position[parent] , lowerCamelCase__ )
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , lowerCamelCase__ )
break
__lowerCamelCase = parent
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , 0 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = len(lowerCamelCase__ ) // 2 - 1
for i in range(lowerCamelCase__ , -1 , -1 ):
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = positions[0]
__lowerCamelCase = sys.maxsize
self.top_to_bottom(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
return temp
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Heap()
__lowerCamelCase = [0] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowerCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
__lowerCamelCase = []
for vertex in range(len(UpperCamelCase__ ) ):
distance_tv.append(sys.maxsize )
positions.append(UpperCamelCase__ )
heap.node_position.append(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = 1
__lowerCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowerCamelCase = 0
__lowerCamelCase = distance
heap.heapify(UpperCamelCase__ , UpperCamelCase__ )
for _ in range(1 , len(UpperCamelCase__ ) ):
__lowerCamelCase = heap.delete_minimum(UpperCamelCase__ , UpperCamelCase__ )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowerCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(UpperCamelCase__ )]
):
__lowerCamelCase = distance
heap.bottom_to_top(
UpperCamelCase__ , heap.get_position(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__A = int(input("Enter number of edges: ").strip())
__A = defaultdict(list)
for _ in range(edges_number):
__A = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 348 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
snake_case_ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
snake_case_ = "question"
snake_case_ = "context"
snake_case_ = "answers"
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 348 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
snake_case_ = None
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any=0.9_99 , UpperCamelCase__ : Dict="cosine" , ) -> Any:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCamelCase__ : str ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCamelCase__ : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
__lowerCamelCase = []
for i in range(UpperCamelCase__ ):
__lowerCamelCase = i / num_diffusion_timesteps
__lowerCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCamelCase__ ) / alpha_bar_fn(UpperCamelCase__ ) , UpperCamelCase__ ) )
return torch.tensor(UpperCamelCase__ , dtype=torch.floataa )
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 1_000 , lowerCamelCase__ = "fixed_small_log" , lowerCamelCase__ = True , lowerCamelCase__ = 1.0 , lowerCamelCase__ = "epsilon" , lowerCamelCase__ = "squaredcos_cap_v2" , ) -> Union[str, Any]:
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )
__lowerCamelCase = betas_for_alpha_bar(lowerCamelCase__ )
__lowerCamelCase = 1.0 - self.betas
__lowerCamelCase = torch.cumprod(self.alphas , dim=0 )
__lowerCamelCase = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__lowerCamelCase = 1.0
# setable values
__lowerCamelCase = None
__lowerCamelCase = torch.from_numpy(np.arange(0 , lowerCamelCase__ )[::-1].copy() )
__lowerCamelCase = variance_type
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = num_inference_steps
__lowerCamelCase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__lowerCamelCase = (np.arange(0 , lowerCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__lowerCamelCase = torch.from_numpy(lowerCamelCase__ ).to(lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None ) -> Union[str, Any]:
'''simple docstring'''
if prev_timestep is None:
__lowerCamelCase = t - 1
__lowerCamelCase = self.alphas_cumprod[t]
__lowerCamelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__lowerCamelCase = 1 - alpha_prod_t
__lowerCamelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__lowerCamelCase = self.betas[t]
else:
__lowerCamelCase = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__lowerCamelCase = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__lowerCamelCase = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__lowerCamelCase = torch.log(torch.clamp(lowerCamelCase__ , min=1e-20 ) )
__lowerCamelCase = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__lowerCamelCase = variance.log()
__lowerCamelCase = beta.log()
__lowerCamelCase = (predicted_variance + 1) / 2
__lowerCamelCase = frac * max_log + (1 - frac) * min_log
return variance
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__=None , lowerCamelCase__ = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
'''simple docstring'''
__lowerCamelCase = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__lowerCamelCase , __lowerCamelCase = torch.split(lowerCamelCase__ , sample.shape[1] , dim=1 )
else:
__lowerCamelCase = None
# 1. compute alphas, betas
if prev_timestep is None:
__lowerCamelCase = t - 1
__lowerCamelCase = self.alphas_cumprod[t]
__lowerCamelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__lowerCamelCase = 1 - alpha_prod_t
__lowerCamelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__lowerCamelCase = self.betas[t]
__lowerCamelCase = self.alphas[t]
else:
__lowerCamelCase = 1 - alpha_prod_t / alpha_prod_t_prev
__lowerCamelCase = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__lowerCamelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__lowerCamelCase = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
' for the UnCLIPScheduler.' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__lowerCamelCase = torch.clamp(
lowerCamelCase__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__lowerCamelCase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__lowerCamelCase = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__lowerCamelCase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__lowerCamelCase = 0
if t > 0:
__lowerCamelCase = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowerCamelCase__ , device=model_output.device )
__lowerCamelCase = self._get_variance(
lowerCamelCase__ , predicted_variance=lowerCamelCase__ , prev_timestep=lowerCamelCase__ , )
if self.variance_type == "fixed_small_log":
__lowerCamelCase = variance
elif self.variance_type == "learned_range":
__lowerCamelCase = (0.5 * variance).exp()
else:
raise ValueError(
f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
' for the UnCLIPScheduler.' )
__lowerCamelCase = variance * variance_noise
__lowerCamelCase = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase__ , pred_original_sample=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> torch.FloatTensor:
'''simple docstring'''
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
__lowerCamelCase = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__lowerCamelCase = timesteps.to(original_samples.device )
__lowerCamelCase = alphas_cumprod[timesteps] ** 0.5
__lowerCamelCase = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__lowerCamelCase = sqrt_alpha_prod.unsqueeze(-1 )
__lowerCamelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCamelCase = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__lowerCamelCase = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__lowerCamelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=100 , lowerCamelCase__=13 , lowerCamelCase__=30 , lowerCamelCase__=2 , lowerCamelCase__=3 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=10 , lowerCamelCase__=0.02 , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=[0, 1, 2, 3] , ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = 100
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = out_indices
__lowerCamelCase = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = num_patches + 1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = BeitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = BeitForMaskedImageModeling(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.type_sequence_label_size
__lowerCamelCase = BeitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__lowerCamelCase = 1
__lowerCamelCase = BeitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = BeitForSemanticSegmentation(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
__lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = BeitModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=37 )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='BEiT does not use inputs_embeds' )
def lowercase_ ( self ) -> str:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__ )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(lowerCamelCase__ ), BeitForMaskedImageModeling]:
continue
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__lowerCamelCase = False
__lowerCamelCase = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(lowerCamelCase__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
__lowerCamelCase = model_class(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ , return_labels=lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = _config_zero_init(lowerCamelCase__ )
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@slow
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = BeitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).pixel_values.to(lowerCamelCase__ )
# prepare bool_masked_pos
__lowerCamelCase = torch.ones((1, 196) , dtype=torch.bool ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , bool_masked_pos=lowerCamelCase__ )
__lowerCamelCase = outputs.logits
# verify the logits
__lowerCamelCase = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , lowerCamelCase__ , atol=1e-2 ) )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = outputs.logits
# verify the logits
__lowerCamelCase = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , lowerCamelCase__ )
__lowerCamelCase = torch.tensor([-1.23_85, -1.09_87, -1.01_08] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
__lowerCamelCase = 281
self.assertEqual(logits.argmax(-1 ).item() , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = outputs.logits
# verify the logits
__lowerCamelCase = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , lowerCamelCase__ )
__lowerCamelCase = torch.tensor([1.68_81, -0.27_87, 0.59_01] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
__lowerCamelCase = 2_396
self.assertEqual(logits.argmax(-1 ).item() , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
__lowerCamelCase = model.to(lowerCamelCase__ )
__lowerCamelCase = BeitImageProcessor(do_resize=lowerCamelCase__ , size=640 , do_center_crop=lowerCamelCase__ )
__lowerCamelCase = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
__lowerCamelCase = Image.open(ds[0]['file'] )
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = outputs.logits
# verify the logits
__lowerCamelCase = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , lowerCamelCase__ )
__lowerCamelCase = version.parse(PIL.__version__ ) < version.parse('9.0.0' )
if is_pillow_less_than_a:
__lowerCamelCase = torch.tensor(
[
[[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
[[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
[[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
] , device=lowerCamelCase__ , )
else:
__lowerCamelCase = torch.tensor(
[
[[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
[[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
[[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
] , device=lowerCamelCase__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
__lowerCamelCase = model.to(lowerCamelCase__ )
__lowerCamelCase = BeitImageProcessor(do_resize=lowerCamelCase__ , size=640 , do_center_crop=lowerCamelCase__ )
__lowerCamelCase = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
__lowerCamelCase = Image.open(ds[0]['file'] )
__lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = outputs.logits.detach().cpu()
__lowerCamelCase = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__ , target_sizes=[(500, 300)] )
__lowerCamelCase = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , lowerCamelCase__ )
__lowerCamelCase = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__ )
__lowerCamelCase = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , lowerCamelCase__ )
| 348 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__A = logging.get_logger(__name__)
__A = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[List[float]] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
else:
return _interleave_iterable_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
else:
return _concatenate_iterable_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
| 348 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , lowerCamelCase__ = False ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = scheduler
__lowerCamelCase = optimizers if isinstance(lowerCamelCase__ , (list, tuple) ) else [optimizers]
__lowerCamelCase = split_batches
__lowerCamelCase = step_with_optimizer
__lowerCamelCase = GradientState()
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__lowerCamelCase = AcceleratorState().num_processes
for _ in range(lowerCamelCase__ ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return self.scheduler.get_last_lr()
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
return self.scheduler.state_dict()
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
self.scheduler.load_state_dict(lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
return self.scheduler.get_lr()
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
return self.scheduler.print_lr(*lowerCamelCase__ , **lowerCamelCase__ )
| 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = ["model.decoder.embed_positions.weights"]
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if "emb" in name:
__lowerCamelCase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__lowerCamelCase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__lowerCamelCase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__lowerCamelCase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__lowerCamelCase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__lowerCamelCase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__lowerCamelCase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__lowerCamelCase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def lowerCamelCase_ ( UpperCamelCase__ : OrderedDict , UpperCamelCase__ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = rename_keys(UpperCamelCase__ )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
__lowerCamelCase = 1024
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 1536
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 2048
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=UpperCamelCase__ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCamelCase__ , num_attention_heads=UpperCamelCase__ , )
return config
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]="cpu" ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = MusicGen.get_pretrained(UpperCamelCase__ , device=UpperCamelCase__ )
__lowerCamelCase = decoder_config_from_checkpoint(UpperCamelCase__ )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase , __lowerCamelCase = rename_state_dict(
UpperCamelCase__ , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('t5-base' )
__lowerCamelCase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__lowerCamelCase = MusicgenForCausalLM(UpperCamelCase__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase , __lowerCamelCase = decoder.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ , audio_encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCamelCase__ )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('t5-base' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# set the appropriate bos/pad token ids
__lowerCamelCase = 2048
__lowerCamelCase = 2048
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCamelCase__ )
processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__A = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 348 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase_ ( UpperCamelCase__ : int = 8 ) -> str:
"""simple docstring"""
__lowerCamelCase = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCamelCase__ ) for _ in range(UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
i -= len(UpperCamelCase__ )
__lowerCamelCase = i // 3
__lowerCamelCase = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
__lowerCamelCase = (
chars_incl
+ random(UpperCamelCase__ , quotient + remainder )
+ random(UpperCamelCase__ , UpperCamelCase__ )
+ random(UpperCamelCase__ , UpperCamelCase__ )
)
__lowerCamelCase = list(UpperCamelCase__ )
shuffle(UpperCamelCase__ )
return "".join(UpperCamelCase__ )
# random is a generalised function for letters, characters and numbers
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
return "".join(secrets.choice(UpperCamelCase__ ) for _ in range(UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] ) -> Any:
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] ) -> Dict:
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : int = 8 ) -> bool:
"""simple docstring"""
if len(UpperCamelCase__ ) < min_length:
# Your Password must be at least 8 characters long
return False
__lowerCamelCase = any(char in ascii_uppercase for char in password )
__lowerCamelCase = any(char in ascii_lowercase for char in password )
__lowerCamelCase = any(char in digits for char in password )
__lowerCamelCase = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = int(input('Please indicate the max length of your password: ' ).strip() )
__lowerCamelCase = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' , password_generator(UpperCamelCase__ ) )
print(
'Alternative Password generated:' , alternative_password_generator(UpperCamelCase__ , UpperCamelCase__ ) , )
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 12
__lowerCamelCase = 12
__lowerCamelCase = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__lowerCamelCase = TransformeraDModel(**lowerCamelCase__ )
return model
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase__ )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCamelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__lowerCamelCase = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__lowerCamelCase = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__A = logging.get_logger("transformers.models.speecht5")
__A = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__A = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = []
__A = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
elif weight_type == "running_mean":
__lowerCamelCase = value
elif weight_type == "running_var":
__lowerCamelCase = value
elif weight_type == "num_batches_tracked":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
if task == "s2t":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2T
__lowerCamelCase = IGNORE_KEYS_S2T
elif task == "t2s":
__lowerCamelCase = None
__lowerCamelCase = MAPPING_T2S
__lowerCamelCase = IGNORE_KEYS_T2S
elif task == "s2s":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2S
__lowerCamelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(UpperCamelCase__ , UpperCamelCase__ ):
logger.info(F"""{name} was ignored""" )
continue
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
__lowerCamelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCamelCase = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCamelCase = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase = 'weight_v'
elif "bias" in name:
__lowerCamelCase = 'bias'
elif "weight" in name:
__lowerCamelCase = 'weight'
elif "running_mean" in name:
__lowerCamelCase = 'running_mean'
elif "running_var" in name:
__lowerCamelCase = 'running_var'
elif "num_batches_tracked" in name:
__lowerCamelCase = 'num_batches_tracked'
else:
__lowerCamelCase = None
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
continue
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = full_name.split('conv_layers.' )[-1]
__lowerCamelCase = name.split('.' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCamelCase__ )
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : str=None , UpperCamelCase__ : List[str]=None , ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__lowerCamelCase = SpeechTaConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCamelCase = SpeechTaConfig()
if task == "s2t":
__lowerCamelCase = config.max_text_positions
__lowerCamelCase = SpeechTaForSpeechToText(UpperCamelCase__ )
elif task == "t2s":
__lowerCamelCase = 1876
__lowerCamelCase = 600
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForTextToSpeech(UpperCamelCase__ )
elif task == "s2s":
__lowerCamelCase = 1876
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForSpeechToSpeech(UpperCamelCase__ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
__lowerCamelCase = SpeechTaTokenizer(UpperCamelCase__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken('<mask>' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
__lowerCamelCase = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
__lowerCamelCase = SpeechTaFeatureExtractor()
__lowerCamelCase = SpeechTaProcessor(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = torch.load(UpperCamelCase__ )
recursively_load_weights(fairseq_checkpoint['model'] , UpperCamelCase__ , UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if repo_id:
print('Pushing to the hub...' )
processor.push_to_hub(UpperCamelCase__ )
model.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__A = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 348 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__A = logging.getLogger(__name__)
__A = 50 # max width of layer names
__A = 70 # max width of quantizer names
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowerCamelCase = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=UpperCamelCase__ , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=UpperCamelCase__ , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=UpperCamelCase__ , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=UpperCamelCase__ , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=UpperCamelCase__ , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=UpperCamelCase__ , type=UpperCamelCase__ , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=UpperCamelCase__ , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if args.calibrator == "max":
__lowerCamelCase = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
__lowerCamelCase = 'histogram'
elif args.calibrator == "mse":
__lowerCamelCase = 'histogram'
else:
raise ValueError(F"""Invalid calibrator {args.calibrator}""" )
__lowerCamelCase = QuantDescriptor(num_bits=args.aprec , calib_method=UpperCamelCase__ )
__lowerCamelCase = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(UpperCamelCase__ )
quant_nn.QuantLinear.set_default_quant_desc_weight(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : List[str]=False ) -> Tuple:
"""simple docstring"""
logger.info('Configuring Model for Quantization' )
logger.info(F"""using quantization package {pytorch_quantization.__file__}""" )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(UpperCamelCase__ , ['embeddings'] , which='weight' , _disabled=UpperCamelCase__ )
if args.quant_disable:
set_quantizer_by_name(UpperCamelCase__ , [''] , _disabled=UpperCamelCase__ )
if args.quant_disable_keyword:
set_quantizer_by_name(UpperCamelCase__ , args.quant_disable_keyword , _disabled=UpperCamelCase__ )
if args.quant_disable_layer_module:
set_quantizer_by_name(UpperCamelCase__ , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=UpperCamelCase__ )
if args.quant_enable_layer_module:
set_quantizer_by_name(UpperCamelCase__ , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=UpperCamelCase__ )
if args.recalibrate_weights:
recalibrate_weights(UpperCamelCase__ )
if args.fuse_qkv:
fuse_qkv(UpperCamelCase__ , UpperCamelCase__ )
if args.clip_gelu:
clip_gelu(UpperCamelCase__ , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Optional[int]:
"""simple docstring"""
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F"""{name:80}: {module}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str ) -> str:
"""simple docstring"""
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str ) -> Optional[int]:
"""simple docstring"""
def fusea(UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ):
for mod in [qq, qk, qv]:
if not hasattr(UpperCamelCase__ , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
__lowerCamelCase = qq._amax.detach().item()
__lowerCamelCase = qk._amax.detach().item()
__lowerCamelCase = qv._amax.detach().item()
__lowerCamelCase = max(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
qq._amax.fill_(UpperCamelCase__ )
qk._amax.fill_(UpperCamelCase__ )
qv._amax.fill_(UpperCamelCase__ )
logger.info(F""" q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}""" )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(F"""FUSE_QKV: {name:{name_width}}""" )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] ) -> Any:
"""simple docstring"""
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
__lowerCamelCase = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=UpperCamelCase__ )
__lowerCamelCase = mod._input_quantizer._amax.data.detach().item()
logger.info(F"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}""" )
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(UpperCamelCase__ , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
__lowerCamelCase = mod.weight.shape[0]
__lowerCamelCase = mod._weight_quantizer._amax.detach()
__lowerCamelCase = torch.ones(UpperCamelCase__ , dtype=amax.dtype , device=amax.device ) * amax
print(F"""expanding {name} {amax} -> {mod._weight_quantizer._amax}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(UpperCamelCase__ , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
__lowerCamelCase = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
__lowerCamelCase = set(range(len(mod.weight.size() ) ) ) - axis_set
__lowerCamelCase = pytorch_quantization.utils.reduce_amax(mod.weight , axis=UpperCamelCase__ , keepdims=UpperCamelCase__ ).detach()
logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}""" )
__lowerCamelCase = amax
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any]=25 , UpperCamelCase__ : List[Any]=180 , UpperCamelCase__ : Any=None ) -> str:
"""simple docstring"""
if ignore is None:
__lowerCamelCase = []
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = [ignore]
__lowerCamelCase = 0
for name, mod in model.named_modules():
if not hasattr(UpperCamelCase__ , 'weight' ):
continue
__lowerCamelCase = max(UpperCamelCase__ , len(UpperCamelCase__ ) )
for name, mod in model.named_modules():
__lowerCamelCase = getattr(UpperCamelCase__ , '_input_quantizer' , UpperCamelCase__ )
__lowerCamelCase = getattr(UpperCamelCase__ , '_weight_quantizer' , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , 'weight' ):
continue
if type(UpperCamelCase__ ) in ignore:
continue
if [True for s in ignore if type(UpperCamelCase__ ) is str and s in name]:
continue
__lowerCamelCase = F"""Act:{input_q.extra_repr()}"""
__lowerCamelCase = F"""Wgt:{weight_q.extra_repr()}"""
__lowerCamelCase = F"""{name:{name_width}} {act_str} {wgt_str}"""
if len(UpperCamelCase__ ) <= line_width:
logger.info(UpperCamelCase__ )
else:
logger.info(F"""{name:{name_width}} {act_str}""" )
logger.info(F"""{' ':{name_width}} {wgt_str}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> str:
"""simple docstring"""
__lowerCamelCase = 0
for name, mod in model.named_modules():
if isinstance(UpperCamelCase__ , pytorch_quantization.nn.TensorQuantizer ):
print(F"""{name:80} {mod}""" )
count += 1
print(F"""{count} TensorQuantizers found in model""" )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if quantizer_mod is not None:
assert hasattr(UpperCamelCase__ , UpperCamelCase__ )
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
else:
logger.warning(F"""{name} has no {quantizer}""" )
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int]="both" , **UpperCamelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = F"""Warning: changing {which} quantizers of {name:{qname_width}}"""
for k, v in kwargs.items():
s += F""" {k}={v}"""
if which in ["input", "both"]:
set_quantizer(UpperCamelCase__ , UpperCamelCase__ , '_input_quantizer' , UpperCamelCase__ , UpperCamelCase__ )
if which in ["weight", "both"]:
set_quantizer(UpperCamelCase__ , UpperCamelCase__ , '_weight_quantizer' , UpperCamelCase__ , UpperCamelCase__ )
logger.info(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : str , **UpperCamelCase__ : Tuple ) -> List[str]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(UpperCamelCase__ , '_input_quantizer' ) or hasattr(UpperCamelCase__ , '_weight_quantizer' ):
for n in names:
if re.search(UpperCamelCase__ , UpperCamelCase__ ):
set_quantizers(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = F"""Warning: changing {name:{name_width}}"""
for k, v in kwargs.items():
s += F""" {k}={v}"""
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
logger.info(UpperCamelCase__ )
| 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''encoder-decoder'''
snake_case_ = True
def __init__( self , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
__lowerCamelCase = kwargs.pop('encoder' )
__lowerCamelCase = encoder_config.pop('model_type' )
__lowerCamelCase = kwargs.pop('decoder' )
__lowerCamelCase = decoder_config.pop('model_type' )
from ..auto.configuration_auto import AutoConfig
__lowerCamelCase = AutoConfig.for_model(lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = AutoConfig.for_model(lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = True
@classmethod
def lowercase_ ( cls , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
__lowerCamelCase = True
__lowerCamelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **lowerCamelCase__ )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.encoder.to_dict()
__lowerCamelCase = self.decoder.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
__lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
}
__lowerCamelCase = Dataset.from_dict(UpperCamelCase__ )
return dataset
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = get_dataset()
__lowerCamelCase = make_duplicate_clusters(lowerCamelCase__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = get_dataset()
__lowerCamelCase , __lowerCamelCase = deduplicate_dataset(lowerCamelCase__ )
self.assertEqual(len(lowerCamelCase__ ) , 2 )
print(lowerCamelCase__ )
self.assertEqual(duplicate_clusters[0][0]['copies'] , 2 )
self.assertEqual(duplicate_clusters[0][0]['is_extreme'] , lowerCamelCase__ )
| 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
__lowerCamelCase = [5, 5, 5, 5]
elif "fl4" in model_name:
__lowerCamelCase = [4, 4, 4, 4]
__lowerCamelCase = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
if "lrf" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
else:
__lowerCamelCase = [2, 2, 2, 2]
if "tiny" in model_name:
__lowerCamelCase = 96
elif "small" in model_name:
__lowerCamelCase = 96
elif "base" in model_name:
__lowerCamelCase = 128
elif "large" in model_name:
__lowerCamelCase = 192
elif "xlarge" in model_name:
__lowerCamelCase = 256
elif "huge" in model_name:
__lowerCamelCase = 352
# set label information
__lowerCamelCase = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
__lowerCamelCase = 'imagenet-22k-id2label.json'
else:
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = FocalNetConfig(
embed_dim=UpperCamelCase__ , depths=UpperCamelCase__ , focal_levels=UpperCamelCase__ , focal_windows=UpperCamelCase__ , use_conv_embed=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , use_post_layernorm=UpperCamelCase__ , use_layerscale=UpperCamelCase__ , )
return config
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
if "patch_embed.proj" in name:
__lowerCamelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__lowerCamelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
__lowerCamelCase = 'encoder.' + name
if "encoder.layers" in name:
__lowerCamelCase = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
__lowerCamelCase = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
__lowerCamelCase = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__lowerCamelCase = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__lowerCamelCase = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__lowerCamelCase = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
__lowerCamelCase = 'layernorm.weight'
if name == "norm.bias":
__lowerCamelCase = 'layernorm.bias'
if "head" in name:
__lowerCamelCase = name.replace('head' , 'classifier' )
else:
__lowerCamelCase = 'focalnet.' + name
return name
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple=False ) -> Dict:
"""simple docstring"""
__lowerCamelCase = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
__lowerCamelCase = model_name_to_url[model_name]
print('Checkpoint URL: ' , UpperCamelCase__ )
__lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = val
__lowerCamelCase = get_focalnet_config(UpperCamelCase__ )
__lowerCamelCase = FocalNetForImageClassification(UpperCamelCase__ )
model.eval()
# load state dict
model.load_state_dict(UpperCamelCase__ )
# verify conversion
__lowerCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase = BitImageProcessor(
do_resize=UpperCamelCase__ , size={'shortest_edge': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCamelCase__ , crop_size=224 , do_normalize=UpperCamelCase__ , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ , )
__lowerCamelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
__lowerCamelCase = processor(images=UpperCamelCase__ , return_tensors='pt' )
__lowerCamelCase = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ),
] )
__lowerCamelCase = image_transforms(UpperCamelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , UpperCamelCase__ , atol=1E-4 )
__lowerCamelCase = model(**UpperCamelCase__ )
__lowerCamelCase = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__lowerCamelCase = torch.tensor([0.21_66, -0.43_68, 0.21_91] )
elif model_name == "focalnet-tiny-lrf":
__lowerCamelCase = torch.tensor([1.16_69, 0.01_25, -0.16_95] )
elif model_name == "focalnet-small":
__lowerCamelCase = torch.tensor([0.49_17, -0.04_30, 0.13_41] )
elif model_name == "focalnet-small-lrf":
__lowerCamelCase = torch.tensor([-0.25_88, -0.53_42, -0.23_31] )
elif model_name == "focalnet-base":
__lowerCamelCase = torch.tensor([-0.16_55, -0.40_90, -0.17_30] )
elif model_name == "focalnet-base-lrf":
__lowerCamelCase = torch.tensor([0.53_06, -0.04_83, -0.39_28] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(F"""{model_name}""" )
processor.push_to_hub(F"""{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
__A = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''xlm-roberta-xl'''
def __init__( self , lowerCamelCase__=250_880 , lowerCamelCase__=2_560 , lowerCamelCase__=36 , lowerCamelCase__=32 , lowerCamelCase__=10_240 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=514 , lowerCamelCase__=1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-05 , lowerCamelCase__=1 , lowerCamelCase__=0 , lowerCamelCase__=2 , lowerCamelCase__="absolute" , lowerCamelCase__=True , lowerCamelCase__=None , **lowerCamelCase__ , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = position_embedding_type
__lowerCamelCase = use_cache
__lowerCamelCase = classifier_dropout
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__lowerCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__lowerCamelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __lowerCamelCase = divmod(len(UpperCamelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = [float(x) for x in input("Enter the elements of first array: ").split()]
__A = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 348 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A = logging.get_logger(__name__)
__A = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__A = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__A = {"facebook/blenderbot-3B": 1_28}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
__lowerCamelCase = bs[:]
__lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(UpperCamelCase__ )
cs.append(2**8 + n )
n += 1
__lowerCamelCase = [chr(UpperCamelCase__ ) for n in cs]
return dict(zip(UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = set()
__lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowerCamelCase = char
return pairs
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='utf-8' ) as vocab_handle:
__lowerCamelCase = json.load(lowerCamelCase__ )
__lowerCamelCase = {v: k for k, v in self.encoder.items()}
__lowerCamelCase = errors # how to handle errors in decoding
__lowerCamelCase = bytes_to_unicode()
__lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='utf-8' ) as merges_handle:
__lowerCamelCase = merges_handle.read().split('\n' )[1:-1]
__lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
__lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
__lowerCamelCase = {}
__lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowerCamelCase = re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
return len(self.encoder )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowerCamelCase = tuple(lowerCamelCase__ )
__lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
__lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__lowerCamelCase , __lowerCamelCase = bigram
__lowerCamelCase = []
__lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
__lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowerCamelCase = tuple(lowerCamelCase__ )
__lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
__lowerCamelCase = get_pairs(lowerCamelCase__ )
__lowerCamelCase = ' '.join(lowerCamelCase__ )
__lowerCamelCase = word
return word
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
__lowerCamelCase = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(' ' ) )
return bpe_tokens
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ''.join(lowerCamelCase__ )
__lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(lowerCamelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '\n' )
__lowerCamelCase = 0
with open(lowerCamelCase__ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
' Please check that the tokenizer is not corrupted!' )
__lowerCamelCase = token_index
writer.write(' '.join(lowerCamelCase__ ) + '\n' )
index += 1
return vocab_file, merge_file
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1, 1] + ([0] * len(lowerCamelCase__ )) + [1]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
__lowerCamelCase = ' ' + text
return (text, kwargs)
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Optional[Any]:
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def lowercase_ ( self , lowerCamelCase__ ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(' ' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
__lowerCamelCase = ' '.join(lowerCamelCase__ )
__lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
__lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = DDIMPipeline
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
snake_case_ = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''latents''',
'''callback''',
'''callback_steps''',
}
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
snake_case_ = False
def lowercase_ ( self ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
__lowerCamelCase = DDIMScheduler()
__lowerCamelCase = {'unet': unet, 'scheduler': scheduler}
return components
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__=0 ) -> Optional[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith('mps' ):
__lowerCamelCase = torch.manual_seed(lowerCamelCase__ )
else:
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__lowerCamelCase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = self.pipeline_class(**lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs(lowerCamelCase__ )
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__lowerCamelCase = np.array(
[1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04] )
__lowerCamelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase__ , 1e-3 )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowercase_ ( self ) -> int:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3e-3 )
def lowercase_ ( self ) -> int:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = 'google/ddpm-cifar10-32'
__lowerCamelCase = UNetaDModel.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = DDIMScheduler()
__lowerCamelCase = DDIMPipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
ddim.to(lowerCamelCase__ )
ddim.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = ddim(generator=lowerCamelCase__ , eta=0.0 , output_type='numpy' ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = 'google/ddpm-ema-bedroom-256'
__lowerCamelCase = UNetaDModel.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = DDIMScheduler.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = DDIMPipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
ddpm.to(lowerCamelCase__ )
ddpm.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = ddpm(generator=lowerCamelCase__ , output_type='numpy' ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__lowerCamelCase = np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''philschmid/bart-large-cnn-samsum'''
snake_case_ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
snake_case_ = '''summarizer'''
snake_case_ = AutoTokenizer
snake_case_ = AutoModelForSeqaSeqLM
snake_case_ = ['''text''']
snake_case_ = ['''text''']
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
return self.model.generate(**lowerCamelCase__ )[0]
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
| 348 | 1 |
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> list[str]:
"""simple docstring"""
__lowerCamelCase = []
__lowerCamelCase = 11
__lowerCamelCase = int('1' + '0' * digit_len )
for num in range(UpperCamelCase__ , UpperCamelCase__ ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(UpperCamelCase__ , UpperCamelCase__ ):
solutions.append(F"""{num}/{den}""" )
den += 1
num += 1
__lowerCamelCase = 10
return solutions
def lowerCamelCase_ ( UpperCamelCase__ : int = 2 ) -> int:
"""simple docstring"""
__lowerCamelCase = 1.0
for fraction in fraction_list(UpperCamelCase__ ):
__lowerCamelCase = Fraction(UpperCamelCase__ )
result *= frac.denominator / frac.numerator
return int(UpperCamelCase__ )
if __name__ == "__main__":
print(solution())
| 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_choices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = True
snake_case_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowerCamelCase__ )
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 348 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> Any:
"""simple docstring"""
__lowerCamelCase = []
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
F"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
F"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
F"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
F"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : str ) -> int:
"""simple docstring"""
__lowerCamelCase = []
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = []
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
return token
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = 1000
__lowerCamelCase = 'huggingface/label-files'
__lowerCamelCase = num_labels
__lowerCamelCase = json.load(open(cached_download(hf_hub_url(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = __lowerCamelCase = CvtConfig(num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
__lowerCamelCase = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
__lowerCamelCase = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__lowerCamelCase = [2, 2, 20]
__lowerCamelCase = [3, 12, 16]
__lowerCamelCase = [192, 768, 1024]
__lowerCamelCase = CvtForImageClassification(UpperCamelCase__ )
__lowerCamelCase = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__lowerCamelCase = image_size
__lowerCamelCase = torch.load(UpperCamelCase__ , map_location=torch.device('cpu' ) )
__lowerCamelCase = OrderedDict()
__lowerCamelCase = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__lowerCamelCase = list_of_state_dict + cls_token(UpperCamelCase__ )
__lowerCamelCase = list_of_state_dict + embeddings(UpperCamelCase__ )
for cnt in range(config.depth[idx] ):
__lowerCamelCase = list_of_state_dict + attention(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = list_of_state_dict + final()
for gg in list_of_state_dict:
print(UpperCamelCase__ )
for i in range(len(UpperCamelCase__ ) ):
__lowerCamelCase = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
image_processor.save_pretrained(UpperCamelCase__ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--cvt_model",
default="cvt-w24",
type=str,
help="Name of the cvt model you'd like to convert.",
)
parser.add_argument(
"--image_size",
default=3_84,
type=int,
help="Input Image Size",
)
parser.add_argument(
"--cvt_file_name",
default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
type=str,
help="Input Image Size",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__A = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
__A = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__A = [{"type": "code", "content": INSTALL_CONTENT}]
__A = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : int ) -> list:
"""simple docstring"""
__lowerCamelCase = word.split()
def justify(UpperCamelCase__ : list , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> str:
__lowerCamelCase = max_width - width
__lowerCamelCase = len(UpperCamelCase__ )
if len(UpperCamelCase__ ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
__lowerCamelCase = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
__lowerCamelCase = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
__lowerCamelCase = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(UpperCamelCase__ ):
num_spaces_between_words_list[i] += 1
__lowerCamelCase = []
for i in range(UpperCamelCase__ ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = 0
for word in words:
if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(UpperCamelCase__ )
width += len(UpperCamelCase__ )
else:
# justify the line and add it to result
answer.append(justify(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) )
# reset new line and new width
__lowerCamelCase , __lowerCamelCase = [word], len(UpperCamelCase__ )
__lowerCamelCase = max_width - width - len(UpperCamelCase__ )
answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__A = logging.get_logger(__name__)
__A = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''whisper'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=51_865 , lowerCamelCase__=80 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=1_536 , lowerCamelCase__=1_536 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=50_257 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="gelu" , lowerCamelCase__=256 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=False , lowerCamelCase__=1_500 , lowerCamelCase__=448 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=None , lowerCamelCase__=[220, 50_256] , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=False , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__=7 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = num_mel_bins
__lowerCamelCase = d_model
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = max_source_positions
__lowerCamelCase = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
__lowerCamelCase = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
__lowerCamelCase = median_filter_width
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , suppress_tokens=lowerCamelCase__ , begin_suppress_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__lowerCamelCase = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
__lowerCamelCase = {0: 'batch'}
else:
__lowerCamelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ , direction='inputs' )
return common_inputs
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = -1 , lowerCamelCase__ = -1 , lowerCamelCase__ = False , lowerCamelCase__ = None , lowerCamelCase__ = 22_050 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
__lowerCamelCase = OrderedDict()
__lowerCamelCase = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowerCamelCase__ , framework=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , time_duration=lowerCamelCase__ , frequency=lowerCamelCase__ , )
__lowerCamelCase = encoder_inputs['input_features'].shape[2]
__lowerCamelCase = encoder_sequence_length // 2 if self.use_past else seq_length
__lowerCamelCase = super().generate_dummy_inputs(
preprocessor.tokenizer , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = encoder_inputs.pop('input_features' )
__lowerCamelCase = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
__lowerCamelCase = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-3
| 348 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
__A = {
"gpt2": 10_24,
"gpt2-medium": 10_24,
"gpt2-large": 10_24,
"gpt2-xl": 10_24,
"distilgpt2": 10_24,
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ['''input_ids''', '''attention_mask''']
snake_case_ = GPTaTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="<|endoftext|>" , lowerCamelCase__="<|endoftext|>" , lowerCamelCase__="<|endoftext|>" , lowerCamelCase__=False , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
super().__init__(
lowerCamelCase__ , lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , unk_token=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
__lowerCamelCase = kwargs.pop('add_bos_token' , lowerCamelCase__ )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowerCamelCase__ ) != add_prefix_space:
__lowerCamelCase = getattr(lowerCamelCase__ , pre_tok_state.pop('type' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**lowerCamelCase__ )
__lowerCamelCase = add_prefix_space
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> BatchEncoding:
'''simple docstring'''
__lowerCamelCase = kwargs.get('is_split_into_words' , lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> BatchEncoding:
'''simple docstring'''
__lowerCamelCase = kwargs.get('is_split_into_words' , lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ ) + [self.eos_token_id] )
if len(lowerCamelCase__ ) > self.model_max_length:
__lowerCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=14 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=0.02 , ) -> int:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = rotary_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = None
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=lowerCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , attention_mask=lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
snake_case_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = FlaxGPTJModelTester(self )
def lowercase_ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@tooslow
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
__lowerCamelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )
__lowerCamelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = False
__lowerCamelCase = model.config.eos_token_id
__lowerCamelCase = jax.jit(model.generate )
__lowerCamelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
__lowerCamelCase = tokenizer.batch_decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
__lowerCamelCase = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCamelCase__ )
__lowerCamelCase = fx_state
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = model_class.from_pretrained(lowerCamelCase__ , from_pt=lowerCamelCase__ )
__lowerCamelCase = fx_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = load_flax_weights_in_pytorch_model(lowerCamelCase__ , fx_model.params )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = pt_model_class.from_pretrained(lowerCamelCase__ , from_flax=lowerCamelCase__ )
with torch.no_grad():
__lowerCamelCase = pt_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowerCamelCase = [0] * len(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = [1] * len(UpperCamelCase__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(UpperCamelCase__ ) ):
if indegree[i] == 0:
queue.append(UpperCamelCase__ )
while queue:
__lowerCamelCase = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__lowerCamelCase = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(UpperCamelCase__ )
print(max(UpperCamelCase__ ) )
# Adjacency list of Graph
__A = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 12
__lowerCamelCase = 12
__lowerCamelCase = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__lowerCamelCase = TransformeraDModel(**lowerCamelCase__ )
return model
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase__ )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCamelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__lowerCamelCase = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__lowerCamelCase = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__A = None
__A = logging.get_logger(__name__)
__A = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
),
},
"tokenizer_file": {
"google/bigbird-roberta-base": (
"https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
),
"google/bigbird-roberta-large": (
"https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
),
"google/bigbird-base-trivia-itc": (
"https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
),
},
}
__A = {
"google/bigbird-roberta-base": 40_96,
"google/bigbird-roberta-large": 40_96,
"google/bigbird-base-trivia-itc": 40_96,
}
__A = "▁"
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BigBirdTokenizer
snake_case_ = ['''input_ids''', '''attention_mask''']
snake_case_ = []
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="<unk>" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<pad>" , lowerCamelCase__="[SEP]" , lowerCamelCase__="[MASK]" , lowerCamelCase__="[CLS]" , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , **lowerCamelCase__ , )
__lowerCamelCase = vocab_file
__lowerCamelCase = False if not self.vocab_file else True
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(lowerCamelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ):
copyfile(self.vocab_file , lowerCamelCase__ )
return (out_vocab_file,)
| 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.dummy_uncond_unet
__lowerCamelCase = KarrasVeScheduler()
__lowerCamelCase = KarrasVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(num_inference_steps=2 , generator=lowerCamelCase__ , output_type='numpy' ).images
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(num_inference_steps=2 , generator=lowerCamelCase__ , output_type='numpy' , return_dict=lowerCamelCase__ )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = 'google/ncsnpp-celebahq-256'
__lowerCamelCase = UNetaDModel.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = KarrasVeScheduler()
__lowerCamelCase = KarrasVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(num_inference_steps=20 , generator=lowerCamelCase__ , output_type='numpy' ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__lowerCamelCase = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mask2former'''
snake_case_ = ['''swin''']
snake_case_ = {'''hidden_size''': '''hidden_dim'''}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 1_024 , lowerCamelCase__ = "relu" , lowerCamelCase__ = 6 , lowerCamelCase__ = 10 , lowerCamelCase__ = 8 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 2_048 , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 4 , lowerCamelCase__ = 255 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 12_544 , lowerCamelCase__ = 3.0 , lowerCamelCase__ = 0.75 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = True , lowerCamelCase__ = [4, 8, 16, 32] , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Tuple:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowerCamelCase = CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = backbone_config.pop('model_type' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(lowerCamelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**lowerCamelCase__ )
@classmethod
def lowercase_ ( cls , lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return cls(
backbone_config=lowerCamelCase__ , **lowerCamelCase__ , )
def lowercase_ ( self ) -> Dict[str, any]:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 348 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__A = logging.get_logger(__name__)
# General docstring
__A = "MobileNetV1Config"
# Base docstring
__A = "google/mobilenet_v1_1.0_224"
__A = [1, 10_24, 7, 7]
# Image classification docstring
__A = "google/mobilenet_v1_1.0_224"
__A = "tabby, tabby cat"
__A = [
"google/mobilenet_v1_1.0_224",
"google/mobilenet_v1_0.75_192",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple=None ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = {}
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = model.mobilenet_va
else:
__lowerCamelCase = model
__lowerCamelCase = 'MobilenetV1/Conv2d_0/'
__lowerCamelCase = backbone.conv_stem.convolution.weight
__lowerCamelCase = backbone.conv_stem.normalization.bias
__lowerCamelCase = backbone.conv_stem.normalization.weight
__lowerCamelCase = backbone.conv_stem.normalization.running_mean
__lowerCamelCase = backbone.conv_stem.normalization.running_var
for i in range(13 ):
__lowerCamelCase = i + 1
__lowerCamelCase = i * 2
__lowerCamelCase = backbone.layer[pt_index]
__lowerCamelCase = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
__lowerCamelCase = pointer.convolution.weight
__lowerCamelCase = pointer.normalization.bias
__lowerCamelCase = pointer.normalization.weight
__lowerCamelCase = pointer.normalization.running_mean
__lowerCamelCase = pointer.normalization.running_var
__lowerCamelCase = backbone.layer[pt_index + 1]
__lowerCamelCase = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
__lowerCamelCase = pointer.convolution.weight
__lowerCamelCase = pointer.normalization.bias
__lowerCamelCase = pointer.normalization.weight
__lowerCamelCase = pointer.normalization.running_mean
__lowerCamelCase = pointer.normalization.running_var
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__lowerCamelCase = model.classifier.weight
__lowerCamelCase = model.classifier.bias
return tf_to_pt_map
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__lowerCamelCase = tf.train.list_variables(UpperCamelCase__ )
__lowerCamelCase = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
__lowerCamelCase = tf.train.load_variable(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = array
# Build TF to PyTorch weights loading map
__lowerCamelCase = _build_tf_to_pytorch_map(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for name, pointer in tf_to_pt_map.items():
logger.info(F"""Importing {name}""" )
if name not in tf_weights:
logger.info(F"""{name} not in tf pre-trained weights, skipping""" )
continue
__lowerCamelCase = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__lowerCamelCase = np.transpose(UpperCamelCase__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__lowerCamelCase = array.squeeze().transpose()
else:
__lowerCamelCase = np.transpose(UpperCamelCase__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(F"""Initialize PyTorch weight {name} {array.shape}""" )
__lowerCamelCase = torch.from_numpy(UpperCamelCase__ )
tf_weights.pop(UpperCamelCase__ , UpperCamelCase__ )
tf_weights.pop(name + '/RMSProp' , UpperCamelCase__ )
tf_weights.pop(name + '/RMSProp_1' , UpperCamelCase__ )
tf_weights.pop(name + '/ExponentialMovingAverage' , UpperCamelCase__ )
logger.info(F"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def lowerCamelCase_ ( UpperCamelCase__ : torch.Tensor , UpperCamelCase__ : nn.Convad ) -> torch.Tensor:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = features.shape[-2:]
__lowerCamelCase , __lowerCamelCase = conv_layer.stride
__lowerCamelCase , __lowerCamelCase = conv_layer.kernel_size
if in_height % stride_height == 0:
__lowerCamelCase = max(kernel_height - stride_height , 0 )
else:
__lowerCamelCase = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__lowerCamelCase = max(kernel_width - stride_width , 0 )
else:
__lowerCamelCase = max(kernel_width - (in_width % stride_width) , 0 )
__lowerCamelCase = pad_along_width // 2
__lowerCamelCase = pad_along_width - pad_left
__lowerCamelCase = pad_along_height // 2
__lowerCamelCase = pad_along_height - pad_top
__lowerCamelCase = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(UpperCamelCase__ , UpperCamelCase__ , 'constant' , 0.0 )
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 1 , lowerCamelCase__ = 1 , lowerCamelCase__ = False , lowerCamelCase__ = True , lowerCamelCase__ = True , ) -> None:
'''simple docstring'''
super().__init__()
__lowerCamelCase = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
__lowerCamelCase = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
__lowerCamelCase = nn.Convad(
in_channels=lowerCamelCase__ , out_channels=lowerCamelCase__ , kernel_size=lowerCamelCase__ , stride=lowerCamelCase__ , padding=lowerCamelCase__ , groups=lowerCamelCase__ , bias=lowerCamelCase__ , padding_mode='zeros' , )
if use_normalization:
__lowerCamelCase = nn.BatchNormad(
num_features=lowerCamelCase__ , eps=config.layer_norm_eps , momentum=0.99_97 , affine=lowerCamelCase__ , track_running_stats=lowerCamelCase__ , )
else:
__lowerCamelCase = None
if use_activation:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = ACTaFN[use_activation]
elif isinstance(config.hidden_act , lowerCamelCase__ ):
__lowerCamelCase = ACTaFN[config.hidden_act]
else:
__lowerCamelCase = config.hidden_act
else:
__lowerCamelCase = None
def lowercase_ ( self , lowerCamelCase__ ) -> torch.Tensor:
'''simple docstring'''
if self.config.tf_padding:
__lowerCamelCase = apply_tf_padding(lowerCamelCase__ , self.convolution )
__lowerCamelCase = self.convolution(lowerCamelCase__ )
if self.normalization is not None:
__lowerCamelCase = self.normalization(lowerCamelCase__ )
if self.activation is not None:
__lowerCamelCase = self.activation(lowerCamelCase__ )
return features
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = MobileNetVaConfig
snake_case_ = load_tf_weights_in_mobilenet_va
snake_case_ = '''mobilenet_v1'''
snake_case_ = '''pixel_values'''
snake_case_ = False
def lowercase_ ( self , lowerCamelCase__ ) -> None:
'''simple docstring'''
if isinstance(lowerCamelCase__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(lowerCamelCase__ , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
__A = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.''' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ = True ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
__lowerCamelCase = config
__lowerCamelCase = 32
__lowerCamelCase = max(int(depth * config.depth_multiplier ) , config.min_depth )
__lowerCamelCase = MobileNetVaConvLayer(
lowerCamelCase__ , in_channels=config.num_channels , out_channels=lowerCamelCase__ , kernel_size=3 , stride=2 , )
__lowerCamelCase = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__lowerCamelCase = nn.ModuleList()
for i in range(13 ):
__lowerCamelCase = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__lowerCamelCase = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
lowerCamelCase__ , in_channels=lowerCamelCase__ , out_channels=lowerCamelCase__ , kernel_size=3 , stride=strides[i] , groups=lowerCamelCase__ , ) )
self.layer.append(
MobileNetVaConvLayer(
lowerCamelCase__ , in_channels=lowerCamelCase__ , out_channels=lowerCamelCase__ , kernel_size=1 , ) )
__lowerCamelCase = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCamelCase__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase_ ( self , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
'''simple docstring'''
__lowerCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__lowerCamelCase = self.conv_stem(lowerCamelCase__ )
__lowerCamelCase = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
__lowerCamelCase = layer_module(lowerCamelCase__ )
if output_hidden_states:
__lowerCamelCase = all_hidden_states + (hidden_states,)
__lowerCamelCase = hidden_states
if self.pooler is not None:
__lowerCamelCase = torch.flatten(self.pooler(lowerCamelCase__ ) , start_dim=1 )
else:
__lowerCamelCase = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCamelCase__ , pooler_output=lowerCamelCase__ , hidden_states=lowerCamelCase__ , )
@add_start_docstrings(
'''
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> None:
'''simple docstring'''
super().__init__(lowerCamelCase__ )
__lowerCamelCase = config.num_labels
__lowerCamelCase = MobileNetVaModel(lowerCamelCase__ )
__lowerCamelCase = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__lowerCamelCase = nn.Dropout(config.classifier_dropout_prob , inplace=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCamelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase_ ( self , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
__lowerCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase = self.mobilenet_va(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ , return_dict=lowerCamelCase__ )
__lowerCamelCase = outputs.pooler_output if return_dict else outputs[1]
__lowerCamelCase = self.classifier(self.dropout(lowerCamelCase__ ) )
__lowerCamelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__lowerCamelCase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__lowerCamelCase = 'single_label_classification'
else:
__lowerCamelCase = 'multi_label_classification'
if self.config.problem_type == "regression":
__lowerCamelCase = MSELoss()
if self.num_labels == 1:
__lowerCamelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__lowerCamelCase = loss_fct(lowerCamelCase__ , lowerCamelCase__ )
elif self.config.problem_type == "single_label_classification":
__lowerCamelCase = CrossEntropyLoss()
__lowerCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__lowerCamelCase = BCEWithLogitsLoss()
__lowerCamelCase = loss_fct(lowerCamelCase__ , lowerCamelCase__ )
if not return_dict:
__lowerCamelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=lowerCamelCase__ , logits=lowerCamelCase__ , hidden_states=outputs.hidden_states , )
| 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = (KDPMaDiscreteScheduler,)
snake_case_ = 10
def lowercase_ ( self , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = {
'num_train_timesteps': 1_100,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**lowerCamelCase__ )
return config
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] , [0.00_02, 0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCamelCase__ , beta_end=lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(prediction_type='v_prediction' )
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowerCamelCase = sample.to(lowerCamelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__lowerCamelCase = scheduler.scale_model_input(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = output.prev_sample
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
assert abs(result_mean.item() - 0.00_02 ) < 1e-3
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
if torch_device == "mps":
return
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowerCamelCase = sample.to(lowerCamelCase__ )
for i, t in enumerate(scheduler.timesteps ):
__lowerCamelCase = scheduler.scale_model_input(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = output.prev_sample
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.41_25 ) < 1e-2
assert abs(result_mean.item() - 0.02_66 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.41_25 ) < 1e-2
assert abs(result_mean.item() - 0.02_66 ) < 1e-3
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
if torch_device == "mps":
return
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCamelCase__ )
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter.to(lowerCamelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowerCamelCase = scheduler.scale_model_input(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = output.prev_sample
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
if str(lowerCamelCase__ ).startswith('cpu' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.41_25 ) < 1e-2
assert abs(result_mean.item() - 0.02_66 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.41_25 ) < 1e-2
assert abs(result_mean.item() - 0.02_66 ) < 1e-3
| 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.node_position[vertex]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = pos
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowerCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowerCamelCase = 2 * start + 1
else:
__lowerCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowerCamelCase , __lowerCamelCase = heap[smallest_child], positions[smallest_child]
__lowerCamelCase , __lowerCamelCase = (
heap[start],
positions[start],
)
__lowerCamelCase , __lowerCamelCase = temp, tempa
__lowerCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase__ )
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = position[index]
while index != 0:
__lowerCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowerCamelCase = heap[parent]
__lowerCamelCase = position[parent]
self.set_position(position[parent] , lowerCamelCase__ )
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , lowerCamelCase__ )
break
__lowerCamelCase = parent
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , 0 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = len(lowerCamelCase__ ) // 2 - 1
for i in range(lowerCamelCase__ , -1 , -1 ):
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = positions[0]
__lowerCamelCase = sys.maxsize
self.top_to_bottom(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
return temp
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Heap()
__lowerCamelCase = [0] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowerCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
__lowerCamelCase = []
for vertex in range(len(UpperCamelCase__ ) ):
distance_tv.append(sys.maxsize )
positions.append(UpperCamelCase__ )
heap.node_position.append(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = 1
__lowerCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowerCamelCase = 0
__lowerCamelCase = distance
heap.heapify(UpperCamelCase__ , UpperCamelCase__ )
for _ in range(1 , len(UpperCamelCase__ ) ):
__lowerCamelCase = heap.delete_minimum(UpperCamelCase__ , UpperCamelCase__ )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowerCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(UpperCamelCase__ )]
):
__lowerCamelCase = distance
heap.bottom_to_top(
UpperCamelCase__ , heap.get_position(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__A = int(input("Enter number of edges: ").strip())
__A = defaultdict(list)
for _ in range(edges_number):
__A = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 348 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__A = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=7 , lowerCamelCase__=3 , lowerCamelCase__=18 , lowerCamelCase__=30 , lowerCamelCase__=400 , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=None , ) -> str:
'''simple docstring'''
__lowerCamelCase = size if size is not None else {'height': 20, 'width': 20}
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = num_channels
__lowerCamelCase = image_size
__lowerCamelCase = min_resolution
__lowerCamelCase = max_resolution
__lowerCamelCase = size
__lowerCamelCase = do_normalize
__lowerCamelCase = do_convert_rgb
__lowerCamelCase = [512, 1_024, 2_048, 4_096]
__lowerCamelCase = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def lowercase_ ( self ) -> int:
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
__lowerCamelCase = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = PixaStructImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = PixaStructImageProcessingTester(self )
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_convert_rgb' ) )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.image_processor_tester.prepare_dummy_image()
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
__lowerCamelCase = 2_048
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.06_06 ) , atol=1e-3 , rtol=1e-3 ) )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase_ ( self ) -> int:
'''simple docstring'''
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
__lowerCamelCase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowerCamelCase__ ):
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
__lowerCamelCase = 'Hello'
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ , header_text=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ , header_text=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , numpify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , np.ndarray )
__lowerCamelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowercase_ ( self ) -> str:
'''simple docstring'''
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , torchify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , torch.Tensor )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = PixaStructImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = PixaStructImageProcessingTester(self , num_channels=4 )
__lowerCamelCase = 3
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_convert_rgb' ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# Initialize image_processor
__lowerCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
__lowerCamelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
__lowerCamelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
__lowerCamelCase = image_processor(
lowerCamelCase__ , return_tensors='pt' , max_patches=lowerCamelCase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
snake_case_ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
snake_case_ = "question"
snake_case_ = "context"
snake_case_ = "answers"
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 348 | 1 |
import argparse
import copy
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = {}
with open(UpperCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__lowerCamelCase = []
_list.append([line.split()[1], line.split()[2]] )
__lowerCamelCase = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
__lowerCamelCase = []
_list.append([line.split()[0], line.split()[2]] )
__lowerCamelCase = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : int ) -> Optional[int]:
"""simple docstring"""
with open(UpperCamelCase__ ) as f:
__lowerCamelCase = f.read(1 )
__lowerCamelCase = start_node
__lowerCamelCase = []
__lowerCamelCase = start_node
__lowerCamelCase = 0
while visiting not in first_solution:
__lowerCamelCase = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(UpperCamelCase__ ) and k[0] not in first_solution:
__lowerCamelCase = k[1]
__lowerCamelCase = k[0]
first_solution.append(UpperCamelCase__ )
__lowerCamelCase = distance_of_first_solution + int(UpperCamelCase__ )
__lowerCamelCase = best_node
first_solution.append(UpperCamelCase__ )
__lowerCamelCase = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
__lowerCamelCase = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] ) -> str:
"""simple docstring"""
__lowerCamelCase = []
for n in solution[1:-1]:
__lowerCamelCase = solution.index(UpperCamelCase__ )
for kn in solution[1:-1]:
__lowerCamelCase = solution.index(UpperCamelCase__ )
if n == kn:
continue
__lowerCamelCase = copy.deepcopy(UpperCamelCase__ )
__lowerCamelCase = kn
__lowerCamelCase = n
__lowerCamelCase = 0
for k in _tmp[:-1]:
__lowerCamelCase = _tmp[_tmp.index(UpperCamelCase__ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
__lowerCamelCase = distance + int(i[1] )
_tmp.append(UpperCamelCase__ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
__lowerCamelCase = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda UpperCamelCase__ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
__lowerCamelCase = 1
__lowerCamelCase = first_solution
__lowerCamelCase = []
__lowerCamelCase = distance_of_first_solution
__lowerCamelCase = solution
while count <= iters:
__lowerCamelCase = find_neighborhood(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = 0
__lowerCamelCase = neighborhood[index_of_best_solution]
__lowerCamelCase = len(UpperCamelCase__ ) - 1
__lowerCamelCase = False
while not found:
__lowerCamelCase = 0
while i < len(UpperCamelCase__ ):
if best_solution[i] != solution[i]:
__lowerCamelCase = best_solution[i]
__lowerCamelCase = solution[i]
break
__lowerCamelCase = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
__lowerCamelCase = True
__lowerCamelCase = best_solution[:-1]
__lowerCamelCase = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
__lowerCamelCase = cost
__lowerCamelCase = solution
else:
__lowerCamelCase = index_of_best_solution + 1
__lowerCamelCase = neighborhood[index_of_best_solution]
if len(UpperCamelCase__ ) >= size:
tabu_list.pop(0 )
__lowerCamelCase = count + 1
return best_solution_ever, best_cost
def lowerCamelCase_ ( UpperCamelCase__ : Tuple=None ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = generate_neighbours(args.File )
__lowerCamelCase , __lowerCamelCase = generate_first_solution(
args.File , UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = tabu_search(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , args.Iterations , args.Size , )
print(F"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = MgpstrTokenizer
snake_case_ = False
snake_case_ = {}
snake_case_ = False
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
__lowerCamelCase = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
# fmt: on
__lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
__lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + '\n' )
def lowercase_ ( self , **lowerCamelCase__ ) -> str:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = 'tester'
__lowerCamelCase = 'tester'
return input_text, output_text
@unittest.skip('MGP-STR always lower cases letters.' )
def lowercase_ ( self ) -> int:
'''simple docstring'''
pass
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__lowerCamelCase = '[SPECIAL_TOKEN]'
tokenizer.add_special_tokens({'cls_token': special_token} )
__lowerCamelCase = tokenizer.encode([special_token] , add_special_tokens=lowerCamelCase__ )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
__lowerCamelCase = tokenizer.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
self.assertTrue(special_token not in decoded )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__lowerCamelCase , __lowerCamelCase = self.get_input_output_texts(lowerCamelCase__ )
__lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
__lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
__lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertNotEqual(len(lowerCamelCase__ ) , 0 )
__lowerCamelCase = tokenizer.decode(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
self.assertEqual(text_a.replace(' ' , '' ) , lowerCamelCase__ )
@unittest.skip('MGP-STR tokenizer only handles one sequence.' )
def lowercase_ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer' )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
| 348 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__A = logging.get_logger(__name__)
__A = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[List[float]] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
else:
return _interleave_iterable_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
else:
return _concatenate_iterable_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
| 348 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict=False ) -> List[Any]:
"""simple docstring"""
try:
__lowerCamelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__lowerCamelCase = default
else:
# KEY is set, convert it to True or False.
try:
__lowerCamelCase = strtobool(UpperCamelCase__ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
__A = parse_flag_from_env("RUN_SLOW", default=False)
__A = parse_flag_from_env("RUN_REMOTE", default=False)
__A = parse_flag_from_env("RUN_LOCAL", default=True)
__A = parse_flag_from_env("RUN_PACKAGED", default=True)
# Compression
__A = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
__A = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
__A = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")
# Audio
__A = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("soundfile") is None or version.parse(importlib_metadata.version("soundfile")) < version.parse("0.12.0"),
reason="test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; ",
)
# Beam
__A = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("0.3.2"),
reason="test requires apache-beam and a compatible dill version",
)
# Dill-cloudpickle compatibility
__A = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("0.3.2"),
reason="test requires dill>0.3.2 for cloudpickle compatibility",
)
# Windows
__A = pytest.mark.skipif(
sys.platform == "win32",
reason="test should not be run on Windows",
)
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> int:
"""simple docstring"""
try:
import faiss # noqa
except ImportError:
__lowerCamelCase = unittest.skip('test requires faiss' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> Union[str, Any]:
"""simple docstring"""
try:
import regex # noqa
except ImportError:
__lowerCamelCase = unittest.skip('test requires regex' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
try:
import elasticsearch # noqa
except ImportError:
__lowerCamelCase = unittest.skip('test requires elasticsearch' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
try:
import sqlalchemy # noqa
except ImportError:
__lowerCamelCase = unittest.skip('test requires sqlalchemy' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Optional[int]:
"""simple docstring"""
if not config.TORCH_AVAILABLE:
__lowerCamelCase = unittest.skip('test requires PyTorch' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not config.TF_AVAILABLE:
__lowerCamelCase = unittest.skip('test requires TensorFlow' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
if not config.JAX_AVAILABLE:
__lowerCamelCase = unittest.skip('test requires JAX' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Tuple:
"""simple docstring"""
if not config.PIL_AVAILABLE:
__lowerCamelCase = unittest.skip('test requires Pillow' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Dict:
"""simple docstring"""
try:
import transformers # noqa F401
except ImportError:
return unittest.skip('test requires transformers' )(UpperCamelCase__ )
else:
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Any:
"""simple docstring"""
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip('test requires tiktoken' )(UpperCamelCase__ )
else:
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
try:
import spacy # noqa F401
except ImportError:
return unittest.skip('test requires spacy' )(UpperCamelCase__ )
else:
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
def _require_spacy_model(UpperCamelCase__ : Optional[int] ):
try:
import spacy # noqa F401
spacy.load(UpperCamelCase__ )
except ImportError:
return unittest.skip('test requires spacy' )(UpperCamelCase__ )
except OSError:
return unittest.skip('test requires spacy model \'{}\''.format(UpperCamelCase__ ) )(UpperCamelCase__ )
else:
return test_case
return _require_spacy_model
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip('test requires pyspark' )(UpperCamelCase__ )
else:
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip('test requires joblibspark' )(UpperCamelCase__ )
else:
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if not _run_slow_tests or _run_slow_tests == 0:
__lowerCamelCase = unittest.skip('test is slow' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> Any:
"""simple docstring"""
if not _run_local_tests or _run_local_tests == 0:
__lowerCamelCase = unittest.skip('test is local' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> int:
"""simple docstring"""
if not _run_packaged_tests or _run_packaged_tests == 0:
__lowerCamelCase = unittest.skip('test is packaged' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if not _run_remote_tests or _run_remote_tests == 0:
__lowerCamelCase = unittest.skip('test requires remote' )(UpperCamelCase__ )
return test_case
def lowerCamelCase_ ( *UpperCamelCase__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
def decorate(cls : Any ):
for name, fn in cls.__dict__.items():
if callable(UpperCamelCase__ ) and name.startswith('test' ):
for decorator in decorators:
__lowerCamelCase = decorator(UpperCamelCase__ )
setattr(cls , UpperCamelCase__ , UpperCamelCase__ )
return cls
return decorate
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
pass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 0
snake_case_ = 1
snake_case_ = 2
@contextmanager
def lowerCamelCase_ ( UpperCamelCase__ : int=OfflineSimulationMode.CONNECTION_FAILS , UpperCamelCase__ : Optional[Any]=1E-16 ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = requests.Session().request
def timeout_request(UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] , **UpperCamelCase__ : Optional[int] ):
# Change the url to an invalid url so that the connection hangs
__lowerCamelCase = 'https://10.255.255.1'
if kwargs.get('timeout' ) is None:
raise RequestWouldHangIndefinitelyError(
F"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout.""" )
__lowerCamelCase = timeout
try:
return online_request(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
__lowerCamelCase = url
__lowerCamelCase = e.args[0]
__lowerCamelCase = (max_retry_error.args[0].replace('10.255.255.1' , F"""OfflineMock[{url}]""" ),)
__lowerCamelCase = (max_retry_error,)
raise
def raise_connection_error(UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , **UpperCamelCase__ : Optional[Any] ):
raise requests.ConnectionError('Offline mode is enabled.' , request=UpperCamelCase__ )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch('requests.Session.send' , UpperCamelCase__ ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch('requests.Session.request' , UpperCamelCase__ ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch('datasets.config.HF_DATASETS_OFFLINE' , UpperCamelCase__ ):
yield
else:
raise ValueError('Please use a value from the OfflineSimulationMode enum.' )
@contextmanager
def lowerCamelCase_ ( *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Tuple ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = str(Path().resolve() )
with tempfile.TemporaryDirectory(*UpperCamelCase__ , **UpperCamelCase__ ) as tmp_dir:
try:
os.chdir(UpperCamelCase__ )
yield
finally:
os.chdir(UpperCamelCase__ )
@contextmanager
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
import gc
gc.collect()
__lowerCamelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
import gc
gc.collect()
__lowerCamelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return deepcopy(UpperCamelCase__ ).integers(0 , 100 , 10 ).tolist() == deepcopy(UpperCamelCase__ ).integers(0 , 100 , 10 ).tolist()
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
import decorator
from requests.exceptions import HTTPError
def _wrapper(UpperCamelCase__ : List[Any] , *UpperCamelCase__ : Any , **UpperCamelCase__ : Dict ):
try:
return func(*UpperCamelCase__ , **UpperCamelCase__ )
except HTTPError as err:
if str(UpperCamelCase__ ).startswith('500' ) or str(UpperCamelCase__ ).startswith('502' ):
pytest.xfail(str(UpperCamelCase__ ) )
raise err
return decorator.decorator(_wrapper , UpperCamelCase__ )
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = returncode
__lowerCamelCase = stdout
__lowerCamelCase = stderr
async def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
while True:
__lowerCamelCase = await stream.readline()
if line:
callback(UpperCamelCase__ )
else:
break
async def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : Union[str, Any]=False , UpperCamelCase__ : List[Any]=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print('\nRunning: ' , ' '.join(UpperCamelCase__ ) )
__lowerCamelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=UpperCamelCase__ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=UpperCamelCase__ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__lowerCamelCase = []
__lowerCamelCase = []
def tee(UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Dict="" ):
__lowerCamelCase = line.decode('utf-8' ).rstrip()
sink.append(UpperCamelCase__ )
if not quiet:
print(UpperCamelCase__ , UpperCamelCase__ , file=UpperCamelCase__ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda UpperCamelCase__ : tee(UpperCamelCase__ , UpperCamelCase__ , sys.stdout , label='stdout:' ) ),
_read_stream(p.stderr , lambda UpperCamelCase__ : tee(UpperCamelCase__ , UpperCamelCase__ , sys.stderr , label='stderr:' ) ),
] , timeout=UpperCamelCase__ , )
return _RunOutput(await p.wait() , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : int=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Optional[Any]=180 , UpperCamelCase__ : str=False , UpperCamelCase__ : Dict=True ) -> _RunOutput:
"""simple docstring"""
__lowerCamelCase = asyncio.get_event_loop()
__lowerCamelCase = loop.run_until_complete(
_stream_subprocess(UpperCamelCase__ , env=UpperCamelCase__ , stdin=UpperCamelCase__ , timeout=UpperCamelCase__ , quiet=UpperCamelCase__ , echo=UpperCamelCase__ ) )
__lowerCamelCase = ' '.join(UpperCamelCase__ )
if result.returncode > 0:
__lowerCamelCase = '\n'.join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F"""'{cmd_str}' produced no output.""" )
return result
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = os.environ.get('PYTEST_XDIST_WORKER' , 'gw0' )
__lowerCamelCase = re.sub(R'^gw' , '' , UpperCamelCase__ , 0 , re.M )
return int(UpperCamelCase__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = 2_9500
__lowerCamelCase = pytest_xdist_worker_id()
return port + uniq_delta
| 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = ["model.decoder.embed_positions.weights"]
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if "emb" in name:
__lowerCamelCase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__lowerCamelCase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__lowerCamelCase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__lowerCamelCase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__lowerCamelCase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__lowerCamelCase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__lowerCamelCase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__lowerCamelCase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def lowerCamelCase_ ( UpperCamelCase__ : OrderedDict , UpperCamelCase__ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = rename_keys(UpperCamelCase__ )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
__lowerCamelCase = 1024
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 1536
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 2048
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=UpperCamelCase__ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCamelCase__ , num_attention_heads=UpperCamelCase__ , )
return config
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]="cpu" ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = MusicGen.get_pretrained(UpperCamelCase__ , device=UpperCamelCase__ )
__lowerCamelCase = decoder_config_from_checkpoint(UpperCamelCase__ )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase , __lowerCamelCase = rename_state_dict(
UpperCamelCase__ , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('t5-base' )
__lowerCamelCase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__lowerCamelCase = MusicgenForCausalLM(UpperCamelCase__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase , __lowerCamelCase = decoder.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ , audio_encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCamelCase__ )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('t5-base' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# set the appropriate bos/pad token ids
__lowerCamelCase = 2048
__lowerCamelCase = 2048
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCamelCase__ )
processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__A = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 348 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mvp'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=50_267 , lowerCamelCase__=1_024 , lowerCamelCase__=12 , lowerCamelCase__=4_096 , lowerCamelCase__=16 , lowerCamelCase__=12 , lowerCamelCase__=4_096 , lowerCamelCase__=16 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__="gelu" , lowerCamelCase__=1_024 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=0.0 , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=1 , lowerCamelCase__=0 , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=2 , lowerCamelCase__=2 , lowerCamelCase__=False , lowerCamelCase__=100 , lowerCamelCase__=800 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = d_model
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = classifier_dropout
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = use_prompt
__lowerCamelCase = prompt_length
__lowerCamelCase = prompt_mid_dim
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , forced_eos_token_id=lowerCamelCase__ , **lowerCamelCase__ , )
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' , lowerCamelCase__ ):
__lowerCamelCase = self.bos_token_id
warnings.warn(
f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
'The config can simply be saved and uploaded again to be fixed.' )
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
# TODO Update this
__A = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''esm'''
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_026 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-12 , lowerCamelCase__="absolute" , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , mask_token_id=lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = position_embedding_type
__lowerCamelCase = use_cache
__lowerCamelCase = emb_layer_norm_before
__lowerCamelCase = token_dropout
__lowerCamelCase = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('No esmfold_config supplied for folding model, using default values.' )
__lowerCamelCase = EsmFoldConfig()
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = EsmFoldConfig(**lowerCamelCase__ )
__lowerCamelCase = esmfold_config
if vocab_list is None:
logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!' )
__lowerCamelCase = get_default_vocab_list()
else:
__lowerCamelCase = vocab_list
else:
__lowerCamelCase = None
__lowerCamelCase = None
if self.esmfold_config is not None and getattr(self.esmfold_config , 'use_esm_attn_map' , lowerCamelCase__ ):
raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = super().to_dict()
if isinstance(self.esmfold_config , lowerCamelCase__ ):
__lowerCamelCase = self.esmfold_config.to_dict()
return output
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = None
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = 0
snake_case_ = True
snake_case_ = False
snake_case_ = 1_28
snake_case_ = None
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if self.trunk is None:
__lowerCamelCase = TrunkConfig()
elif isinstance(self.trunk , lowerCamelCase__ ):
__lowerCamelCase = TrunkConfig(**self.trunk )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = asdict(self )
__lowerCamelCase = self.trunk.to_dict()
return output
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = 48
snake_case_ = 10_24
snake_case_ = 1_28
snake_case_ = 32
snake_case_ = 32
snake_case_ = 32
snake_case_ = 0
snake_case_ = 0
snake_case_ = False
snake_case_ = 4
snake_case_ = 1_28
snake_case_ = None
def lowercase_ ( self ) -> int:
'''simple docstring'''
if self.structure_module is None:
__lowerCamelCase = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCamelCase__ ):
__lowerCamelCase = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
f""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
__lowerCamelCase = self.sequence_state_dim // self.sequence_head_width
__lowerCamelCase = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = asdict(self )
__lowerCamelCase = self.structure_module.to_dict()
return output
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = 3_84
snake_case_ = 1_28
snake_case_ = 16
snake_case_ = 1_28
snake_case_ = 12
snake_case_ = 4
snake_case_ = 8
snake_case_ = 0.1
snake_case_ = 8
snake_case_ = 1
snake_case_ = 2
snake_case_ = 7
snake_case_ = 10
snake_case_ = 1E-8
snake_case_ = 1E5
def lowercase_ ( self ) -> str:
'''simple docstring'''
return asdict(self )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__A = logging.get_logger("transformers.models.speecht5")
__A = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__A = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = []
__A = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
elif weight_type == "running_mean":
__lowerCamelCase = value
elif weight_type == "running_var":
__lowerCamelCase = value
elif weight_type == "num_batches_tracked":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
if task == "s2t":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2T
__lowerCamelCase = IGNORE_KEYS_S2T
elif task == "t2s":
__lowerCamelCase = None
__lowerCamelCase = MAPPING_T2S
__lowerCamelCase = IGNORE_KEYS_T2S
elif task == "s2s":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2S
__lowerCamelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(UpperCamelCase__ , UpperCamelCase__ ):
logger.info(F"""{name} was ignored""" )
continue
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
__lowerCamelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCamelCase = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCamelCase = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase = 'weight_v'
elif "bias" in name:
__lowerCamelCase = 'bias'
elif "weight" in name:
__lowerCamelCase = 'weight'
elif "running_mean" in name:
__lowerCamelCase = 'running_mean'
elif "running_var" in name:
__lowerCamelCase = 'running_var'
elif "num_batches_tracked" in name:
__lowerCamelCase = 'num_batches_tracked'
else:
__lowerCamelCase = None
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
continue
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = full_name.split('conv_layers.' )[-1]
__lowerCamelCase = name.split('.' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCamelCase__ )
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : str=None , UpperCamelCase__ : List[str]=None , ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__lowerCamelCase = SpeechTaConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCamelCase = SpeechTaConfig()
if task == "s2t":
__lowerCamelCase = config.max_text_positions
__lowerCamelCase = SpeechTaForSpeechToText(UpperCamelCase__ )
elif task == "t2s":
__lowerCamelCase = 1876
__lowerCamelCase = 600
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForTextToSpeech(UpperCamelCase__ )
elif task == "s2s":
