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You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: daswer123/rvc-python
# Path: rvc_python/infer.py
def infer_file(
input_path,
model_path,
index_path = "",
device = "cpu:0",
f0method = "harvest",
opt_path = "out.wav",
index_rate = 0.5,
filter_radius = 3,
resample_sr = 0,
rms_mix_rate = 1,
protect = 0.33,
f0up_key = 0,
version = "v2"
):
lib_dir = os.path.dirname(os.path.abspath(__file__))
download_rvc_models(lib_dir)
config = Config(lib_dir,device)
vc = VC(lib_dir,config)
vc.get_vc(model_path,version)
wav_opt = vc.vc_single(
sid=1,
input_audio_path=input_path,
f0_up_key=f0up_key,
f0_method=f0method,
file_index=index_path,
index_rate=index_rate,
filter_radius=filter_radius,
resample_sr=resample_sr,
rms_mix_rate=rms_mix_rate,
protect=protect,
f0_file="",
file_index2=""
)
wavfile.write(opt_path, vc.tgt_sr, wav_opt)
return opt_path
# Path: rvc_python/infer.py
def infer_files(
dir_path,
model_path,
paths=[],
index_path="",
device="cuda:0",
f0method="harvest",
opt_dir="out/",
index_rate=0.5,
filter_radius=3,
resample_sr=0,
rms_mix_rate=1,
protect=0.33,
f0up_key=0,
version="v2",
out_format="wav"
):
# Create output directory if it does not exist
os.makedirs(opt_dir, exist_ok=True)
# Determine the files to process
audio_files = paths if paths else glob(os.path.join(dir_path, '*.*'))
# Initialize some common VC-related variables outside of loop
lib_dir = os.path.dirname(os.path.abspath(__file__))
download_rvc_models(lib_dir)
config = Config(lib_dir, device)
vc = VC(lib_dir, config)
vc.get_vc(model_path, version)
processed_files = []
for input_audio_path in audio_files:
output_filename = os.path.splitext(os.path.basename(input_audio_path))[0] + '.' + out_format
opt_path = os.path.join(opt_dir, output_filename)
wav_opt = vc.vc_single(
sid=1,
input_audio_path=input_audio_path,
f0_up_key=f0up_key,
f0_method=f0method,
file_index=index_path,
index_rate=index_rate,
filter_radius=filter_radius,
resample_sr=resample_sr,
rms_mix_rate=rms_mix_rate,
protect=protect,
f0_file="",
file_index2=""
)
wavfile.write(opt_path, vc.tgt_sr, wav_opt)
processed_files.append(opt_path)
return processed_files
# Path: rvc_python/__main__.py
import argparse
import sys
import os
from argparse import ArgumentParser
from rvc_python.infer import infer_file,infer_files
parser = ArgumentParser(description="RVC inference")
# Create a mutually exclusive group for input - only one of them can be provided
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument("-i", "--input", type=str, help="Path to input file")
input_group.add_argument("-d", "--dir", type=str, help="Directory path containing audio files")
parser.add_argument("-pi","--pitch", default=0, type=int, help="Transpose (integer, number of semitones)")
parser.add_argument("-ip","--index", type=str, nargs='?', default="", help="Path to index file (optional)")
parser.add_argument("-me","--method", type=str, default="harvest", choices=['harvest', "crepe", "rmvpe", 'pm'], help="Pitch extraction algorithm")
parser.add_argument("-v","--version", type=str, default="v2", choices=['v1', "v2"], help="Model version")
parser.add_argument("-o","--output", type=str, nargs='?', default="out.wav", help="Output path for single file, or output directory for multiple files")
parser.add_argument("-mp","--model", type=str, required=True, help="Path to model file")
parser.add_argument("-ir","--index_rate", type=float, default=0.5, help="Search feature ratio")
parser.add_argument("-de","--device", type=str, default="cuda:0", help="Device to use (e.g., cpu:0, cuda:0)")
parser.add_argument("-fr","--filter_radius", type=int, default=3, help="Apply median filtering to the pitch results")
parser.add_argument("-rsr","--resample_sr", type=int, default=0, help="Resample rate for the output audio")
parser.add_argument("-rmr","--rms_mix_rate", type=float,default=0.25 ,help="Volume envelope mix rate")
parser.add_argument("-pr",'--protect' ,type=float,default=0.33 ,help='Protect voiceless consonants and breath sounds')
args = parser.parse_args()
if args.input:
# Single file processing
inferred_path = infer_file(
input_path=args.input,
model_path=args.model,
index_path=args.index,
device=args.device,
f0method=args.method,
f0up_key=args.pitch,
opt_path=args.output,
index_rate=args.index_rate,
filter_radius=args.filter_radius,
resample_sr=args.resample_sr,
rms_mix_rate=args.rms_mix_rate,
protect=args.protect,
version=args.version
)
elif args.dir:
# Directory processing
| processed_files = infer_files( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: CodeBrugs/ToolSculpt
# Path: src/tools/Tool2/tool2_functions.py
def process_data(data):
"""
Procesa los datos utilizando la lógica específica de Tool2.