__lowerCamelCase = 1876
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForSpeechToSpeech(UpperCamelCase__ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
__lowerCamelCase = SpeechTaTokenizer(UpperCamelCase__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken('<mask>' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
__lowerCamelCase = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
__lowerCamelCase = SpeechTaFeatureExtractor()
__lowerCamelCase = SpeechTaProcessor(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = torch.load(UpperCamelCase__ )
recursively_load_weights(fairseq_checkpoint['model'] , UpperCamelCase__ , UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if repo_id:
print('Pushing to the hub...' )
processor.push_to_hub(UpperCamelCase__ )
model.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__A = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 348 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
__lowerCamelCase = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> Dict:
"""simple docstring"""
__lowerCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
__lowerCamelCase = tf.cast(math.pi , x.dtype )
__lowerCamelCase = tf.cast(0.04_47_15 , x.dtype )
__lowerCamelCase = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(UpperCamelCase__ , 3 )) ))
return x * cdf
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
return x * tf.tanh(tf.math.softplus(UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
__lowerCamelCase = tf.cast(0.04_47_15 , x.dtype )
__lowerCamelCase = tf.cast(0.79_78_84_56_08 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
__lowerCamelCase = tf.cast(1.7_02 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return tf.clip_by_value(_gelu(UpperCamelCase__ ) , -10 , 10 )
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any]=-1 ) -> Tuple:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = tf.split(UpperCamelCase__ , 2 , axis=UpperCamelCase__ )
return a * tf.math.sigmoid(UpperCamelCase__ )
if version.parse(tf.version.VERSION) >= version.parse("2.4"):
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return tf.keras.activations.gelu(UpperCamelCase__ , approximate=UpperCamelCase__ )
__A = tf.keras.activations.gelu
__A = approximate_gelu_wrap
else:
__A = _gelu
__A = _gelu_new
__A = {
"gelu": gelu,
"gelu_10": gelu_aa,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": tf.keras.activations.relu,
"sigmoid": tf.keras.activations.sigmoid,
"silu": tf.keras.activations.swish,
"swish": tf.keras.activations.swish,
"tanh": tf.keras.activations.tanh,
}
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ) -> Any:
"""simple docstring"""
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )
| 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
__lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
__A = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
__A = {
"ctrl": 2_56,
}
__A = {
"Pregnancy": 16_86_29,
"Christianity": 76_75,
"Explain": 10_64_23,
"Fitness": 6_34_40,
"Saving": 6_31_63,
"Ask": 2_71_71,
"Ass": 9_59_85,
"Joke": 16_35_09,
"Questions": 4_56_22,
"Thoughts": 4_96_05,
"Retail": 5_23_42,
"Feminism": 16_43_38,
"Writing": 1_19_92,
"Atheism": 19_22_63,
"Netflix": 4_86_16,
"Computing": 3_96_39,
"Opinion": 4_32_13,
"Alone": 4_49_67,
"Funny": 5_89_17,
"Gaming": 4_03_58,
"Human": 40_88,
"India": 13_31,
"Joker": 7_71_38,
"Diet": 3_62_06,
"Legal": 1_18_59,
"Norman": 49_39,
"Tip": 7_26_89,
"Weight": 5_23_43,
"Movies": 4_62_73,
"Running": 2_34_25,
"Science": 20_90,
"Horror": 3_77_93,
"Confession": 6_05_72,
"Finance": 1_22_50,
"Politics": 1_63_60,
"Scary": 19_19_85,
"Support": 1_26_54,
"Technologies": 3_25_16,
"Teenage": 6_61_60,
"Event": 3_27_69,
"Learned": 6_74_60,
"Notion": 18_27_70,
"Wikipedia": 3_75_83,
"Books": 66_65,
"Extract": 7_60_50,
"Confessions": 10_27_01,
"Conspiracy": 7_59_32,
"Links": 6_36_74,
"Narcissus": 15_04_25,
"Relationship": 5_47_66,
"Relationships": 13_47_96,
"Reviews": 4_16_71,
"News": 42_56,
"Translation": 2_68_20,
"multilingual": 12_84_06,
}
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> Any:
"""simple docstring"""
__lowerCamelCase = set()
__lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowerCamelCase = char
__lowerCamelCase = set(UpperCamelCase__ )
return pairs
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = CONTROL_CODES
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="<unk>" , **lowerCamelCase__ ) -> str:
'''simple docstring'''
super().__init__(unk_token=lowerCamelCase__ , **lowerCamelCase__ )
with open(lowerCamelCase__ , encoding='utf-8' ) as vocab_handle:
__lowerCamelCase = json.load(lowerCamelCase__ )
__lowerCamelCase = {v: k for k, v in self.encoder.items()}
with open(lowerCamelCase__ , encoding='utf-8' ) as merges_handle:
__lowerCamelCase = merges_handle.read().split('\n' )[1:-1]
__lowerCamelCase = [tuple(merge.split() ) for merge in merges]
__lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
__lowerCamelCase = {}
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
return len(self.encoder )
def lowercase_ ( self ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowerCamelCase = tuple(lowerCamelCase__ )
__lowerCamelCase = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
__lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
__lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__lowerCamelCase , __lowerCamelCase = bigram
__lowerCamelCase = []
__lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
__lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowerCamelCase = tuple(lowerCamelCase__ )
__lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
__lowerCamelCase = get_pairs(lowerCamelCase__ )
__lowerCamelCase = '@@ '.join(lowerCamelCase__ )
__lowerCamelCase = word[:-4]
__lowerCamelCase = word
return word
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
__lowerCamelCase = []
__lowerCamelCase = re.findall(R'\S+\n?' , lowerCamelCase__ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCamelCase__ ).split(' ' ) ) )
return split_tokens
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
return self.decoder.get(lowerCamelCase__ , self.unk_token )
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = ' '.join(lowerCamelCase__ ).replace('@@ ' , '' ).strip()
return out_string
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(lowerCamelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '\n' )
__lowerCamelCase = 0
with open(lowerCamelCase__ , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
' Please check that the tokenizer is not corrupted!' )
__lowerCamelCase = token_index
writer.write(' '.join(lowerCamelCase__ ) + '\n' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
__lowerCamelCase = [5, 5, 5, 5]
elif "fl4" in model_name:
__lowerCamelCase = [4, 4, 4, 4]
__lowerCamelCase = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
if "lrf" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
else:
__lowerCamelCase = [2, 2, 2, 2]
if "tiny" in model_name:
__lowerCamelCase = 96
elif "small" in model_name:
__lowerCamelCase = 96
elif "base" in model_name:
__lowerCamelCase = 128
elif "large" in model_name:
__lowerCamelCase = 192
elif "xlarge" in model_name:
__lowerCamelCase = 256
elif "huge" in model_name:
__lowerCamelCase = 352
# set label information
__lowerCamelCase = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
__lowerCamelCase = 'imagenet-22k-id2label.json'
else:
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = FocalNetConfig(
embed_dim=UpperCamelCase__ , depths=UpperCamelCase__ , focal_levels=UpperCamelCase__ , focal_windows=UpperCamelCase__ , use_conv_embed=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , use_post_layernorm=UpperCamelCase__ , use_layerscale=UpperCamelCase__ , )
return config
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
if "patch_embed.proj" in name:
__lowerCamelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__lowerCamelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
__lowerCamelCase = 'encoder.' + name
if "encoder.layers" in name:
__lowerCamelCase = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
__lowerCamelCase = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
__lowerCamelCase = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__lowerCamelCase = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__lowerCamelCase = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__lowerCamelCase = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
__lowerCamelCase = 'layernorm.weight'
if name == "norm.bias":
__lowerCamelCase = 'layernorm.bias'
if "head" in name:
__lowerCamelCase = name.replace('head' , 'classifier' )
else:
__lowerCamelCase = 'focalnet.' + name
return name
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple=False ) -> Dict:
"""simple docstring"""
__lowerCamelCase = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
__lowerCamelCase = model_name_to_url[model_name]
print('Checkpoint URL: ' , UpperCamelCase__ )
__lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = val
__lowerCamelCase = get_focalnet_config(UpperCamelCase__ )
__lowerCamelCase = FocalNetForImageClassification(UpperCamelCase__ )
model.eval()
# load state dict
model.load_state_dict(UpperCamelCase__ )
# verify conversion
__lowerCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase = BitImageProcessor(
do_resize=UpperCamelCase__ , size={'shortest_edge': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCamelCase__ , crop_size=224 , do_normalize=UpperCamelCase__ , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ , )
__lowerCamelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
__lowerCamelCase = processor(images=UpperCamelCase__ , return_tensors='pt' )
__lowerCamelCase = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ),
] )
__lowerCamelCase = image_transforms(UpperCamelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , UpperCamelCase__ , atol=1E-4 )
__lowerCamelCase = model(**UpperCamelCase__ )
__lowerCamelCase = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__lowerCamelCase = torch.tensor([0.21_66, -0.43_68, 0.21_91] )
elif model_name == "focalnet-tiny-lrf":
__lowerCamelCase = torch.tensor([1.16_69, 0.01_25, -0.16_95] )
elif model_name == "focalnet-small":
__lowerCamelCase = torch.tensor([0.49_17, -0.04_30, 0.13_41] )
elif model_name == "focalnet-small-lrf":
__lowerCamelCase = torch.tensor([-0.25_88, -0.53_42, -0.23_31] )
elif model_name == "focalnet-base":
__lowerCamelCase = torch.tensor([-0.16_55, -0.40_90, -0.17_30] )
elif model_name == "focalnet-base-lrf":
__lowerCamelCase = torch.tensor([0.53_06, -0.04_83, -0.39_28] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(F"""{model_name}""" )
processor.push_to_hub(F"""{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
__A = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : bool = False ) -> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
__lowerCamelCase = [
2047,
137_3653,
2532_6001,
32_1503_1751,
2_1523_0289_8747,
3_4747_4966_0383,
341_5500_7172_8321,
1,
382_5123_0565_4641_3051,
1,
1,
3186_6585_7834_0311_5116_7461,
3_3170_4406_4679_8873_8596_1981,
]
__lowerCamelCase = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(UpperCamelCase__ , 1 ):
if n < _p:
# then we have our last prime to check
__lowerCamelCase = primes[:idx]
break
__lowerCamelCase , __lowerCamelCase = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
__lowerCamelCase = False
for r in range(UpperCamelCase__ ):
__lowerCamelCase = pow(UpperCamelCase__ , d * 2**r , UpperCamelCase__ )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
__lowerCamelCase = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(83_8201 )
assert miller_rabin(83_8207 )
# 1_373_653
assert not miller_rabin(1731_6001 )
assert miller_rabin(1731_6017 )
# 25_326_001
assert not miller_rabin(30_7838_6641 )
assert miller_rabin(30_7838_6653 )
# 3_215_031_751
assert not miller_rabin(1_7130_4557_4801 )
assert miller_rabin(1_7130_4557_4819 )
# 2_152_302_898_747
assert not miller_rabin(2_7797_9972_8307 )
assert miller_rabin(2_7797_9972_8327 )
# 3_474_749_660_383
assert not miller_rabin(113_8500_2390_9441 )
assert miller_rabin(113_8500_2390_9527 )
# 341_550_071_728_321
assert not miller_rabin(127_5041_0188_4880_4351 )
assert miller_rabin(127_5041_0188_4880_4391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(796_6646_4458_5077_8779_1867 )
assert miller_rabin(796_6646_4458_5077_8779_1951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5528_4067_7446_6478_9766_0333 )
assert miller_rabin(5528_4067_7446_6478_9766_0359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __lowerCamelCase = divmod(len(UpperCamelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = [float(x) for x in input("Enter the elements of first array: ").split()]
__A = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> tuple[int, int]:
"""simple docstring"""
if b == 0:
return (1, 0)
((__lowerCamelCase) , (__lowerCamelCase)) = extended_euclid(UpperCamelCase__ , a % b )
__lowerCamelCase = a // b
return (y, x - k * y)
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
((__lowerCamelCase) , (__lowerCamelCase)) = extended_euclid(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = na * na
__lowerCamelCase = ra * x * na + ra * y * na
return (n % m + m) % m
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
((__lowerCamelCase) , (__lowerCamelCase)) = extended_euclid(UpperCamelCase__ , UpperCamelCase__ )
if b < 0:
__lowerCamelCase = (b % n + n) % n
return b
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = invert_modulo(UpperCamelCase__ , UpperCamelCase__ ), invert_modulo(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = na * na
__lowerCamelCase = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
__A = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
__A = {
"facebook/bart-base": 10_24,
"facebook/bart-large": 10_24,
"facebook/bart-large-mnli": 10_24,
"facebook/bart-large-cnn": 10_24,
"facebook/bart-large-xsum": 10_24,
"yjernite/bart_eli5": 10_24,
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ['''input_ids''', '''attention_mask''']
snake_case_ = BartTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , lowerCamelCase__=True , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(
lowerCamelCase__ , lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , trim_offsets=lowerCamelCase__ , **lowerCamelCase__ , )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowerCamelCase__ ) != add_prefix_space:
__lowerCamelCase = getattr(lowerCamelCase__ , pre_tok_state.pop('type' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**lowerCamelCase__ )
__lowerCamelCase = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__lowerCamelCase = 'post_processor'
__lowerCamelCase = getattr(self.backend_tokenizer , lowerCamelCase__ , lowerCamelCase__ )
if tokenizer_component_instance:
__lowerCamelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__lowerCamelCase = tuple(state['sep'] )
if "cls" in state:
__lowerCamelCase = tuple(state['cls'] )
__lowerCamelCase = False
if state.get('add_prefix_space' , lowerCamelCase__ ) != add_prefix_space:
__lowerCamelCase = add_prefix_space
__lowerCamelCase = True
if state.get('trim_offsets' , lowerCamelCase__ ) != trim_offsets:
__lowerCamelCase = trim_offsets
__lowerCamelCase = True
if changes_to_apply:
__lowerCamelCase = getattr(lowerCamelCase__ , state.pop('type' ) )
__lowerCamelCase = component_class(**lowerCamelCase__ )
setattr(self.backend_tokenizer , lowerCamelCase__ , lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase_ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else value
__lowerCamelCase = value
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> BatchEncoding:
'''simple docstring'''
__lowerCamelCase = kwargs.get('is_split_into_words' , lowerCamelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._batch_encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> BatchEncoding:
'''simple docstring'''
__lowerCamelCase = kwargs.get('is_split_into_words' , lowerCamelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._encode_plus(*lowerCamelCase__ , **lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__=None ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''philschmid/bart-large-cnn-samsum'''
snake_case_ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
snake_case_ = '''summarizer'''
snake_case_ = AutoTokenizer
snake_case_ = AutoModelForSeqaSeqLM
snake_case_ = ['''text''']
snake_case_ = ['''text''']
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
return self.model.generate(**lowerCamelCase__ )[0]
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
| 348 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = BlenderbotConfig
snake_case_ = {}
snake_case_ = '''gelu'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=2 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=20 , lowerCamelCase__=2 , lowerCamelCase__=1 , lowerCamelCase__=0 , ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = eos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = bos_token_id
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__lowerCamelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__lowerCamelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__lowerCamelCase = prepare_blenderbot_inputs_dict(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = TFBlenderbotModel(config=lowerCamelCase__ ).get_decoder()
__lowerCamelCase = inputs_dict['input_ids']
__lowerCamelCase = input_ids[:1, :]
__lowerCamelCase = inputs_dict['attention_mask'][:1, :]
__lowerCamelCase = inputs_dict['head_mask']
__lowerCamelCase = 1
# first forward pass
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , head_mask=lowerCamelCase__ , use_cache=lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowerCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCamelCase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__lowerCamelCase = tf.concat([input_ids, next_tokens] , axis=-1 )
__lowerCamelCase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )[0]
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__lowerCamelCase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__lowerCamelCase = output_from_no_past[:, -3:, random_slice_idx]
__lowerCamelCase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase__ , lowerCamelCase__ , rtol=1e-3 )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any=None , UpperCamelCase__ : int=None , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : int=None , ) -> List[str]:
"""simple docstring"""
if attention_mask is None:
__lowerCamelCase = tf.cast(tf.math.not_equal(UpperCamelCase__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__lowerCamelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__lowerCamelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowerCamelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowerCamelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
snake_case_ = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
snake_case_ = (
{
'''conversational''': TFBlenderbotForConditionalGeneration,
'''feature-extraction''': TFBlenderbotModel,
'''summarization''': TFBlenderbotForConditionalGeneration,
'''text2text-generation''': TFBlenderbotForConditionalGeneration,
'''translation''': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
snake_case_ = True
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = TFBlenderbotModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__ )
@require_tokenizers
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = ['''My friends are cool but they eat too many carbs.''']
snake_case_ = '''facebook/blenderbot-400M-distill'''
@cached_property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.tokenizer(self.src_text , return_tensors='tf' )
__lowerCamelCase = self.model.generate(
model_inputs.input_ids , )
__lowerCamelCase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowerCamelCase__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_choices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = True
snake_case_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowerCamelCase__ )
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 348 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__A = TypeVar("T")
__A = Union[List[T], Tuple[T, ...]]
__A = Union[T, List[T], Dict[str, T]]
__A = Union[str, bytes, os.PathLike]
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
__A = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
__A = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> Dict:
"""simple docstring"""
def remove_articles(UpperCamelCase__ : Any ):
__lowerCamelCase = re.compile(R'\b(a|an|the)\b' , re.UNICODE )
return re.sub(UpperCamelCase__ , ' ' , UpperCamelCase__ )
def white_space_fix(UpperCamelCase__ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase__ : List[Any] ):
__lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase__ : Tuple ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
return int(normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str ) -> int:
"""simple docstring"""
__lowerCamelCase = [any(compute_exact(UpperCamelCase__ , UpperCamelCase__ ) for ref in refs ) for pred, refs in zip(UpperCamelCase__ , UpperCamelCase__ )]
return (sum(UpperCamelCase__ ) / len(UpperCamelCase__ )) * 100
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
__lowerCamelCase = Counter(UpperCamelCase__ )
__lowerCamelCase = Counter(UpperCamelCase__ )
__lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
__lowerCamelCase = scount * numref
__lowerCamelCase = Counter(UpperCamelCase__ )
__lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
__lowerCamelCase = ccount * numref
# KEEP
__lowerCamelCase = sgramcounter_rep & cgramcounter_rep
__lowerCamelCase = keepgramcounter_rep & rgramcounter
__lowerCamelCase = sgramcounter_rep & rgramcounter
__lowerCamelCase = 0
__lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__lowerCamelCase = 1
__lowerCamelCase = 1
if len(UpperCamelCase__ ) > 0:
__lowerCamelCase = keeptmpscorea / len(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
__lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
__lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
__lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
__lowerCamelCase = sgramcounter_rep - cgramcounter_rep
__lowerCamelCase = delgramcounter_rep - rgramcounter
__lowerCamelCase = sgramcounter_rep - rgramcounter
__lowerCamelCase = 0
__lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__lowerCamelCase = 1
if len(UpperCamelCase__ ) > 0:
__lowerCamelCase = deltmpscorea / len(UpperCamelCase__ )
# ADDITION
__lowerCamelCase = set(UpperCamelCase__ ) - set(UpperCamelCase__ )
__lowerCamelCase = set(UpperCamelCase__ ) & set(UpperCamelCase__ )
__lowerCamelCase = set(UpperCamelCase__ ) - set(UpperCamelCase__ )
__lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__lowerCamelCase = 1
__lowerCamelCase = 1
if len(UpperCamelCase__ ) > 0:
__lowerCamelCase = addtmpscore / len(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
__lowerCamelCase = addtmpscore / len(UpperCamelCase__ )
__lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
__lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = len(UpperCamelCase__ )
__lowerCamelCase = ssent.split(' ' )
__lowerCamelCase = csent.split(' ' )
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
for rsent in rsents:
__lowerCamelCase = rsent.split(' ' )
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = []
ragramslist.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
__lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1]
ragrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
__lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
ragrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
__lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
ragrams.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
__lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1]
sagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
__lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
sagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
__lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
sagrams.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
__lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1]
cagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
__lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
cagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
__lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
cagrams.append(UpperCamelCase__ )
((__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase)) = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase)) = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase)) = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((__lowerCamelCase) , (__lowerCamelCase) , (__lowerCamelCase)) = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
__lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
__lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
__lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : bool = True , UpperCamelCase__ : str = "13a" , UpperCamelCase__ : bool = True ) -> List[str]:
"""simple docstring"""
if lowercase:
__lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
__lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__ )()(UpperCamelCase__ )
else:
__lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__ )
elif tokenizer == "moses":
__lowerCamelCase = sacremoses.MosesTokenizer().tokenize(UpperCamelCase__ , return_str=UpperCamelCase__ , escape=UpperCamelCase__ )
elif tokenizer == "penn":
__lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__ , return_str=UpperCamelCase__ )
else:
__lowerCamelCase = sentence
if not return_str:
__lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any] ) -> int:
"""simple docstring"""
if not (len(UpperCamelCase__ ) == len(UpperCamelCase__ ) == len(UpperCamelCase__ )):
raise ValueError('Sources length must match predictions and references lengths.' )
__lowerCamelCase = 0
for src, pred, refs in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
sari_score += SARIsent(normalize(UpperCamelCase__ ) , normalize(UpperCamelCase__ ) , [normalize(UpperCamelCase__ ) for sent in refs] )
__lowerCamelCase = sari_score / len(UpperCamelCase__ )
return 100 * sari_score
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str]="exp" , UpperCamelCase__ : int=None , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : Any=False , UpperCamelCase__ : Union[str, Any]=False , ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = len(references[0] )
if any(len(UpperCamelCase__ ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
__lowerCamelCase = [[refs[i] for refs in references] for i in range(UpperCamelCase__ )]
__lowerCamelCase = sacrebleu.corpus_bleu(
UpperCamelCase__ , UpperCamelCase__ , smooth_method=UpperCamelCase__ , smooth_value=UpperCamelCase__ , force=UpperCamelCase__ , lowercase=UpperCamelCase__ , use_effective_order=UpperCamelCase__ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=[
'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
'https://github.com/cocoxu/simplification/blob/master/SARI.py',
'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
'https://github.com/mjpost/sacreBLEU',
] , reference_urls=[
'https://www.aclweb.org/anthology/Q16-1029.pdf',
'https://github.com/mjpost/sacreBLEU',
'https://en.wikipedia.org/wiki/BLEU',
'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
] , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = {}
result.update({'sari': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'sacrebleu': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'exact': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''autoformer'''
snake_case_ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "student_t" , lowerCamelCase__ = "nll" , lowerCamelCase__ = 1 , lowerCamelCase__ = [1, 2, 3, 4, 5, 6, 7] , lowerCamelCase__ = True , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = 64 , lowerCamelCase__ = 2 , lowerCamelCase__ = 2 , lowerCamelCase__ = 2 , lowerCamelCase__ = 2 , lowerCamelCase__ = 32 , lowerCamelCase__ = 32 , lowerCamelCase__ = "gelu" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = True , lowerCamelCase__=True , lowerCamelCase__ = 10 , lowerCamelCase__ = 25 , lowerCamelCase__ = 3 , **lowerCamelCase__ , ) -> List[Any]:
'''simple docstring'''
# time series specific configuration
__lowerCamelCase = prediction_length
__lowerCamelCase = context_length if context_length is not None else prediction_length
__lowerCamelCase = distribution_output
__lowerCamelCase = loss
__lowerCamelCase = input_size
__lowerCamelCase = num_time_features
__lowerCamelCase = lags_sequence
__lowerCamelCase = scaling
__lowerCamelCase = num_dynamic_real_features
__lowerCamelCase = num_static_real_features
__lowerCamelCase = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(lowerCamelCase__ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
__lowerCamelCase = cardinality
else:
__lowerCamelCase = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(lowerCamelCase__ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
__lowerCamelCase = embedding_dimension
else:
__lowerCamelCase = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
__lowerCamelCase = num_parallel_samples
# Transformer architecture configuration
__lowerCamelCase = input_size * len(self.lags_sequence ) + self._number_of_features
__lowerCamelCase = d_model
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = use_cache
# Autoformer
__lowerCamelCase = label_length
__lowerCamelCase = moving_average
__lowerCamelCase = autocorrelation_factor
super().__init__(is_encoder_decoder=lowerCamelCase__ , **lowerCamelCase__ )
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__A = logging.get_logger(__name__)
__A = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''whisper'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=51_865 , lowerCamelCase__=80 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=1_536 , lowerCamelCase__=1_536 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=50_257 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="gelu" , lowerCamelCase__=256 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=False , lowerCamelCase__=1_500 , lowerCamelCase__=448 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=None , lowerCamelCase__=[220, 50_256] , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=False , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__=7 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = num_mel_bins
__lowerCamelCase = d_model
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = max_source_positions
__lowerCamelCase = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
__lowerCamelCase = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
__lowerCamelCase = median_filter_width
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , suppress_tokens=lowerCamelCase__ , begin_suppress_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__lowerCamelCase = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
__lowerCamelCase = {0: 'batch'}
else:
__lowerCamelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ , direction='inputs' )
return common_inputs
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = -1 , lowerCamelCase__ = -1 , lowerCamelCase__ = False , lowerCamelCase__ = None , lowerCamelCase__ = 22_050 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
__lowerCamelCase = OrderedDict()
__lowerCamelCase = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowerCamelCase__ , framework=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , time_duration=lowerCamelCase__ , frequency=lowerCamelCase__ , )
__lowerCamelCase = encoder_inputs['input_features'].shape[2]
__lowerCamelCase = encoder_sequence_length // 2 if self.use_past else seq_length
__lowerCamelCase = super().generate_dummy_inputs(
preprocessor.tokenizer , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = encoder_inputs.pop('input_features' )
__lowerCamelCase = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
__lowerCamelCase = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-3
| 348 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__A = logging.getLogger(__name__)
__A = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = field(
default=__magic_name__ , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__magic_name__ )} , )
snake_case_ = field(
default=__magic_name__ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
snake_case_ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
snake_case_ = field(
default=__magic_name__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
'--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
snake_case_ = field(default=__magic_name__ , metadata={'''help''': '''The input training data file (a text file).'''} )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
snake_case_ = field(
default=5 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
snake_case_ = field(
default=__magic_name__ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated. Default to the max input length of the model.'''