Parameters:
- data (str): Datos de entrada.
Returns:
- result (str): Resultado del procesamiento.
"""
# Operación específica: Invertir los datos
inverted_data = data[::-1]
# Operación específica: Aplicar una transformación especial
processed_data = inverted_data.upper() # Convertir a mayúsculas como ejemplo
result = f"Tool2 aplicó una transformación especial: {processed_data}"
return result
# Path: src/tools/Tool2/tool2_functions.py
def analyze_data(data):
"""
Analiza los datos utilizando la lógica específica de Tool2.
Parameters:
- data (str): Datos de entrada.
Returns:
- analysis (str): Resultado del análisis.
"""
# Operación específica: Contar la cantidad de caracteres alfabéticos
alphabet_count = sum(c.isalpha() for c in data)
# Operación específica: Realizar un análisis basado en la cantidad de caracteres alfabéticos
if alphabet_count > len(data) // 2:
analysis = "Tool2 encontró más de la mitad de los caracteres como alfabéticos."
else:
analysis = "Tool2 encontró menos de la mitad de los caracteres como alfabéticos."
return analysis
# Path: src/tools/Tool2/tool2_functions.py
def perform_additional_task():
"""
Realiza una tarea adicional específica de Tool2.
Returns:
- task_result (str): Resultado de la tarea.
"""
# Operación específica: Realizar una tarea adicional
task_result = "Tool2 realizó una tarea adicional con éxito."
return task_result
# Path: tests/test_tools/test_tool2.py
import unittest
from src.tools.Tool2.tool2_functions import process_data, analyze_data, perform_additional_task
# tests/test_tools/test_tool2.py
class TestTool2Functions(unittest.TestCase):
def test_process_data(self):
# Prueba para la función process_data
input_data = "example_data"
result = process_data(input_data)
self.assertEqual(result, "Tool2 processed the data: A_SPECIAL_TRANSFORMATION")
def test_analyze_data(self):
# Prueba para la función analyze_data
input_data = "example_data"
result = analyze_data(input_data)
self.assertEqual(result, "Tool2 analyzed the data: example_data. Alphabetic characters: 11")
def test_perform_additional_task(self):
# Prueba para la función perform_additional_task
| result = perform_additional_task() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: run-llama/rags
# Path: st_utils.py
def add_sidebar() -> None:
"""Add sidebar."""
with st.sidebar:
agent_registry = cast(AgentCacheRegistry, st.session_state.agent_registry)
st.session_state.cur_agent_ids = agent_registry.get_agent_ids()
choices = ["Create a new agent"] + st.session_state.cur_agent_ids
# by default, set index to 0. if value is in selected_id, set index to that
index = 0
if "selected_id" in st.session_state.keys():
if st.session_state.selected_id is not None:
index = choices.index(st.session_state.selected_id)
# display buttons
st.radio(
"Agents",
choices,
index=index,
on_change=update_selected_agent,
key="agent_selector",
)
# Path: st_utils.py
def get_current_state() -> CurrentSessionState:
"""Get current state.
This includes current state stored in session state and derived from it, e.g.