)
} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
snake_case_ = field(
default=0.1_5 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
snake_case_ = field(
default=__magic_name__ , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
if self.train_file is not None:
__lowerCamelCase = self.train_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
__lowerCamelCase = self.validation_file.split('.' )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
with open(UpperCamelCase__ , 'r' , encoding='utf-8' ) as f:
__lowerCamelCase = [json.loads(UpperCamelCase__ ) for line in f.read().splitlines() if (len(UpperCamelCase__ ) > 0 and not line.isspace())]
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
__lowerCamelCase = {c: dataset[c] for c in dataset.column_names}
__lowerCamelCase = refs
return Dataset.from_dict(UpperCamelCase__ )
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowerCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , UpperCamelCase__ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCamelCase = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
__lowerCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[:{data_args.validation_split_percentage}%]""" , )
__lowerCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[{data_args.validation_split_percentage}%:]""" , )
else:
__lowerCamelCase = {}
if data_args.train_file is not None:
__lowerCamelCase = data_args.train_file
if data_args.validation_file is not None:
__lowerCamelCase = data_args.validation_file
__lowerCamelCase = data_args.train_file.split('.' )[-1]
if extension == "txt":
__lowerCamelCase = 'text'
__lowerCamelCase = load_dataset(UpperCamelCase__ , data_files=UpperCamelCase__ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCamelCase = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
__lowerCamelCase = AutoConfig.from_pretrained(model_args.config_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
__lowerCamelCase = AutoConfig.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
__lowerCamelCase = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
__lowerCamelCase = {
'cache_dir': model_args.cache_dir,
'use_fast': model_args.use_fast_tokenizer,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
__lowerCamelCase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
__lowerCamelCase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.' )
if model_args.model_name_or_path:
__lowerCamelCase = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
__lowerCamelCase = AutoModelForMaskedLM.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
__lowerCamelCase = datasets['train'].column_names
else:
__lowerCamelCase = datasets['validation'].column_names
__lowerCamelCase = 'text' if 'text' in column_names else column_names[0]
__lowerCamelCase = 'max_length' if data_args.pad_to_max_length else False
def tokenize_function(UpperCamelCase__ : Tuple ):
# Remove empty lines
__lowerCamelCase = [line for line in examples['text'] if len(UpperCamelCase__ ) > 0 and not line.isspace()]
return tokenizer(examples['text'] , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=data_args.max_seq_length )
__lowerCamelCase = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
__lowerCamelCase = add_chinese_references(tokenized_datasets['train'] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
__lowerCamelCase = add_chinese_references(
tokenized_datasets['validation'] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
__lowerCamelCase = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
__lowerCamelCase = False
# Data collator
# This one will take care of randomly masking the tokens.
__lowerCamelCase = DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowerCamelCase = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=tokenized_datasets['train'] if training_args.do_train else None , eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowerCamelCase = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
__lowerCamelCase = model_args.model_name_or_path
else:
__lowerCamelCase = None
__lowerCamelCase = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
__lowerCamelCase = os.path.join(training_args.output_dir , 'train_results.txt' )
if trainer.is_world_process_zero():
with open(UpperCamelCase__ , 'w' ) as writer:
logger.info('***** Train results *****' )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , 'trainer_state.json' ) )
# Evaluation
__lowerCamelCase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__lowerCamelCase = trainer.evaluate()
__lowerCamelCase = math.exp(eval_output['eval_loss'] )
__lowerCamelCase = perplexity
__lowerCamelCase = os.path.join(training_args.output_dir , 'eval_results_mlm_wwm.txt' )
if trainer.is_world_process_zero():
with open(UpperCamelCase__ , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in sorted(results.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
return results
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> str:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=14 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=0.02 , ) -> int:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = rotary_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = None
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=lowerCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , attention_mask=lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
snake_case_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = FlaxGPTJModelTester(self )
def lowercase_ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@tooslow
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
__lowerCamelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )
__lowerCamelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = False
__lowerCamelCase = model.config.eos_token_id
__lowerCamelCase = jax.jit(model.generate )
__lowerCamelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
__lowerCamelCase = tokenizer.batch_decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
__lowerCamelCase = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCamelCase__ )
__lowerCamelCase = fx_state
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = model_class.from_pretrained(lowerCamelCase__ , from_pt=lowerCamelCase__ )
__lowerCamelCase = fx_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = load_flax_weights_in_pytorch_model(lowerCamelCase__ , fx_model.params )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = pt_model_class.from_pretrained(lowerCamelCase__ , from_flax=lowerCamelCase__ )
with torch.no_grad():
__lowerCamelCase = pt_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 348 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = ['''input_features''', '''is_longer''']
def __init__( self , lowerCamelCase__=64 , lowerCamelCase__=48_000 , lowerCamelCase__=480 , lowerCamelCase__=10 , lowerCamelCase__=1_024 , lowerCamelCase__=0.0 , lowerCamelCase__=False , lowerCamelCase__ = 0 , lowerCamelCase__ = 14_000 , lowerCamelCase__ = None , lowerCamelCase__ = "fusion" , lowerCamelCase__ = "repeatpad" , **lowerCamelCase__ , ) -> Dict:
'''simple docstring'''
super().__init__(
feature_size=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , padding_value=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , **lowerCamelCase__ , )
__lowerCamelCase = top_db
__lowerCamelCase = truncation
__lowerCamelCase = padding
__lowerCamelCase = fft_window_size
__lowerCamelCase = (fft_window_size >> 1) + 1
__lowerCamelCase = hop_length
__lowerCamelCase = max_length_s
__lowerCamelCase = max_length_s * sampling_rate
__lowerCamelCase = sampling_rate
__lowerCamelCase = frequency_min
__lowerCamelCase = frequency_max
__lowerCamelCase = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=lowerCamelCase__ , min_frequency=lowerCamelCase__ , max_frequency=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , norm=lowerCamelCase__ , mel_scale='htk' , )
__lowerCamelCase = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=lowerCamelCase__ , min_frequency=lowerCamelCase__ , max_frequency=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , norm='slaney' , mel_scale='slaney' , )
def lowercase_ ( self ) -> Dict[str, Any]:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> np.ndarray:
'''simple docstring'''
__lowerCamelCase = spectrogram(
lowerCamelCase__ , window_function(self.fft_window_size , 'hann' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=lowerCamelCase__ , log_mel='dB' , )
return log_mel_spectrogram.T
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
__lowerCamelCase = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
__lowerCamelCase = [0]
# randomly choose index for each part
__lowerCamelCase = np.random.choice(ranges[0] )
__lowerCamelCase = np.random.choice(ranges[1] )
__lowerCamelCase = np.random.choice(ranges[2] )
__lowerCamelCase = mel[idx_front : idx_front + chunk_frames, :]
__lowerCamelCase = mel[idx_middle : idx_middle + chunk_frames, :]
__lowerCamelCase = mel[idx_back : idx_back + chunk_frames, :]
__lowerCamelCase = torch.tensor(mel[None, None, :] )
__lowerCamelCase = torch.nn.functional.interpolate(
lowerCamelCase__ , size=[chunk_frames, 64] , mode='bilinear' , align_corners=lowerCamelCase__ )
__lowerCamelCase = mel_shrink[0][0].numpy()
__lowerCamelCase = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
__lowerCamelCase = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
__lowerCamelCase = len(lowerCamelCase__ ) - max_length
__lowerCamelCase = np.random.randint(0 , overflow + 1 )
__lowerCamelCase = waveform[idx : idx + max_length]
__lowerCamelCase = self._np_extract_fbank_features(lowerCamelCase__ , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
__lowerCamelCase = self._np_extract_fbank_features(lowerCamelCase__ , self.mel_filters )
__lowerCamelCase = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
__lowerCamelCase = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
__lowerCamelCase = np.stack([mel, mel, mel, mel] , axis=0 )
__lowerCamelCase = False
else:
__lowerCamelCase = self._random_mel_fusion(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = True
else:
raise NotImplementedError(f"""data_truncating {truncation} not implemented""" )
else:
__lowerCamelCase = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
__lowerCamelCase = int(max_length / len(lowerCamelCase__ ) )
__lowerCamelCase = np.stack(np.tile(lowerCamelCase__ , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
__lowerCamelCase = int(max_length / len(lowerCamelCase__ ) )
__lowerCamelCase = np.stack(np.tile(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase = np.pad(lowerCamelCase__ , (0, max_length - waveform.shape[0]) , mode='constant' , constant_values=0 )
if truncation == "fusion":
__lowerCamelCase = self._np_extract_fbank_features(lowerCamelCase__ , self.mel_filters )
__lowerCamelCase = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
__lowerCamelCase = self._np_extract_fbank_features(lowerCamelCase__ , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> BatchFeature:
'''simple docstring'''
__lowerCamelCase = truncation if truncation is not None else self.truncation
__lowerCamelCase = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
__lowerCamelCase = isinstance(lowerCamelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__lowerCamelCase = is_batched_numpy or (
isinstance(lowerCamelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__lowerCamelCase = [np.asarray(lowerCamelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase__ , np.ndarray ):
__lowerCamelCase = np.asarray(lowerCamelCase__ , dtype=np.floataa )
elif isinstance(lowerCamelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__lowerCamelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__lowerCamelCase = [np.asarray(lowerCamelCase__ )]
# convert to mel spectrogram, truncate and pad if needed.
__lowerCamelCase = [
self._get_input_mel(lowerCamelCase__ , max_length if max_length else self.nb_max_samples , lowerCamelCase__ , lowerCamelCase__ )
for waveform in raw_speech
]
__lowerCamelCase = []
__lowerCamelCase = []
for mel, longer in padded_inputs:
input_mel.append(lowerCamelCase__ )
is_longer.append(lowerCamelCase__ )
if truncation == "fusion" and sum(lowerCamelCase__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
__lowerCamelCase = np.random.randint(0 , len(lowerCamelCase__ ) )
__lowerCamelCase = True
if isinstance(input_mel[0] , lowerCamelCase__ ):
__lowerCamelCase = [np.asarray(lowerCamelCase__ , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
__lowerCamelCase = [[longer] for longer in is_longer]
__lowerCamelCase = {'input_features': input_mel, 'is_longer': is_longer}
__lowerCamelCase = BatchFeature(lowerCamelCase__ )
if return_tensors is not None:
__lowerCamelCase = input_features.convert_to_tensors(lowerCamelCase__ )
return input_features
| 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 12
__lowerCamelCase = 12
__lowerCamelCase = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__lowerCamelCase = TransformeraDModel(**lowerCamelCase__ )
return model
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase__ )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCamelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__lowerCamelCase = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__lowerCamelCase = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase_ ( UpperCamelCase__ : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def lowerCamelCase_ ( UpperCamelCase__ : np.ndarray , UpperCamelCase__ : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
__lowerCamelCase = XGBClassifier()
classifier.fit(UpperCamelCase__ , UpperCamelCase__ )
return classifier
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
__lowerCamelCase = load_iris()
__lowerCamelCase , __lowerCamelCase = data_handling(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = train_test_split(
UpperCamelCase__ , UpperCamelCase__ , test_size=0.25 )
__lowerCamelCase = iris['target_names']
# Create an XGBoost Classifier from the training data
__lowerCamelCase = xgboost(UpperCamelCase__ , UpperCamelCase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , display_labels=UpperCamelCase__ , cmap='Blues' , normalize='true' , )
plt.title('Normalized Confusion Matrix - IRIS Dataset' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 | 1 |
from scipy.stats import spearmanr
import datasets
__A = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"
__A = "\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {'spearmanr': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results['spearmanr'])\n -0.7\n >>> print(round(results['spearmanr_pvalue'], 2))\n 0.19\n"
__A = R"\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'] , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = spearmanr(lowerCamelCase__ , lowerCamelCase__ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mask2former'''
snake_case_ = ['''swin''']
snake_case_ = {'''hidden_size''': '''hidden_dim'''}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 1_024 , lowerCamelCase__ = "relu" , lowerCamelCase__ = 6 , lowerCamelCase__ = 10 , lowerCamelCase__ = 8 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 2_048 , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 4 , lowerCamelCase__ = 255 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 12_544 , lowerCamelCase__ = 3.0 , lowerCamelCase__ = 0.75 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = True , lowerCamelCase__ = [4, 8, 16, 32] , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Tuple:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowerCamelCase = CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = backbone_config.pop('model_type' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(lowerCamelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**lowerCamelCase__ )
@classmethod
def lowercase_ ( cls , lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return cls(
backbone_config=lowerCamelCase__ , **lowerCamelCase__ , )
def lowercase_ ( self ) -> Dict[str, any]:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 348 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
__A = {"allegro/herbert-base-cased": 5_14}
__A = {}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = HerbertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__="</s>" , **lowerCamelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
lowerCamelCase__ , lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , cls_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , **lowerCamelCase__ , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.cls_token_id]
__lowerCamelCase = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> List[int]:
'''simple docstring'''
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
__lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.node_position[vertex]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = pos
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowerCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowerCamelCase = 2 * start + 1
else:
__lowerCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowerCamelCase , __lowerCamelCase = heap[smallest_child], positions[smallest_child]
__lowerCamelCase , __lowerCamelCase = (
heap[start],
positions[start],
)
__lowerCamelCase , __lowerCamelCase = temp, tempa
__lowerCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase__ )
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = position[index]
while index != 0:
__lowerCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowerCamelCase = heap[parent]
__lowerCamelCase = position[parent]
self.set_position(position[parent] , lowerCamelCase__ )
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , lowerCamelCase__ )
break
__lowerCamelCase = parent
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , 0 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = len(lowerCamelCase__ ) // 2 - 1
for i in range(lowerCamelCase__ , -1 , -1 ):
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = positions[0]
__lowerCamelCase = sys.maxsize
self.top_to_bottom(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
return temp
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Heap()
__lowerCamelCase = [0] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowerCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
__lowerCamelCase = []
for vertex in range(len(UpperCamelCase__ ) ):
distance_tv.append(sys.maxsize )
positions.append(UpperCamelCase__ )
heap.node_position.append(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = 1
__lowerCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowerCamelCase = 0
__lowerCamelCase = distance
heap.heapify(UpperCamelCase__ , UpperCamelCase__ )
for _ in range(1 , len(UpperCamelCase__ ) ):
__lowerCamelCase = heap.delete_minimum(UpperCamelCase__ , UpperCamelCase__ )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowerCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(UpperCamelCase__ )]
):
__lowerCamelCase = distance
heap.bottom_to_top(
UpperCamelCase__ , heap.get_position(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__A = int(input("Enter number of edges: ").strip())
__A = defaultdict(list)
for _ in range(edges_number):
__A = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 348 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__A = 50_00_00
__A , __A = os.path.split(__file__)
__A = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def lowerCamelCase_ ( UpperCamelCase__ : datasets.Dataset , **UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = dataset.map(**UpperCamelCase__ )
@get_duration
def lowerCamelCase_ ( UpperCamelCase__ : datasets.Dataset , **UpperCamelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = dataset.filter(**UpperCamelCase__ )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
__lowerCamelCase = {'num examples': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase = datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
__lowerCamelCase = generate_example_dataset(
os.path.join(UpperCamelCase__ , 'dataset.arrow' ) , UpperCamelCase__ , num_examples=UpperCamelCase__ )
__lowerCamelCase = transformers.AutoTokenizer.from_pretrained('bert-base-cased' , use_fast=UpperCamelCase__ )
def tokenize(UpperCamelCase__ : List[Any] ):
return tokenizer(examples['text'] )
__lowerCamelCase = map(UpperCamelCase__ )
__lowerCamelCase = map(UpperCamelCase__ , batched=UpperCamelCase__ )
__lowerCamelCase = map(UpperCamelCase__ , function=lambda UpperCamelCase__ : None , batched=UpperCamelCase__ )
with dataset.formatted_as(type='numpy' ):
__lowerCamelCase = map(UpperCamelCase__ , function=lambda UpperCamelCase__ : None , batched=UpperCamelCase__ )
with dataset.formatted_as(type='pandas' ):
__lowerCamelCase = map(UpperCamelCase__ , function=lambda UpperCamelCase__ : None , batched=UpperCamelCase__ )
with dataset.formatted_as(type='torch' , columns='numbers' ):
__lowerCamelCase = map(UpperCamelCase__ , function=lambda UpperCamelCase__ : None , batched=UpperCamelCase__ )
with dataset.formatted_as(type='tensorflow' , columns='numbers' ):
__lowerCamelCase = map(UpperCamelCase__ , function=lambda UpperCamelCase__ : None , batched=UpperCamelCase__ )
__lowerCamelCase = map(UpperCamelCase__ , function=UpperCamelCase__ , batched=UpperCamelCase__ )
__lowerCamelCase = filter(UpperCamelCase__ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(UpperCamelCase__ , 'wb' ) as f:
f.write(json.dumps(UpperCamelCase__ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
snake_case_ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
snake_case_ = "question"
snake_case_ = "context"
snake_case_ = "answers"
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 348 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_choices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = True
snake_case_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowerCamelCase__ )
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
snake_case_ = '''swin'''
snake_case_ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , lowerCamelCase__=224 , lowerCamelCase__=4 , lowerCamelCase__=3 , lowerCamelCase__=96 , lowerCamelCase__=[2, 2, 6, 2] , lowerCamelCase__=[3, 6, 12, 24] , lowerCamelCase__=7 , lowerCamelCase__=4.0 , lowerCamelCase__=True , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__="gelu" , lowerCamelCase__=False , lowerCamelCase__=0.02 , lowerCamelCase__=1e-5 , lowerCamelCase__=32 , lowerCamelCase__=None , lowerCamelCase__=None , **lowerCamelCase__ , ) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = embed_dim
__lowerCamelCase = depths
__lowerCamelCase = len(lowerCamelCase__ )
__lowerCamelCase = num_heads
__lowerCamelCase = window_size
__lowerCamelCase = mlp_ratio
__lowerCamelCase = qkv_bias
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = drop_path_rate
__lowerCamelCase = hidden_act
__lowerCamelCase = use_absolute_embeddings
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowerCamelCase = int(embed_dim * 2 ** (len(lowerCamelCase__ ) - 1) )
__lowerCamelCase = ['stem'] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase__ ) + 1 )]
__lowerCamelCase , __lowerCamelCase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase__ , out_indices=lowerCamelCase__ , stage_names=self.stage_names )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = version.parse('''1.11''' )
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-4
| 348 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__A = logging.get_logger(__name__)
__A = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[List[float]] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
else:
return _interleave_iterable_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
else:
return _concatenate_iterable_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 10 ) -> str:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or n < 0:
raise ValueError('Invalid input' )
__lowerCamelCase = 10**n
__lowerCamelCase = 2_8433 * (pow(2 , 783_0457 , UpperCamelCase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f'''{solution(10) = }''')
| 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = ["model.decoder.embed_positions.weights"]
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if "emb" in name:
__lowerCamelCase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__lowerCamelCase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__lowerCamelCase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__lowerCamelCase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__lowerCamelCase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__lowerCamelCase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__lowerCamelCase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__lowerCamelCase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def lowerCamelCase_ ( UpperCamelCase__ : OrderedDict , UpperCamelCase__ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = rename_keys(UpperCamelCase__ )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
__lowerCamelCase = 1024
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 1536
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 2048
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=UpperCamelCase__ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCamelCase__ , num_attention_heads=UpperCamelCase__ , )
return config
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]="cpu" ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = MusicGen.get_pretrained(UpperCamelCase__ , device=UpperCamelCase__ )
__lowerCamelCase = decoder_config_from_checkpoint(UpperCamelCase__ )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase , __lowerCamelCase = rename_state_dict(
UpperCamelCase__ , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('t5-base' )
__lowerCamelCase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__lowerCamelCase = MusicgenForCausalLM(UpperCamelCase__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase , __lowerCamelCase = decoder.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ , audio_encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCamelCase__ )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('t5-base' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# set the appropriate bos/pad token ids
__lowerCamelCase = 2048
__lowerCamelCase = 2048
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCamelCase__ )
processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__A = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 348 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = (PNDMScheduler,)
snake_case_ = (('''num_inference_steps''', 50),)
def lowercase_ ( self , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = {
'num_train_timesteps': 1_000,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**lowerCamelCase__ )
return config
def lowercase_ ( self , lowerCamelCase__=0 , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('num_inference_steps' , lowerCamelCase__ )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config(**lowerCamelCase__ )
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals
__lowerCamelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase__ )
__lowerCamelCase = scheduler_class.from_pretrained(lowerCamelCase__ )
new_scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals
__lowerCamelCase = dummy_past_residuals[:]
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = new_scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__lowerCamelCase = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = new_scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def lowercase_ ( self , lowerCamelCase__=0 , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('num_inference_steps' , lowerCamelCase__ )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCamelCase = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase__ )
__lowerCamelCase = scheduler_class.from_pretrained(lowerCamelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residual (must be after setting timesteps)
__lowerCamelCase = dummy_past_residuals[:]
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = new_scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__lowerCamelCase = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = new_scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase_ ( self , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(**lowerCamelCase__ )
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
__lowerCamelCase = 10
__lowerCamelCase = self.dummy_model()
__lowerCamelCase = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase__ )
for i, t in enumerate(scheduler.prk_timesteps ):
__lowerCamelCase = model(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
__lowerCamelCase = model(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
return sample
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = dict(self.forward_default_kwargs )
__lowerCamelCase = kwargs.pop('num_inference_steps' , lowerCamelCase__ )
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCamelCase__ , 'set_timesteps' ):
scheduler.set_timesteps(lowerCamelCase__ )
elif num_inference_steps is not None and not hasattr(lowerCamelCase__ , 'set_timesteps' ):
__lowerCamelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__lowerCamelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
__lowerCamelCase = dummy_past_residuals[:]
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , 0 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , 1 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__lowerCamelCase = scheduler.step_plms(lowerCamelCase__ , 0 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
__lowerCamelCase = scheduler.step_plms(lowerCamelCase__ , 1 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase__ )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCamelCase__ )
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config(steps_offset=1 )
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.00_01, 0.0_01] , [0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowerCamelCase__ , beta_end=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowerCamelCase__ )
def lowercase_ ( self ) -> str:
'''simple docstring'''
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
__lowerCamelCase = 27
for scheduler_class in self.scheduler_classes:
__lowerCamelCase = self.dummy_sample
__lowerCamelCase = 0.1 * sample
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
__lowerCamelCase = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
def lowercase_ ( self ) -> str:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
__lowerCamelCase = self.scheduler_classes[0]
__lowerCamelCase = self.get_scheduler_config()
__lowerCamelCase = scheduler_class(**lowerCamelCase__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.full_loop()
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1e-2
assert abs(result_mean.item() - 0.25_80 ) < 1e-3
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.full_loop(prediction_type='v_prediction' )
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1e-2
assert abs(result_mean.item() - 0.08_78 ) < 1e-3
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
__lowerCamelCase = self.full_loop(set_alpha_to_one=lowerCamelCase__ , beta_start=0.01 )
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1e-2
assert abs(result_mean.item() - 0.29_95 ) < 1e-3
def lowercase_ ( self ) -> str:
'''simple docstring'''
# We specify different beta, so that the first alpha is 0.99
__lowerCamelCase = self.full_loop(set_alpha_to_one=lowerCamelCase__ , beta_start=0.01 )
__lowerCamelCase = torch.sum(torch.abs(lowerCamelCase__ ) )
__lowerCamelCase = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1e-2
assert abs(result_mean.item() - 0.24_34 ) < 1e-3
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__A = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=DataRow("", "|", "|"),
padding=1,
with_header_hide=None,
)
__A = []
__A = []
__A = {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}
__A = [
{
"type": "header",
"text": {
"type": "plain_text",
"text": f'''🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results''',
"emoji": True,
},
}
]
__A = 0
for log in Path().glob("*.log"):
__A = 0
with open(log, "r") as f:
for line in f:
__A = json.loads(line)
if line.get("nodeid", "") != "":
__A = line["nodeid"]
if line.get("duration", None) is not None:
__A = f'''{line['duration']:.4f}'''
if line.get("outcome", "") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("_")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__A = []
log.unlink()
__A = ""
__A = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
__A = []
__A = {}
for test in failed_tests:
__A = test[0].split("::")
__A = data[0].split("/")[-1]
if data[0] not in filesafailed:
__A = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__A = [test[0] for test in failed_table]
__A = list(set(files))
# Count number of instances in failed_tests
__A = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__A = tabulate(
table,
headers=["Test Location", "Num Failed"],
tablefmt=hf_table_format,
stralign="right",
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 30_00:
__A = "Too many failed tests, please see the full report in the Action results."
__A = len(err) + 10
__A = message[: 30_00 - offset] + f'''\n...\n```\n{err}'''
print(f'''### {message}''')
else:
__A = "No failed tests! 🤗"
print(f'''## {message}''')
payload.append(no_error_payload)
if os.environ.get("TEST_TYPE", "") != "":
from slack_sdk import WebClient
__A = WebClient(token=os.environ["SLACK_API_TOKEN"])
if message != "No failed tests! 🤗":
__A = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": message,
},
}
payload.append(md_report)
__A = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": "*For more details:*",
},
"accessory": {
"type": "button",
"text": {
"type": "plain_text",
"text": "Check Action results",
"emoji": True,
},
"url": f'''https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
payload.append(action_button)
__A = {
"type": "context",
"elements": [
{
"type": "plain_text",
"text": f'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
}
],
}
payload.append(date_report)
__A = client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
__A = response.data["ts"]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__A = ""
for i, row in enumerate(test_failures):
if row[0] != test_class:
__A = row[0]
else:
__A = ""
__A = {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```''',
},
}
client.chat_postMessage(
channel="#accelerate-ci-daily",
thread_ts=ts,
blocks=[payload],
)
| 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__A = logging.get_logger("transformers.models.speecht5")
__A = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__A = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = []
__A = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
elif weight_type == "running_mean":
__lowerCamelCase = value
elif weight_type == "running_var":
__lowerCamelCase = value
elif weight_type == "num_batches_tracked":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
if task == "s2t":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2T
__lowerCamelCase = IGNORE_KEYS_S2T
elif task == "t2s":
__lowerCamelCase = None
__lowerCamelCase = MAPPING_T2S
__lowerCamelCase = IGNORE_KEYS_T2S
elif task == "s2s":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2S
__lowerCamelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(UpperCamelCase__ , UpperCamelCase__ ):
logger.info(F"""{name} was ignored""" )
continue
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
__lowerCamelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCamelCase = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCamelCase = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase = 'weight_v'
elif "bias" in name:
__lowerCamelCase = 'bias'
elif "weight" in name:
__lowerCamelCase = 'weight'
elif "running_mean" in name:
__lowerCamelCase = 'running_mean'
elif "running_var" in name:
__lowerCamelCase = 'running_var'
elif "num_batches_tracked" in name:
__lowerCamelCase = 'num_batches_tracked'
else:
__lowerCamelCase = None
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
continue
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = full_name.split('conv_layers.' )[-1]
__lowerCamelCase = name.split('.' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCamelCase__ )
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : str=None , UpperCamelCase__ : List[str]=None , ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__lowerCamelCase = SpeechTaConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCamelCase = SpeechTaConfig()
if task == "s2t":
__lowerCamelCase = config.max_text_positions
__lowerCamelCase = SpeechTaForSpeechToText(UpperCamelCase__ )
elif task == "t2s":
__lowerCamelCase = 1876
__lowerCamelCase = 600
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForTextToSpeech(UpperCamelCase__ )
elif task == "s2s":
__lowerCamelCase = 1876
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForSpeechToSpeech(UpperCamelCase__ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
__lowerCamelCase = SpeechTaTokenizer(UpperCamelCase__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken('<mask>' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
__lowerCamelCase = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
__lowerCamelCase = SpeechTaFeatureExtractor()
__lowerCamelCase = SpeechTaProcessor(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = torch.load(UpperCamelCase__ )
recursively_load_weights(fairseq_checkpoint['model'] , UpperCamelCase__ , UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if repo_id:
print('Pushing to the hub...' )
processor.push_to_hub(UpperCamelCase__ )
model.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__A = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 348 | 1 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__A = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
__A = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
__A = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
] , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ) -> Optional[Any]:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(lowerCamelCase__ , lowerCamelCase__ , sample_weight=lowerCamelCase__ ) ),
}
| 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
__lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''text''': Value('''string''' )} )
snake_case_ = Features({'''labels''': ClassLabel} )
snake_case_ = "text"
snake_case_ = "labels"
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
if self.label_column not in features:
raise ValueError(f"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , lowerCamelCase__ ):
raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""" )
__lowerCamelCase = copy.deepcopy(self )
__lowerCamelCase = self.label_schema.copy()
__lowerCamelCase = features[self.label_column]
__lowerCamelCase = label_schema
return task_template
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
__lowerCamelCase = [5, 5, 5, 5]
elif "fl4" in model_name:
__lowerCamelCase = [4, 4, 4, 4]
__lowerCamelCase = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
if "lrf" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
else:
__lowerCamelCase = [2, 2, 2, 2]
if "tiny" in model_name:
__lowerCamelCase = 96
elif "small" in model_name:
__lowerCamelCase = 96
elif "base" in model_name:
__lowerCamelCase = 128
elif "large" in model_name:
__lowerCamelCase = 192
elif "xlarge" in model_name:
__lowerCamelCase = 256
elif "huge" in model_name:
__lowerCamelCase = 352
# set label information
__lowerCamelCase = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
__lowerCamelCase = 'imagenet-22k-id2label.json'
else:
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = FocalNetConfig(
embed_dim=UpperCamelCase__ , depths=UpperCamelCase__ , focal_levels=UpperCamelCase__ , focal_windows=UpperCamelCase__ , use_conv_embed=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , use_post_layernorm=UpperCamelCase__ , use_layerscale=UpperCamelCase__ , )
return config
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
if "patch_embed.proj" in name:
__lowerCamelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__lowerCamelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
__lowerCamelCase = 'encoder.' + name
if "encoder.layers" in name:
__lowerCamelCase = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
__lowerCamelCase = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
__lowerCamelCase = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__lowerCamelCase = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__lowerCamelCase = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__lowerCamelCase = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
__lowerCamelCase = 'layernorm.weight'
if name == "norm.bias":
__lowerCamelCase = 'layernorm.bias'
if "head" in name:
__lowerCamelCase = name.replace('head' , 'classifier' )
else:
__lowerCamelCase = 'focalnet.' + name
return name
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple=False ) -> Dict:
"""simple docstring"""
__lowerCamelCase = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
__lowerCamelCase = model_name_to_url[model_name]
print('Checkpoint URL: ' , UpperCamelCase__ )
__lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = val
__lowerCamelCase = get_focalnet_config(UpperCamelCase__ )
__lowerCamelCase = FocalNetForImageClassification(UpperCamelCase__ )
model.eval()
# load state dict
model.load_state_dict(UpperCamelCase__ )
# verify conversion
__lowerCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase = BitImageProcessor(
do_resize=UpperCamelCase__ , size={'shortest_edge': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCamelCase__ , crop_size=224 , do_normalize=UpperCamelCase__ , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ , )
__lowerCamelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
__lowerCamelCase = processor(images=UpperCamelCase__ , return_tensors='pt' )
__lowerCamelCase = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ),
] )
__lowerCamelCase = image_transforms(UpperCamelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , UpperCamelCase__ , atol=1E-4 )
__lowerCamelCase = model(**UpperCamelCase__ )
__lowerCamelCase = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__lowerCamelCase = torch.tensor([0.21_66, -0.43_68, 0.21_91] )
elif model_name == "focalnet-tiny-lrf":
__lowerCamelCase = torch.tensor([1.16_69, 0.01_25, -0.16_95] )
elif model_name == "focalnet-small":
__lowerCamelCase = torch.tensor([0.49_17, -0.04_30, 0.13_41] )
elif model_name == "focalnet-small-lrf":
__lowerCamelCase = torch.tensor([-0.25_88, -0.53_42, -0.23_31] )
elif model_name == "focalnet-base":
__lowerCamelCase = torch.tensor([-0.16_55, -0.40_90, -0.17_30] )
elif model_name == "focalnet-base-lrf":
__lowerCamelCase = torch.tensor([0.53_06, -0.04_83, -0.39_28] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(F"""{model_name}""" )
processor.push_to_hub(F"""{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
__A = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 348 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small' )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='np' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='np' ).input_ids
__lowerCamelCase = shift_tokens_right(lowerCamelCase__ , model.config.pad_token_id , model.config.decoder_start_token_id )
__lowerCamelCase = model(lowerCamelCase__ , decoder_input_ids=lowerCamelCase__ ).logits
__lowerCamelCase = optax.softmax_cross_entropy(lowerCamelCase__ , onehot(lowerCamelCase__ , logits.shape[-1] ) ).mean()
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __lowerCamelCase = divmod(len(UpperCamelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = [float(x) for x in input("Enter the elements of first array: ").split()]
__A = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 348 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
__lowerCamelCase = s_dict.pop(UpperCamelCase__ )
elif "subsample" in key:
__lowerCamelCase = s_dict.pop(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> str:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = emb.weight.shape
__lowerCamelCase = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
__lowerCamelCase = emb.weight.data
return lin_layer
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = torch.load(UpperCamelCase__ , map_location='cpu' )
__lowerCamelCase = mam_aaa['args']
__lowerCamelCase = mam_aaa['model']
__lowerCamelCase = state_dict['decoder.output_projection.weight']
remove_ignore_keys_(UpperCamelCase__ )
rename_keys(UpperCamelCase__ )
__lowerCamelCase = state_dict['decoder.embed_tokens.weight'].shape[0]
__lowerCamelCase = args.share_decoder_input_output_embed
__lowerCamelCase = [int(UpperCamelCase__ ) for i in args.conv_kernel_sizes.split(',' )]
__lowerCamelCase = SpeechaTextConfig(
vocab_size=UpperCamelCase__ , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , num_conv_layers=len(UpperCamelCase__ ) , conv_channels=args.conv_channels , conv_kernel_sizes=UpperCamelCase__ , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=UpperCamelCase__ , num_beams=5 , max_length=200 , use_cache=UpperCamelCase__ , decoder_start_token_id=2 , early_stopping=UpperCamelCase__ , )
__lowerCamelCase = SpeechaTextForConditionalGeneration(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0 and not set(UpperCamelCase__ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F""" but all the following weights are missing {missing}""" )
if tie_embeds:
__lowerCamelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__lowerCamelCase = lm_head_weights
model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
__A = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["BloomTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
"BloomForCausalLM",
"BloomModel",
"BloomPreTrainedModel",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''philschmid/bart-large-cnn-samsum'''
snake_case_ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
snake_case_ = '''summarizer'''
snake_case_ = AutoTokenizer
snake_case_ = AutoModelForSeqaSeqLM
snake_case_ = ['''text''']
snake_case_ = ['''text''']
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
return self.model.generate(**lowerCamelCase__ )[0]
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : bytes ) -> str:
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bytes:
"""simple docstring"""
if (len(UpperCamelCase__ ) % 2) != 0:
raise ValueError(
'Base16 encoded data is invalid:\nData does not have an even number of hex digits.' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(UpperCamelCase__ ) <= set('0123456789ABCDEF' ):
raise ValueError(
'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(UpperCamelCase__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_choices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = True
snake_case_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowerCamelCase__ )
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 348 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self , lowerCamelCase__ ) -> str:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
__lowerCamelCase = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowerCamelCase__ , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = 'sgugger/tiny-distilbert-classification'
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , only_pretrain_model=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = AutoConfig.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowerCamelCase__ , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ , [config] )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = AutoConfig.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ , [config] )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = AutoConfig.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ , [config] )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = 'patrickvonplaten/t5-tiny-random'
__lowerCamelCase = AutoConfig.from_pretrained(lowerCamelCase__ )
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ , configs=[config] )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU' ) ) == 0 , 'Cannot do xla on CPU.' )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowerCamelCase__ , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=lowerCamelCase__ , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowerCamelCase__ , save_to_csv=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(lowerCamelCase__ , 'inf_time.csv' ) , inference_memory_csv_file=os.path.join(lowerCamelCase__ , 'inf_mem.csv' ) , env_info_csv_file=os.path.join(lowerCamelCase__ , 'env.csv' ) , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
benchmark.run()
self.assertTrue(Path(os.path.join(lowerCamelCase__ , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowerCamelCase__ , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(lowerCamelCase__ , 'env.csv' ) ).exists() )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(lowerCamelCase__ ):
self.assertTrue(hasattr(lowerCamelCase__ , 'sequential' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'cumulative' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'current' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowerCamelCase__ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(lowerCamelCase__ , 'log.txt' ) , log_print=lowerCamelCase__ , trace_memory_line_by_line=lowerCamelCase__ , eager_mode=lowerCamelCase__ , multi_process=lowerCamelCase__ , )
__lowerCamelCase = TensorFlowBenchmark(lowerCamelCase__ )
__lowerCamelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(lowerCamelCase__ , 'log.txt' ) ).exists() )
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__lowerCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A red cat sitting on a park bench'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , mask_image=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A red cat sitting on a park bench'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , mask_image=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__A = logging.get_logger(__name__)
__A = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
__A = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''whisper'''
snake_case_ = ['''past_key_values''']
snake_case_ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , lowerCamelCase__=51_865 , lowerCamelCase__=80 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=6 , lowerCamelCase__=4 , lowerCamelCase__=1_536 , lowerCamelCase__=1_536 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=50_257 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="gelu" , lowerCamelCase__=256 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=False , lowerCamelCase__=1_500 , lowerCamelCase__=448 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=50_256 , lowerCamelCase__=None , lowerCamelCase__=[220, 50_256] , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=False , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__=7 , **lowerCamelCase__ , ) -> str:
'''simple docstring'''
__lowerCamelCase = vocab_size
__lowerCamelCase = num_mel_bins
__lowerCamelCase = d_model
__lowerCamelCase = encoder_layers
__lowerCamelCase = encoder_attention_heads
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = encoder_ffn_dim
__lowerCamelCase = dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = activation_function
__lowerCamelCase = init_std
__lowerCamelCase = encoder_layerdrop
__lowerCamelCase = decoder_layerdrop
__lowerCamelCase = use_cache
__lowerCamelCase = encoder_layers
__lowerCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCamelCase = max_source_positions
__lowerCamelCase = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
__lowerCamelCase = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
__lowerCamelCase = median_filter_width
super().__init__(
pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , decoder_start_token_id=lowerCamelCase__ , suppress_tokens=lowerCamelCase__ , begin_suppress_tokens=lowerCamelCase__ , **lowerCamelCase__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__lowerCamelCase = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
__lowerCamelCase = {0: 'batch'}
else:
__lowerCamelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase__ , direction='inputs' )
return common_inputs
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = -1 , lowerCamelCase__ = -1 , lowerCamelCase__ = False , lowerCamelCase__ = None , lowerCamelCase__ = 22_050 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 220 , ) -> Mapping[str, Any]:
'''simple docstring'''
__lowerCamelCase = OrderedDict()
__lowerCamelCase = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowerCamelCase__ , framework=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , time_duration=lowerCamelCase__ , frequency=lowerCamelCase__ , )
__lowerCamelCase = encoder_inputs['input_features'].shape[2]
__lowerCamelCase = encoder_sequence_length // 2 if self.use_past else seq_length
__lowerCamelCase = super().generate_dummy_inputs(
preprocessor.tokenizer , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = encoder_inputs.pop('input_features' )
__lowerCamelCase = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
__lowerCamelCase = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-3
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=14 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=0.02 , ) -> int:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = rotary_dim
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = initializer_range
__lowerCamelCase = None
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
__lowerCamelCase = vocab_size - 1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=lowerCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , attention_mask=lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = 20
__lowerCamelCase = model_class_name(lowerCamelCase__ )
__lowerCamelCase = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__lowerCamelCase = model.init_cache(input_ids.shape[0] , lowerCamelCase__ )
__lowerCamelCase = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCamelCase = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCamelCase = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowerCamelCase__ , position_ids=lowerCamelCase__ , )
__lowerCamelCase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
snake_case_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = FlaxGPTJModelTester(self )
def lowercase_ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@tooslow
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
__lowerCamelCase = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=lowerCamelCase__ , truncation=lowerCamelCase__ )
__lowerCamelCase = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = False
__lowerCamelCase = model.config.eos_token_id
__lowerCamelCase = jax.jit(model.generate )
__lowerCamelCase = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
__lowerCamelCase = tokenizer.batch_decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
__lowerCamelCase = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCamelCase__ )
__lowerCamelCase = fx_state
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = model_class.from_pretrained(lowerCamelCase__ , from_pt=lowerCamelCase__ )
__lowerCamelCase = fx_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCamelCase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCamelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = pt_model_class(lowerCamelCase__ ).eval()
__lowerCamelCase = model_class(lowerCamelCase__ , dtype=jnp.floataa )
__lowerCamelCase = load_flax_weights_in_pytorch_model(lowerCamelCase__ , fx_model.params )
__lowerCamelCase , __lowerCamelCase = pt_inputs['input_ids'].shape
__lowerCamelCase = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 0
__lowerCamelCase = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__lowerCamelCase = pt_model(**lowerCamelCase__ ).to_tuple()
__lowerCamelCase = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase = pt_model_class.from_pretrained(lowerCamelCase__ , from_flax=lowerCamelCase__ )
with torch.no_grad():
__lowerCamelCase = pt_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) , len(lowerCamelCase__ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowerCamelCase__ , lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 348 | 1 |
__A = "Input must be a string of 8 numbers plus letter"
__A = "TRWAGMYFPDXBNJZSQVHLCKE"
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bool:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = F"""Expected string as input, found {type(UpperCamelCase__ ).__name__}"""
raise TypeError(UpperCamelCase__ )
__lowerCamelCase = spanish_id.replace('-' , '' ).upper()
if len(UpperCamelCase__ ) != 9:
raise ValueError(UpperCamelCase__ )
try:
__lowerCamelCase = int(spanish_id_clean[0:8] )
__lowerCamelCase = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(UpperCamelCase__ ) from ex
if letter.isdigit():
raise ValueError(UpperCamelCase__ )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__A = False
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return 12
@property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowerCamelCase__ )
@property
def lowercase_ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__lowerCamelCase = 12
__lowerCamelCase = 12
__lowerCamelCase = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__lowerCamelCase = TransformeraDModel(**lowerCamelCase__ )
return model
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCamelCase__ )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = 'cpu'
__lowerCamelCase = self.dummy_vqvae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = self.dummy_tokenizer
__lowerCamelCase = self.dummy_transformer
__lowerCamelCase = VQDiffusionScheduler(self.num_embed )
__lowerCamelCase = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCamelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__lowerCamelCase = VQDiffusionPipeline(
vqvae=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , transformer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
__lowerCamelCase = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'teddy bear playing in the pool'
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe([prompt] , generator=lowerCamelCase__ , num_inference_steps=2 , output_type='np' )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipe(
[prompt] , generator=lowerCamelCase__ , output_type='np' , return_dict=lowerCamelCase__ , num_inference_steps=2 )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__lowerCamelCase = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__lowerCamelCase = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__lowerCamelCase = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__lowerCamelCase = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__lowerCamelCase = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__A = logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
snake_case_ = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
snake_case_ = field(
default=1_28 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
snake_case_ = field(
default=__magic_name__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.task_name.lower()
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''train'''
snake_case_ = '''dev'''
snake_case_ = '''test'''
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = Split.train , lowerCamelCase__ = None , ) -> int:
'''simple docstring'''
warnings.warn(
'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' , lowerCamelCase__ , )
__lowerCamelCase = args
__lowerCamelCase = glue_processors[args.task_name]()
__lowerCamelCase = glue_output_modes[args.task_name]
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
try:
__lowerCamelCase = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
# Load data features from cache or dataset file
__lowerCamelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" , )
__lowerCamelCase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowerCamelCase , __lowerCamelCase = label_list[2], label_list[1]
__lowerCamelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCamelCase = cached_features_file + '.lock'
with FileLock(lowerCamelCase__ ):
if os.path.exists(lowerCamelCase__ ) and not args.overwrite_cache:
__lowerCamelCase = time.time()
__lowerCamelCase = torch.load(lowerCamelCase__ )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start )
else:
logger.info(f"""Creating features from dataset file at {args.data_dir}""" )
if mode == Split.dev:
__lowerCamelCase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowerCamelCase = self.processor.get_test_examples(args.data_dir )
else:
__lowerCamelCase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowerCamelCase = examples[:limit_length]
__lowerCamelCase = glue_convert_examples_to_features(
lowerCamelCase__ , lowerCamelCase__ , max_length=args.max_seq_length , label_list=lowerCamelCase__ , output_mode=self.output_mode , )
__lowerCamelCase = time.time()
torch.save(self.features , lowerCamelCase__ )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__( self ) -> List[Any]:
'''simple docstring'''
return len(self.features )
def __getitem__( self , lowerCamelCase__ ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
def lowercase_ ( self ) -> int:
'''simple docstring'''
return self.label_list
| 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = RoFormerTokenizer
snake_case_ = RoFormerTokenizerFast
snake_case_ = True
snake_case_ = True
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
def lowercase_ ( self , **lowerCamelCase__ ) -> str:
'''simple docstring'''
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowerCamelCase__ )
def lowercase_ ( self , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = '永和服装饰品有限公司,今天天气非常好'
__lowerCamelCase = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
return input_text, output_text
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase , __lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase , __lowerCamelCase = self.get_chinese_input_output_texts()
__lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , output_text.split() )
__lowerCamelCase = tokens + [tokenizer.unk_token]
__lowerCamelCase = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
| 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mask2former'''
snake_case_ = ['''swin''']
snake_case_ = {'''hidden_size''': '''hidden_dim'''}
def __init__( self , lowerCamelCase__ = None , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 256 , lowerCamelCase__ = 1_024 , lowerCamelCase__ = "relu" , lowerCamelCase__ = 6 , lowerCamelCase__ = 10 , lowerCamelCase__ = 8 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = 2_048 , lowerCamelCase__ = False , lowerCamelCase__ = False , lowerCamelCase__ = 4 , lowerCamelCase__ = 255 , lowerCamelCase__ = 100 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 12_544 , lowerCamelCase__ = 3.0 , lowerCamelCase__ = 0.75 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = True , lowerCamelCase__ = [4, 8, 16, 32] , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Tuple:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowerCamelCase = CONFIG_MAPPING['swin'](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = backbone_config.pop('model_type' )
__lowerCamelCase = CONFIG_MAPPING[backbone_model_type]
__lowerCamelCase = config_class.from_dict(lowerCamelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
__lowerCamelCase = backbone_config
__lowerCamelCase = feature_size
__lowerCamelCase = mask_feature_size
__lowerCamelCase = hidden_dim
__lowerCamelCase = encoder_feedforward_dim
__lowerCamelCase = activation_function
__lowerCamelCase = encoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = dropout
__lowerCamelCase = dim_feedforward
__lowerCamelCase = pre_norm
__lowerCamelCase = enforce_input_projection
__lowerCamelCase = common_stride
__lowerCamelCase = ignore_value
__lowerCamelCase = num_queries
__lowerCamelCase = no_object_weight
__lowerCamelCase = class_weight
__lowerCamelCase = mask_weight
__lowerCamelCase = dice_weight
__lowerCamelCase = train_num_points
__lowerCamelCase = oversample_ratio
__lowerCamelCase = importance_sample_ratio
__lowerCamelCase = init_std
__lowerCamelCase = init_xavier_std
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = feature_strides
__lowerCamelCase = output_auxiliary_logits
__lowerCamelCase = decoder_layers
super().__init__(**lowerCamelCase__ )
@classmethod
def lowercase_ ( cls , lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
return cls(
backbone_config=lowerCamelCase__ , **lowerCamelCase__ , )
def lowercase_ ( self ) -> Dict[str, any]:
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(self.__dict__ )
__lowerCamelCase = self.backbone_config.to_dict()
__lowerCamelCase = self.__class__.model_type
return output
| 348 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
__A = parser.parse_args()
__A = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A = CLIPImageProcessor()
__A = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
__A = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
__A = logging.get_logger(__name__)
__A = {
"allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
"allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
"allenai/longformer-large-4096-finetuned-triviaqa": (
"https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
),
"allenai/longformer-base-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
),
"allenai/longformer-large-4096-extra.pos.embd.only": (
"https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
),
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''longformer'''
def __init__( self , lowerCamelCase__ = 512 , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 0 , lowerCamelCase__ = 2 , lowerCamelCase__ = 30_522 , lowerCamelCase__ = 768 , lowerCamelCase__ = 12 , lowerCamelCase__ = 12 , lowerCamelCase__ = 3_072 , lowerCamelCase__ = "gelu" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 512 , lowerCamelCase__ = 2 , lowerCamelCase__ = 0.02 , lowerCamelCase__ = 1e-12 , lowerCamelCase__ = False , **lowerCamelCase__ , ) -> Tuple:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = attention_window
__lowerCamelCase = sep_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = eos_token_id
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = onnx_export
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ = "default" , lowerCamelCase__ = None ) -> int:
'''simple docstring'''
super().__init__(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = True
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__lowerCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__lowerCamelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def lowercase_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__lowerCamelCase = super().outputs
if self.task == "default":
__lowerCamelCase = {0: 'batch'}
return outputs
@property
def lowercase_ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ = -1 , lowerCamelCase__ = -1 , lowerCamelCase__ = False , lowerCamelCase__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
__lowerCamelCase = super().generate_dummy_inputs(
preprocessor=lowerCamelCase__ , batch_size=lowerCamelCase__ , seq_length=lowerCamelCase__ , is_pair=lowerCamelCase__ , framework=lowerCamelCase__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
__lowerCamelCase = torch.zeros_like(inputs['input_ids'] )
# make every second token global
__lowerCamelCase = 1
return inputs
| 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
return self.node_position[vertex]
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = pos
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowerCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowerCamelCase = 2 * start + 1
else:
__lowerCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowerCamelCase , __lowerCamelCase = heap[smallest_child], positions[smallest_child]
__lowerCamelCase , __lowerCamelCase = (
heap[start],
positions[start],
)
__lowerCamelCase , __lowerCamelCase = temp, tempa
__lowerCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase__ )
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = position[index]
while index != 0:
__lowerCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowerCamelCase = heap[parent]
__lowerCamelCase = position[parent]
self.set_position(position[parent] , lowerCamelCase__ )
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , lowerCamelCase__ )
break
__lowerCamelCase = parent
else:
__lowerCamelCase = val
__lowerCamelCase = temp
self.set_position(lowerCamelCase__ , 0 )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> str:
'''simple docstring'''
__lowerCamelCase = len(lowerCamelCase__ ) // 2 - 1
for i in range(lowerCamelCase__ , -1 , -1 ):
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = positions[0]
__lowerCamelCase = sys.maxsize
self.top_to_bottom(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
return temp
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Heap()
__lowerCamelCase = [0] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowerCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
__lowerCamelCase = []
for vertex in range(len(UpperCamelCase__ ) ):
distance_tv.append(sys.maxsize )
positions.append(UpperCamelCase__ )
heap.node_position.append(UpperCamelCase__ )
__lowerCamelCase = []
__lowerCamelCase = 1
__lowerCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowerCamelCase = 0
__lowerCamelCase = distance
heap.heapify(UpperCamelCase__ , UpperCamelCase__ )
for _ in range(1 , len(UpperCamelCase__ ) ):
__lowerCamelCase = heap.delete_minimum(UpperCamelCase__ , UpperCamelCase__ )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowerCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(UpperCamelCase__ )]
):
__lowerCamelCase = distance
heap.bottom_to_top(
UpperCamelCase__ , heap.get_position(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__A = int(input("Enter number of edges: ").strip())
__A = defaultdict(list)
for _ in range(edges_number):
__A = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 348 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : List[Any]="pt" ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = {'add_prefix_space': True} if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and not line.startswith(' ' ) else {}
__lowerCamelCase = padding_side
return tokenizer(
[line] , max_length=UpperCamelCase__ , padding='max_length' if pad_to_max_length else None , truncation=UpperCamelCase__ , return_tensors=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any]=None , ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = input_ids.ne(UpperCamelCase__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="train" , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="" , ) -> int:
'''simple docstring'''
super().__init__()
__lowerCamelCase = Path(lowerCamelCase__ ).joinpath(type_path + '.source' )
__lowerCamelCase = Path(lowerCamelCase__ ).joinpath(type_path + '.target' )
__lowerCamelCase = self.get_char_lens(self.src_file )
__lowerCamelCase = max_source_length
__lowerCamelCase = max_target_length
assert min(self.src_lens ) > 0, f"""found empty line in {self.src_file}"""
__lowerCamelCase = tokenizer
__lowerCamelCase = prefix
if n_obs is not None:
__lowerCamelCase = self.src_lens[:n_obs]
__lowerCamelCase = src_lang
__lowerCamelCase = tgt_lang
def __len__( self ) -> str:
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self , lowerCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
__lowerCamelCase = index + 1 # linecache starts at 1
__lowerCamelCase = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase__ ).rstrip('\n' )
__lowerCamelCase = linecache.getline(str(self.tgt_file ) , lowerCamelCase__ ).rstrip('\n' )
assert source_line, f"""empty source line for index {index}"""
assert tgt_line, f"""empty tgt line for index {index}"""
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__lowerCamelCase = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase__ ) else self.tokenizer
)
__lowerCamelCase = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase__ ) else self.tokenizer
__lowerCamelCase = encode_line(lowerCamelCase__ , lowerCamelCase__ , self.max_source_length , 'right' )
__lowerCamelCase = encode_line(lowerCamelCase__ , lowerCamelCase__ , self.max_target_length , 'right' )
__lowerCamelCase = source_inputs['input_ids'].squeeze()
__lowerCamelCase = target_inputs['input_ids'].squeeze()
__lowerCamelCase = source_inputs['attention_mask'].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowercase_ ( lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
return [len(lowerCamelCase__ ) for x in Path(lowerCamelCase__ ).open().readlines()]
def lowercase_ ( self , lowerCamelCase__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
__lowerCamelCase = torch.stack([x['input_ids'] for x in batch] )
__lowerCamelCase = torch.stack([x['attention_mask'] for x in batch] )
__lowerCamelCase = torch.stack([x['decoder_input_ids'] for x in batch] )
__lowerCamelCase = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase__ )
else self.tokenizer.pad_token_id
)
__lowerCamelCase = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase__ )
else self.tokenizer.pad_token_id
)
__lowerCamelCase = trim_batch(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase = trim_batch(lowerCamelCase__ , lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = {
'input_ids': source_ids,
'attention_mask': source_mask,
'decoder_input_ids': y,
}
return batch
__A = getLogger(__name__)
def lowerCamelCase_ ( UpperCamelCase__ : List[List] ) -> Any:
"""simple docstring"""
return list(itertools.chain.from_iterable(UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = get_git_info()
save_json(UpperCamelCase__ , os.path.join(UpperCamelCase__ , 'git_log.json' ) )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str]=4 , **UpperCamelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
with open(UpperCamelCase__ , 'w' ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Tuple:
"""simple docstring"""
with open(UpperCamelCase__ ) as f:
return json.load(UpperCamelCase__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = git.Repo(search_parent_directories=UpperCamelCase__ )
__lowerCamelCase = {
'repo_id': str(UpperCamelCase__ ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
'hostname': str(socket.gethostname() ),
}
return repo_infos
def lowerCamelCase_ ( UpperCamelCase__ : Callable , UpperCamelCase__ : Iterable ) -> List:
"""simple docstring"""
return list(map(UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
with open(UpperCamelCase__ , 'wb' ) as f:
return pickle.dump(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
def remove_articles(UpperCamelCase__ : str ):
return re.sub(R'\b(a|an|the)\b' , ' ' , UpperCamelCase__ )
def white_space_fix(UpperCamelCase__ : Any ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase__ : Optional[Any] ):
__lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase__ : Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = normalize_answer(UpperCamelCase__ ).split()
__lowerCamelCase = normalize_answer(UpperCamelCase__ ).split()
__lowerCamelCase = Counter(UpperCamelCase__ ) & Counter(UpperCamelCase__ )
__lowerCamelCase = sum(common.values() )
if num_same == 0:
return 0
__lowerCamelCase = 1.0 * num_same / len(UpperCamelCase__ )
__lowerCamelCase = 1.0 * num_same / len(UpperCamelCase__ )
__lowerCamelCase = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] ) -> Dict:
"""simple docstring"""
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
__lowerCamelCase = 0
for hypo, pred in zip(UpperCamelCase__ , UpperCamelCase__ ):
em += exact_match_score(UpperCamelCase__ , UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
em /= len(UpperCamelCase__ )
return {"em": em}
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] ) -> Tuple:
"""simple docstring"""
return model_prefix.startswith('rag' )
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__lowerCamelCase = 'dropout_rate'
for p in extra_params:
if getattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if not hasattr(UpperCamelCase__ , UpperCamelCase__ ) and not hasattr(UpperCamelCase__ , equivalent_param[p] ):
logger.info('config doesn\'t have a `{}` attribute'.format(UpperCamelCase__ ) )
delattr(UpperCamelCase__ , UpperCamelCase__ )
continue
__lowerCamelCase = p if hasattr(UpperCamelCase__ , UpperCamelCase__ ) else equivalent_param[p]
setattr(UpperCamelCase__ , UpperCamelCase__ , getattr(UpperCamelCase__ , UpperCamelCase__ ) )
delattr(UpperCamelCase__ , UpperCamelCase__ )
return hparams, config
| 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
snake_case_ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
snake_case_ = "question"
snake_case_ = "context"
snake_case_ = "answers"
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 348 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__A = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] ) -> str:
"""simple docstring"""
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
__lowerCamelCase = None
for name, value in fairseq_dict.items():
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase = True
elif name.split('.' )[0] == "proj":
__lowerCamelCase = fairseq_model.proj
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCamelCase = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCamelCase = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase = 'weight_v'
elif "bias" in name:
__lowerCamelCase = 'bias'
elif "weight" in name:
__lowerCamelCase = 'weight'
else:
__lowerCamelCase = None
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
continue
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
return proj_weight
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = full_name.split('conv_layers.' )[-1]
__lowerCamelCase = name.split('.' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = emb.weight.shape
__lowerCamelCase = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
__lowerCamelCase = emb.weight.data
return lin_layer
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
with open(UpperCamelCase__ , 'r' , encoding='utf-8' ) as f:
__lowerCamelCase = f.readlines()
__lowerCamelCase = [line.split(' ' )[0] for line in lines]
__lowerCamelCase = len(UpperCamelCase__ )
__lowerCamelCase = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(UpperCamelCase__ , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = WavaVecaConfig.from_pretrained(UpperCamelCase__ )
__lowerCamelCase = SpeechaTextaConfig.from_pretrained(
UpperCamelCase__ , vocab_size=UpperCamelCase__ , decoder_layers=UpperCamelCase__ , do_stable_layer_norm=UpperCamelCase__ )
__lowerCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
__lowerCamelCase = model[0].eval()
# set weights for wav2vec2 encoder
__lowerCamelCase = WavaVecaModel(UpperCamelCase__ )
__lowerCamelCase = recursively_load_weights_wavaveca(model.encoder , UpperCamelCase__ )
__lowerCamelCase = SpeechaTextaForCausalLM(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=UpperCamelCase__ )
# set output linear layer
unexpected_keys.remove('embed_out' )
__lowerCamelCase = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
__lowerCamelCase = SpeechEncoderDecoderModel(encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
__lowerCamelCase = False
# add projection layer
__lowerCamelCase = nn.Parameter(projection_layer.weight )
__lowerCamelCase = nn.Parameter(projection_layer.bias )
__lowerCamelCase = create_vocab_dict(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__ , 'vocab.json' ) , 'w' ) as fp:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = SpeechaTextaTokenizer(os.path.join(UpperCamelCase__ , 'vocab.json' ) )
tokenizer.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = hf_wavavec.config.to_dict()
__lowerCamelCase = tokenizer.pad_token_id
__lowerCamelCase = tokenizer.bos_token_id
__lowerCamelCase = tokenizer.eos_token_id
__lowerCamelCase = 'speech_to_text_2'
__lowerCamelCase = 'wav2vec2'
__lowerCamelCase = SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__ )
hf_wavavec.save_pretrained(UpperCamelCase__ )
feature_extractor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=1_02_24, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__A = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] ) -> bool:
"""simple docstring"""
if len(UpperCamelCase__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater than 0' )
__lowerCamelCase = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__A = logging.get_logger(__name__)
__A = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[List[float]] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
else:
return _interleave_iterable_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
else:
return _concatenate_iterable_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
| 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : int ) -> List[str]:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
if dist[i][j] != float('inf' ):
print(int(dist[i][j] ) , end='\t' )
else:
print('INF' , end='\t' )
print()
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = [[float('inf' ) for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
__lowerCamelCase = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(UpperCamelCase__ ):
# looping through rows of graph array
for i in range(UpperCamelCase__ ):
# looping through columns of graph array
for j in range(UpperCamelCase__ ):
if (
dist[i][k] != float('inf' )
and dist[k][j] != float('inf' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
__lowerCamelCase = dist[i][k] + dist[k][j]
_print_dist(UpperCamelCase__ , UpperCamelCase__ )
return dist, v
if __name__ == "__main__":
__A = int(input("Enter number of vertices: "))
__A = int(input("Enter number of edges: "))
__A = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
__A = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
__A = int(input("Enter source:"))
__A = int(input("Enter destination:"))
__A = float(input("Enter weight:"))
__A = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = ["model.decoder.embed_positions.weights"]
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if "emb" in name:
__lowerCamelCase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__lowerCamelCase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__lowerCamelCase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__lowerCamelCase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__lowerCamelCase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__lowerCamelCase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__lowerCamelCase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__lowerCamelCase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def lowerCamelCase_ ( UpperCamelCase__ : OrderedDict , UpperCamelCase__ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = rename_keys(UpperCamelCase__ )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
__lowerCamelCase = 1024
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 1536
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 2048
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=UpperCamelCase__ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCamelCase__ , num_attention_heads=UpperCamelCase__ , )
return config
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]="cpu" ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = MusicGen.get_pretrained(UpperCamelCase__ , device=UpperCamelCase__ )
__lowerCamelCase = decoder_config_from_checkpoint(UpperCamelCase__ )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase , __lowerCamelCase = rename_state_dict(
UpperCamelCase__ , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('t5-base' )
__lowerCamelCase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__lowerCamelCase = MusicgenForCausalLM(UpperCamelCase__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase , __lowerCamelCase = decoder.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ , audio_encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCamelCase__ )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('t5-base' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# set the appropriate bos/pad token ids
__lowerCamelCase = 2048
__lowerCamelCase = 2048
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCamelCase__ )
processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__A = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 348 | 1 |
import json
import sys
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(UpperCamelCase__ , encoding='utf-8' ) as f:
__lowerCamelCase = json.load(UpperCamelCase__ )
__lowerCamelCase = ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']
for benchmark_name in sorted(UpperCamelCase__ ):
__lowerCamelCase = results[benchmark_name]
__lowerCamelCase = benchmark_name.split('/' )[-1]
output_md.append(F"""### Benchmark: {benchmark_file_name}""" )
__lowerCamelCase = '| metric |'
__lowerCamelCase = '|--------|'
__lowerCamelCase = '| new / old (diff) |'
for metric_name in sorted(UpperCamelCase__ ):
__lowerCamelCase = benchmark_res[metric_name]
__lowerCamelCase = metric_vals['new']
__lowerCamelCase = metric_vals.get('old' , UpperCamelCase__ )
__lowerCamelCase = metric_vals.get('diff' , UpperCamelCase__ )
__lowerCamelCase = F""" {new_val:f}""" if isinstance(UpperCamelCase__ , (int, float) ) else 'None'
if old_val is not None:
val_str += F""" / {old_val:f}""" if isinstance(UpperCamelCase__ , (int, float) ) else "None"
if dif_val is not None:
val_str += F""" ({dif_val:f})""" if isinstance(UpperCamelCase__ , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('</details>' )
with open(UpperCamelCase__ , 'w' , encoding='utf-8' ) as f:
f.writelines('\n'.join(UpperCamelCase__ ) )
if __name__ == "__main__":
__A = sys.argv[1]
__A = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
__lowerCamelCase = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
__lowerCamelCase = torch.zeros(lowerCamelCase__ , lowerCamelCase__ )
else:
__lowerCamelCase = None
__lowerCamelCase = torch.nn.Parameter(lowerCamelCase__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=lowerCamelCase__ , transformer=lowerCamelCase__ , text_encoder=lowerCamelCase__ , tokenizer=lowerCamelCase__ , scheduler=lowerCamelCase__ , learned_classifier_free_sampling_embeddings=lowerCamelCase__ , )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = len(lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else 1
# get prompt text embeddings
__lowerCamelCase = self.tokenizer(
lowerCamelCase__ , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
__lowerCamelCase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__lowerCamelCase = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
__lowerCamelCase = text_input_ids[:, : self.tokenizer.model_max_length]
__lowerCamelCase = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
__lowerCamelCase = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=lowerCamelCase__ )
# duplicate text embeddings for each generation per prompt
__lowerCamelCase = prompt_embeds.repeat_interleave(lowerCamelCase__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
__lowerCamelCase = self.learned_classifier_free_sampling_embeddings.embeddings
__lowerCamelCase = negative_prompt_embeds.unsqueeze(0 ).repeat(lowerCamelCase__ , 1 , 1 )
else:
__lowerCamelCase = [''] * batch_size
__lowerCamelCase = text_input_ids.shape[-1]
__lowerCamelCase = self.tokenizer(
lowerCamelCase__ , padding='max_length' , max_length=lowerCamelCase__ , truncation=lowerCamelCase__ , return_tensors='pt' , )
__lowerCamelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
__lowerCamelCase = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=lowerCamelCase__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__lowerCamelCase = negative_prompt_embeds.shape[1]
__lowerCamelCase = negative_prompt_embeds.repeat(1 , lowerCamelCase__ , 1 )
__lowerCamelCase = negative_prompt_embeds.view(batch_size * num_images_per_prompt , lowerCamelCase__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__lowerCamelCase = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , lowerCamelCase__ , lowerCamelCase__ = 100 , lowerCamelCase__ = 5.0 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = 1 , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "pil" , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = 1
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = len(lowerCamelCase__ )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__ )}""" )
__lowerCamelCase = batch_size * num_images_per_prompt
__lowerCamelCase = guidance_scale > 1.0
__lowerCamelCase = self._encode_prompt(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(lowerCamelCase__ )}.""" )
# get the initial completely masked latents unless the user supplied it
__lowerCamelCase = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
__lowerCamelCase = self.transformer.num_vector_embeds - 1
__lowerCamelCase = torch.full(lowerCamelCase__ , lowerCamelCase__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
f""" {self.transformer.num_vector_embeds - 1} (inclusive).""" )
__lowerCamelCase = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(lowerCamelCase__ , device=self.device )
__lowerCamelCase = self.scheduler.timesteps.to(self.device )
__lowerCamelCase = latents
for i, t in enumerate(self.progress_bar(lowerCamelCase__ ) ):
# expand the sample if we are doing classifier free guidance
__lowerCamelCase = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
__lowerCamelCase = self.transformer(lowerCamelCase__ , encoder_hidden_states=lowerCamelCase__ , timestep=lowerCamelCase__ ).sample
if do_classifier_free_guidance:
__lowerCamelCase , __lowerCamelCase = model_output.chunk(2 )
__lowerCamelCase = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(lowerCamelCase__ , dim=1 , keepdim=lowerCamelCase__ )
__lowerCamelCase = self.truncate(lowerCamelCase__ , lowerCamelCase__ )
# remove `log(0)`'s (`-inf`s)
__lowerCamelCase = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
__lowerCamelCase = self.scheduler.step(lowerCamelCase__ , timestep=lowerCamelCase__ , sample=lowerCamelCase__ , generator=lowerCamelCase__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = self.vqvae.config.vq_embed_dim
__lowerCamelCase = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
__lowerCamelCase = self.vqvae.quantize.get_codebook_entry(lowerCamelCase__ , shape=lowerCamelCase__ )
__lowerCamelCase = self.vqvae.decode(lowerCamelCase__ , force_not_quantize=lowerCamelCase__ ).sample
__lowerCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
__lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase = self.numpy_to_pil(lowerCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> torch.FloatTensor:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = torch.sort(lowerCamelCase__ , 1 , descending=lowerCamelCase__ )
__lowerCamelCase = torch.exp(lowerCamelCase__ )
__lowerCamelCase = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
__lowerCamelCase = torch.full_like(keep_mask[:, 0:1, :] , lowerCamelCase__ )
__lowerCamelCase = torch.cat((all_true, keep_mask) , dim=1 )
__lowerCamelCase = keep_mask[:, :-1, :]
__lowerCamelCase = keep_mask.gather(1 , indices.argsort(1 ) )
__lowerCamelCase = log_p_x_0.clone()
__lowerCamelCase = -torch.inf # -inf = log(0)
return rv
| 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__A = logging.get_logger("transformers.models.speecht5")
__A = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__A = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__A = []
__A = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
__A = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] ) -> Dict:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCamelCase = value
elif weight_type == "weight_g":
__lowerCamelCase = value
elif weight_type == "weight_v":
__lowerCamelCase = value
elif weight_type == "bias":
__lowerCamelCase = value
elif weight_type == "running_mean":
__lowerCamelCase = value
elif weight_type == "running_var":
__lowerCamelCase = value
elif weight_type == "num_batches_tracked":
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
if task == "s2t":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2T
__lowerCamelCase = IGNORE_KEYS_S2T
elif task == "t2s":
__lowerCamelCase = None
__lowerCamelCase = MAPPING_T2S
__lowerCamelCase = IGNORE_KEYS_T2S
elif task == "s2s":
__lowerCamelCase = hf_model.speechta.encoder.prenet.feature_encoder
__lowerCamelCase = MAPPING_S2S
__lowerCamelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(UpperCamelCase__ , UpperCamelCase__ ):
logger.info(F"""{name} was ignored""" )
continue
__lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
__lowerCamelCase , __lowerCamelCase = key.split('.*.' )
if prefix in name and suffix in name:
__lowerCamelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
__lowerCamelCase = True
if "*" in mapped_key:
__lowerCamelCase = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCamelCase = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCamelCase = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase = 'weight_v'
elif "bias" in name:
__lowerCamelCase = 'bias'
elif "weight" in name:
__lowerCamelCase = 'weight'
elif "running_mean" in name:
__lowerCamelCase = 'running_mean'
elif "running_var" in name:
__lowerCamelCase = 'running_var'
elif "num_batches_tracked" in name:
__lowerCamelCase = 'num_batches_tracked'
else:
__lowerCamelCase = None
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
continue
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
__lowerCamelCase = full_name.split('conv_layers.' )[-1]
__lowerCamelCase = name.split('.' )
__lowerCamelCase = int(items[0] )
__lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCamelCase__ )
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : str=None , UpperCamelCase__ : List[str]=None , ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__lowerCamelCase = SpeechTaConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCamelCase = SpeechTaConfig()
if task == "s2t":
__lowerCamelCase = config.max_text_positions
__lowerCamelCase = SpeechTaForSpeechToText(UpperCamelCase__ )
elif task == "t2s":
__lowerCamelCase = 1876
__lowerCamelCase = 600
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForTextToSpeech(UpperCamelCase__ )
elif task == "s2s":
__lowerCamelCase = 1876
__lowerCamelCase = config.max_speech_positions
__lowerCamelCase = SpeechTaForSpeechToSpeech(UpperCamelCase__ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
__lowerCamelCase = SpeechTaTokenizer(UpperCamelCase__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
__lowerCamelCase = AddedToken('<mask>' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
__lowerCamelCase = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
__lowerCamelCase = SpeechTaFeatureExtractor()
__lowerCamelCase = SpeechTaProcessor(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = torch.load(UpperCamelCase__ )
recursively_load_weights(fairseq_checkpoint['model'] , UpperCamelCase__ , UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
if repo_id:
print('Pushing to the hub...' )
processor.push_to_hub(UpperCamelCase__ )
model.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__A = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["ConditionalDetrFeatureExtractor"]
__A = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = [False] * len(UpperCamelCase__ )
__lowerCamelCase = [-1] * len(UpperCamelCase__ )
def dfs(UpperCamelCase__ : int , UpperCamelCase__ : int ):
__lowerCamelCase = True
__lowerCamelCase = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCamelCase__ , 1 - c )
for i in range(len(UpperCamelCase__ ) ):
if not visited[i]:
dfs(UpperCamelCase__ , 0 )
for i in range(len(UpperCamelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__A = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = ["model.decoder.embed_positions.weights"]
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
if "emb" in name:
__lowerCamelCase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__lowerCamelCase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__lowerCamelCase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__lowerCamelCase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__lowerCamelCase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__lowerCamelCase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__lowerCamelCase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__lowerCamelCase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__lowerCamelCase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__lowerCamelCase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCamelCase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def lowerCamelCase_ ( UpperCamelCase__ : OrderedDict , UpperCamelCase__ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
__lowerCamelCase = list(state_dict.keys() )
__lowerCamelCase = {}
for key in keys:
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = rename_keys(UpperCamelCase__ )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCamelCase = val[:hidden_size, :]
__lowerCamelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCamelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCamelCase = val
else:
__lowerCamelCase = val
return state_dict, enc_dec_proj_state_dict
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
__lowerCamelCase = 1024
__lowerCamelCase = 24
__lowerCamelCase = 16
elif checkpoint == "medium":
__lowerCamelCase = 1536
__lowerCamelCase = 48
__lowerCamelCase = 24
elif checkpoint == "large":
__lowerCamelCase = 2048
__lowerCamelCase = 48
__lowerCamelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
__lowerCamelCase = MusicgenDecoderConfig(
hidden_size=UpperCamelCase__ , ffn_dim=hidden_size * 4 , num_hidden_layers=UpperCamelCase__ , num_attention_heads=UpperCamelCase__ , )
return config
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]="cpu" ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = MusicGen.get_pretrained(UpperCamelCase__ , device=UpperCamelCase__ )
__lowerCamelCase = decoder_config_from_checkpoint(UpperCamelCase__ )
__lowerCamelCase = fairseq_model.lm.state_dict()
__lowerCamelCase , __lowerCamelCase = rename_state_dict(
UpperCamelCase__ , hidden_size=decoder_config.hidden_size )
__lowerCamelCase = TaEncoderModel.from_pretrained('t5-base' )
__lowerCamelCase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__lowerCamelCase = MusicgenForCausalLM(UpperCamelCase__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCamelCase , __lowerCamelCase = decoder.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(UpperCamelCase__ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
__lowerCamelCase = MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__ , audio_encoder=UpperCamelCase__ , decoder=UpperCamelCase__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(UpperCamelCase__ )
# check we can do a forward pass
__lowerCamelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCamelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCamelCase = model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__lowerCamelCase = AutoTokenizer.from_pretrained('t5-base' )
__lowerCamelCase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__lowerCamelCase = MusicgenProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# set the appropriate bos/pad token ids
__lowerCamelCase = 2048
__lowerCamelCase = 2048
# set other default generation config params
__lowerCamelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCamelCase = True
__lowerCamelCase = 3.0
if pytorch_dump_folder is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(UpperCamelCase__ )
processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__A = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
__lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = tempfile.mkdtemp()
# fmt: off
__lowerCamelCase = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
__lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
__lowerCamelCase = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
__lowerCamelCase = {'unk_token': '<unk>'}
__lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCamelCase__ ) )
__lowerCamelCase = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
__lowerCamelCase = os.path.join(self.tmpdirname , lowerCamelCase__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def lowercase_ ( self , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def lowercase_ ( self , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__lowerCamelCase = [Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = self.get_rust_tokenizer()
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
__lowerCamelCase = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase__ )
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
__lowerCamelCase = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCamelCase__ )
self.assertIsInstance(processor_fast.image_processor , lowerCamelCase__ )
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__lowerCamelCase = self.get_image_processor(do_normalize=lowerCamelCase__ , padding_value=1.0 )
__lowerCamelCase = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowerCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='np' )
__lowerCamelCase = processor(images=lowerCamelCase__ , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__lowerCamelCase = 'lower newer'
__lowerCamelCase = processor(text=lowerCamelCase__ )
__lowerCamelCase = tokenizer(lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__lowerCamelCase = 'lower newer'
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase__ ):
processor()
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__lowerCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowerCamelCase = processor.batch_decode(lowerCamelCase__ )
__lowerCamelCase = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.get_image_processor()
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = CLIPProcessor(tokenizer=lowerCamelCase__ , image_processor=lowerCamelCase__ )
__lowerCamelCase = 'lower newer'
__lowerCamelCase = self.prepare_image_inputs()
__lowerCamelCase = processor(text=lowerCamelCase__ , images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
__lowerCamelCase = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
__lowerCamelCase = [5, 5, 5, 5]
elif "fl4" in model_name:
__lowerCamelCase = [4, 4, 4, 4]
__lowerCamelCase = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
if "lrf" in model_name:
__lowerCamelCase = [3, 3, 3, 3]
else:
__lowerCamelCase = [2, 2, 2, 2]
if "tiny" in model_name:
__lowerCamelCase = 96
elif "small" in model_name:
__lowerCamelCase = 96
elif "base" in model_name:
__lowerCamelCase = 128
elif "large" in model_name:
__lowerCamelCase = 192
elif "xlarge" in model_name:
__lowerCamelCase = 256
elif "huge" in model_name:
__lowerCamelCase = 352
# set label information
__lowerCamelCase = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
__lowerCamelCase = 'imagenet-22k-id2label.json'
else:
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = {v: k for k, v in idalabel.items()}
__lowerCamelCase = FocalNetConfig(
embed_dim=UpperCamelCase__ , depths=UpperCamelCase__ , focal_levels=UpperCamelCase__ , focal_windows=UpperCamelCase__ , use_conv_embed=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , use_post_layernorm=UpperCamelCase__ , use_layerscale=UpperCamelCase__ , )
return config
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
if "patch_embed.proj" in name:
__lowerCamelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__lowerCamelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
__lowerCamelCase = 'encoder.' + name
if "encoder.layers" in name:
__lowerCamelCase = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
__lowerCamelCase = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
__lowerCamelCase = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__lowerCamelCase = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__lowerCamelCase = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__lowerCamelCase = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
__lowerCamelCase = 'layernorm.weight'
if name == "norm.bias":
__lowerCamelCase = 'layernorm.bias'
if "head" in name:
__lowerCamelCase = name.replace('head' , 'classifier' )
else:
__lowerCamelCase = 'focalnet.' + name
return name
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple=False ) -> Dict:
"""simple docstring"""
__lowerCamelCase = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
__lowerCamelCase = model_name_to_url[model_name]
print('Checkpoint URL: ' , UpperCamelCase__ )
__lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase = state_dict.pop(UpperCamelCase__ )
__lowerCamelCase = val
__lowerCamelCase = get_focalnet_config(UpperCamelCase__ )
__lowerCamelCase = FocalNetForImageClassification(UpperCamelCase__ )
model.eval()
# load state dict
model.load_state_dict(UpperCamelCase__ )
# verify conversion
__lowerCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase = BitImageProcessor(
do_resize=UpperCamelCase__ , size={'shortest_edge': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=UpperCamelCase__ , crop_size=224 , do_normalize=UpperCamelCase__ , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ , )
__lowerCamelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
__lowerCamelCase = processor(images=UpperCamelCase__ , return_tensors='pt' )
__lowerCamelCase = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ),
] )
__lowerCamelCase = image_transforms(UpperCamelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , UpperCamelCase__ , atol=1E-4 )
__lowerCamelCase = model(**UpperCamelCase__ )
__lowerCamelCase = outputs.logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
print('First values of logits:' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__lowerCamelCase = torch.tensor([0.21_66, -0.43_68, 0.21_91] )
elif model_name == "focalnet-tiny-lrf":
__lowerCamelCase = torch.tensor([1.16_69, 0.01_25, -0.16_95] )
elif model_name == "focalnet-small":
__lowerCamelCase = torch.tensor([0.49_17, -0.04_30, 0.13_41] )
elif model_name == "focalnet-small-lrf":
__lowerCamelCase = torch.tensor([-0.25_88, -0.53_42, -0.23_31] )
elif model_name == "focalnet-base":
__lowerCamelCase = torch.tensor([-0.16_55, -0.40_90, -0.17_30] )
elif model_name == "focalnet-base-lrf":
__lowerCamelCase = torch.tensor([0.53_06, -0.04_83, -0.39_28] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(F"""{model_name}""" )
processor.push_to_hub(F"""{model_name}""" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
__A = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''mra'''
def __init__( self , lowerCamelCase__=50_265 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-5 , lowerCamelCase__="absolute" , lowerCamelCase__=4 , lowerCamelCase__="full" , lowerCamelCase__=0 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=0 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ , **lowerCamelCase__ )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = type_vocab_size
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = position_embedding_type
__lowerCamelCase = block_per_row
__lowerCamelCase = approx_mode
__lowerCamelCase = initial_prior_first_n_blocks
__lowerCamelCase = initial_prior_diagonal_n_blocks
| 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __lowerCamelCase = divmod(len(UpperCamelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = [float(x) for x in input("Enter the elements of first array: ").split()]
__A = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 348 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any , ) -> Any:
"""simple docstring"""
__lowerCamelCase = {
'7z': (seven_zip_file, SevenZipExtractor),
'bz2': (bza_file, BzipaExtractor),
'gzip': (gz_file, GzipExtractor),
'lz4': (lza_file, LzaExtractor),
'tar': (tar_file, TarExtractor),
'xz': (xz_file, XzExtractor),
'zip': (zip_file, ZipExtractor),
'zstd': (zstd_file, ZstdExtractor),
}
__lowerCamelCase , __lowerCamelCase = input_paths_and_base_extractors[compression_format]
if input_path is None:
__lowerCamelCase = F"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(UpperCamelCase__ )
assert base_extractor.is_extractable(UpperCamelCase__ )
__lowerCamelCase = tmp_path / ('extracted' if is_archive else 'extracted.txt')
base_extractor.extract(UpperCamelCase__ , UpperCamelCase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
__lowerCamelCase = file_path.read_text(encoding='utf-8' )
else:
__lowerCamelCase = output_path.read_text(encoding='utf-8' )
__lowerCamelCase = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'compression_format, is_archive' , [
('7z', True),
('bz2', False),
('gzip', False),
('lz4', False),
('tar', True),
('xz', False),
('zip', True),
('zstd', False),
] , )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any , ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = {
'7z': seven_zip_file,
'bz2': bza_file,
'gzip': gz_file,
'lz4': lza_file,
'tar': tar_file,
'xz': xz_file,
'zip': zip_file,
'zstd': zstd_file,
}
__lowerCamelCase = input_paths[compression_format]
if input_path is None:
__lowerCamelCase = F"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(UpperCamelCase__ )
__lowerCamelCase = Extractor.infer_extractor_format(UpperCamelCase__ )
assert extractor_format is not None
__lowerCamelCase = tmp_path / ('extracted' if is_archive else 'extracted.txt')
Extractor.extract(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
__lowerCamelCase = file_path.read_text(encoding='utf-8' )
else:
__lowerCamelCase = output_path.read_text(encoding='utf-8' )
__lowerCamelCase = text_file.read_text(encoding='utf-8' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any ) -> Optional[int]:
"""simple docstring"""
import tarfile
__lowerCamelCase = tmp_path / 'data_dot_dot'
directory.mkdir()
__lowerCamelCase = directory / 'tar_file_with_dot_dot.tar'
with tarfile.TarFile(UpperCamelCase__ , 'w' ) as f:
f.add(UpperCamelCase__ , arcname=os.path.join('..' , text_file.name ) )
return path
@pytest.fixture
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
"""simple docstring"""
import tarfile
__lowerCamelCase = tmp_path / 'data_sym_link'
directory.mkdir()
__lowerCamelCase = directory / 'tar_file_with_sym_link.tar'
os.symlink('..' , directory / 'subdir' , target_is_directory=UpperCamelCase__ )
with tarfile.TarFile(UpperCamelCase__ , 'w' ) as f:
f.add(str(directory / 'subdir' ) , arcname='subdir' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'insecure_tar_file, error_log' , [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')] , )
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : str ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = {
'tar_file_with_dot_dot': tar_file_with_dot_dot,
'tar_file_with_sym_link': tar_file_with_sym_link,
}
__lowerCamelCase = insecure_tar_files[insecure_tar_file]
__lowerCamelCase = tmp_path / 'extracted'
TarExtractor.extract(UpperCamelCase__ , UpperCamelCase__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = tmpdir / 'not_a_zip_file'
# From: https://github.com/python/cpython/pull/5053
__lowerCamelCase = (
b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
b'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
b'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
b'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
)
with not_a_zip_file.open('wb' ) as f:
f.write(UpperCamelCase__ )
assert zipfile.is_zipfile(str(UpperCamelCase__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(UpperCamelCase__ ) # but we're right
| 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 10_55.0_55_85,
"footpound": 1.3_5_5_8_1_8,
}
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
__lowerCamelCase = (
F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {', '.join(UpperCamelCase__ )}"""
)
raise ValueError(UpperCamelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : int | None = None , UpperCamelCase__ : int | None = None ) -> None:
"""simple docstring"""
if start is None:
__lowerCamelCase = 0
if end is None:
__lowerCamelCase = len(UpperCamelCase__ ) - 1
if start >= end:
return
__lowerCamelCase = (start + end) // 2
slowsort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
slowsort(UpperCamelCase__ , mid + 1 , UpperCamelCase__ )
if sequence[end] < sequence[mid]:
__lowerCamelCase , __lowerCamelCase = sequence[mid], sequence[end]
slowsort(UpperCamelCase__ , UpperCamelCase__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''philschmid/bart-large-cnn-samsum'''
snake_case_ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
snake_case_ = '''summarizer'''
snake_case_ = AutoTokenizer
snake_case_ = AutoModelForSeqaSeqLM
snake_case_ = ['''text''']
snake_case_ = ['''text''']
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
return self.model.generate(**lowerCamelCase__ )[0]
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
| 348 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = 42
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
@register_to_config
def __init__( self , lowerCamelCase__ = 32 , lowerCamelCase__ = 64 , lowerCamelCase__ = 20 , lowerCamelCase__ = 768 , lowerCamelCase__=77 , lowerCamelCase__=4 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = "silu" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = "linear" , lowerCamelCase__ = "prd" , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowerCamelCase = num_attention_heads
__lowerCamelCase = attention_head_dim
__lowerCamelCase = num_attention_heads * attention_head_dim
__lowerCamelCase = additional_embeddings
__lowerCamelCase = time_embed_dim or inner_dim
__lowerCamelCase = embedding_proj_dim or embedding_dim
__lowerCamelCase = clip_embed_dim or embedding_dim
__lowerCamelCase = Timesteps(lowerCamelCase__ , lowerCamelCase__ , 0 )
__lowerCamelCase = TimestepEmbedding(lowerCamelCase__ , lowerCamelCase__ , out_dim=lowerCamelCase__ , act_fn=lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if embedding_proj_norm_type is None:
__lowerCamelCase = None
elif embedding_proj_norm_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
if encoder_hid_proj_type is None:
__lowerCamelCase = None
elif encoder_hid_proj_type == "linear":
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , lowerCamelCase__ ) )
if added_emb_type == "prd":
__lowerCamelCase = nn.Parameter(torch.zeros(1 , 1 , lowerCamelCase__ ) )
elif added_emb_type is None:
__lowerCamelCase = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
__lowerCamelCase = nn.ModuleList(
[
BasicTransformerBlock(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dropout=lowerCamelCase__ , activation_fn='gelu' , attention_bias=lowerCamelCase__ , )
for d in range(lowerCamelCase__ )
] )
if norm_in_type == "layer":
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
elif norm_in_type is None:
__lowerCamelCase = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
__lowerCamelCase = nn.LayerNorm(lowerCamelCase__ )
__lowerCamelCase = nn.Linear(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0 )
causal_attention_mask.triu_(1 )
__lowerCamelCase = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , lowerCamelCase__ , persistent=lowerCamelCase__ )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
__lowerCamelCase = nn.Parameter(torch.zeros(1 , lowerCamelCase__ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase_ ( self ) -> Dict[str, AttentionProcessor]:
'''simple docstring'''
__lowerCamelCase = {}
def fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
__lowerCamelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return processors
def lowercase_ ( self , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = len(self.attn_processors.keys() )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if hasattr(lowerCamelCase__ , 'set_processor' ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
module.set_processor(lowerCamelCase__ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , lowerCamelCase__ , lowerCamelCase__ )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = True , ) -> int:
'''simple docstring'''
__lowerCamelCase = hidden_states.shape[0]
__lowerCamelCase = timestep
if not torch.is_tensor(lowerCamelCase__ ):
__lowerCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
__lowerCamelCase = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__lowerCamelCase = timesteps * torch.ones(lowerCamelCase__ , dtype=timesteps.dtype , device=timesteps.device )
__lowerCamelCase = self.time_proj(lowerCamelCase__ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__lowerCamelCase = timesteps_projected.to(dtype=self.dtype )
__lowerCamelCase = self.time_embedding(lowerCamelCase__ )
if self.embedding_proj_norm is not None:
__lowerCamelCase = self.embedding_proj_norm(lowerCamelCase__ )
__lowerCamelCase = self.embedding_proj(lowerCamelCase__ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__lowerCamelCase = self.encoder_hidden_states_proj(lowerCamelCase__ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
__lowerCamelCase = self.proj_in(lowerCamelCase__ )
__lowerCamelCase = self.positional_embedding.to(hidden_states.dtype )
__lowerCamelCase = []
__lowerCamelCase = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCamelCase__ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__lowerCamelCase = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__lowerCamelCase = hidden_states[:, None, :]
__lowerCamelCase = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__lowerCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCamelCase__ , -1 , -1 )
additional_embeds.append(lowerCamelCase__ )
__lowerCamelCase = torch.cat(
lowerCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__lowerCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__lowerCamelCase = F.pad(
lowerCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__lowerCamelCase = hidden_states + positional_embeddings
if attention_mask is not None:
__lowerCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
__lowerCamelCase = F.pad(lowerCamelCase__ , (0, self.additional_embeddings) , value=0.0 )
__lowerCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__lowerCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__lowerCamelCase = self.norm_in(lowerCamelCase__ )
for block in self.transformer_blocks:
__lowerCamelCase = block(lowerCamelCase__ , attention_mask=lowerCamelCase__ )
__lowerCamelCase = self.norm_out(lowerCamelCase__ )
if self.prd_embedding is not None:
__lowerCamelCase = hidden_states[:, -1]
else:
__lowerCamelCase = hidden_states[:, additional_embeddings_len:]
__lowerCamelCase = self.proj_to_clip_embeddings(lowerCamelCase__ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=13 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=16 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_choices
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = True
__lowerCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = True
snake_case_ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__lowerCamelCase = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
__lowerCamelCase = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , lowerCamelCase__ )
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=lowerCamelCase__ )
__lowerCamelCase = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__lowerCamelCase = model(lowerCamelCase__ )[0]
# compare the actual values for a slice.
__lowerCamelCase = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 348 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' )
__lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids
__lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
__lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss
__lowerCamelCase = -(labels.shape[-1] * loss.item())
__lowerCamelCase = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> str:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> int:
'''simple docstring'''
requires_backends(self , ['sentencepiece'] )
| 348 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=99 , lowerCamelCase__=13 , lowerCamelCase__=16 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=2 , lowerCamelCase__=32 , lowerCamelCase__=4 , lowerCamelCase__=4 , lowerCamelCase__=30 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , lowerCamelCase__=None , ) -> str:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = decoder_seq_length
# For common tests
__lowerCamelCase = self.decoder_seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_attention_mask
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = d_model
__lowerCamelCase = d_model
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_layers
__lowerCamelCase = decoder_ffn_dim
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = decoder_attention_heads
__lowerCamelCase = eos_token_id
__lowerCamelCase = bos_token_id
__lowerCamelCase = pad_token_id
__lowerCamelCase = decoder_start_token_id
__lowerCamelCase = use_cache
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = None
__lowerCamelCase = decoder_seq_length
__lowerCamelCase = 2
__lowerCamelCase = 1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_attention_mask:
__lowerCamelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
__lowerCamelCase = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> Any:
'''simple docstring'''
__lowerCamelCase = True
__lowerCamelCase = TrOCRDecoder(config=lowerCamelCase__ ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
__lowerCamelCase = model(lowerCamelCase__ , use_cache=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , use_cache=lowerCamelCase__ )
self.parent.assertTrue(len(lowerCamelCase__ ) == len(lowerCamelCase__ ) )
self.parent.assertTrue(len(lowerCamelCase__ ) == len(lowerCamelCase__ ) + 1 )
__lowerCamelCase = outputs['past_key_values']
# create hypothetical next token and extent to next_input_ids
__lowerCamelCase = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
__lowerCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCamelCase = model(lowerCamelCase__ )['last_hidden_state']
__lowerCamelCase = model(lowerCamelCase__ , past_key_values=lowerCamelCase__ )['last_hidden_state']
# select random slice
__lowerCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCamelCase = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
__lowerCamelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = config_and_inputs
__lowerCamelCase = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
snake_case_ = (TrOCRForCausalLM,) if is_torch_available() else ()
snake_case_ = {'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
snake_case_ = True
snake_case_ = False
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = TrOCRStandaloneDecoderModelTester(self , is_training=lowerCamelCase__ )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase__ )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
| 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> int:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = np.random.RandomState(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
# warmup pass to apply optimizations
__lowerCamelCase = pipe(**self.get_dummy_inputs() )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowerCamelCase = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> int:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((768, 512) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__lowerCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCamelCase__ , safety_checker=lowerCamelCase__ , feature_extractor=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = np.random.RandomState(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__lowerCamelCase = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
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