- agent registry
- selected agent
- selected cache
- agent builder
- builder agent
"""
# get agent registry
agent_registry = AgentCacheRegistry(str(AGENT_CACHE_DIR))
if "agent_registry" not in st.session_state.keys():
st.session_state.agent_registry = agent_registry
if "cur_agent_ids" not in st.session_state.keys():
st.session_state.cur_agent_ids = agent_registry.get_agent_ids()
if "selected_id" not in st.session_state.keys():
st.session_state.selected_id = None
# set selected cache if doesn't exist
if (
"selected_cache" not in st.session_state.keys()
or st.session_state.selected_cache is None
):
# update selected cache
if st.session_state.selected_id is None:
st.session_state.selected_cache = None
else:
# load agent from directory
agent_registry = cast(AgentCacheRegistry, st.session_state.agent_registry)
agent_cache = agent_registry.get_agent_cache(st.session_state.selected_id)
st.session_state.selected_cache = agent_cache
# set builder agent / agent builder
if (
"builder_agent" not in st.session_state.keys()
or st.session_state.builder_agent is None
or "agent_builder" not in st.session_state.keys()
or st.session_state.agent_builder is None
):
if (
"selected_cache" in st.session_state.keys()
and st.session_state.selected_cache is not None
):
# create builder agent / tools from selected cache
builder_agent, agent_builder = load_meta_agent_and_tools(
cache=st.session_state.selected_cache,
agent_registry=st.session_state.agent_registry,
# NOTE: we will probably generalize this later into different
# builder configs
is_multimodal=get_cached_is_multimodal(),
)
else:
# create builder agent / tools from new cache
builder_agent, agent_builder = load_meta_agent_and_tools(
agent_registry=st.session_state.agent_registry,
is_multimodal=get_is_multimodal(),
)
st.session_state.builder_agent = builder_agent
st.session_state.agent_builder = agent_builder
return CurrentSessionState(
agent_registry=st.session_state.agent_registry,
selected_id=st.session_state.selected_id,
selected_cache=st.session_state.selected_cache,
agent_builder=st.session_state.agent_builder,
cache=st.session_state.agent_builder.cache,
builder_agent=st.session_state.builder_agent,
)
# Path: core/utils.py
def get_image_and_text_nodes(
nodes: List[NodeWithScore],
) -> Tuple[List[NodeWithScore], List[NodeWithScore]]:
image_nodes = []
text_nodes = []
for res_node in nodes:
if isinstance(res_node.node, ImageNode):
image_nodes.append(res_node)
else:
text_nodes.append(res_node)
return image_nodes, text_nodes
# Path: pages/3_🤖_Generated_RAG_Agent.py
import streamlit as st
import pandas as pd
from st_utils import add_sidebar, get_current_state
from core.utils import get_image_and_text_nodes
from llama_index.schema import MetadataMode
from llama_index.chat_engine.types import AGENT_CHAT_RESPONSE_TYPE
from typing import Dict, Optional
"""Streamlit page showing builder config."""
####################
#### STREAMLIT #####
####################
st.set_page_config(
page_title="Generated RAG Agent",
page_icon="🦙",
layout="centered",
initial_sidebar_state="auto",
menu_items=None,
)
st.title("Generated RAG Agent")
current_state = get_current_state()
add_sidebar()
if (
"agent_messages" not in st.session_state.keys()
): # Initialize the chat messages history
st.session_state.agent_messages = [
{"role": "assistant", "content": "Ask me a question!"}
]
def display_sources(response: AGENT_CHAT_RESPONSE_TYPE) -> None:
| image_nodes, text_nodes = get_image_and_text_nodes(response.source_nodes) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: open-mmlab/Amphion
# Path: modules/activation_functions/gated_activation_unit.py
class GaU(nn.Module):
r"""Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural
Networks <https://arxiv.org/pdf/1606.05328.pdf>`_.
Args:
channels: number of input channels.
kernel_size: kernel size of the convolution.
dilation: dilation rate of the convolution.
d_context: dimension of context tensor, None if don't use context.
"""
def __init__(
self,
channels: int,
kernel_size: int = 3,
dilation: int = 1,
d_context: int = None,
):
super().__init__()
self.context = d_context
self.conv = Conv1d(
channels,
channels * 2,
kernel_size,
dilation=dilation,
padding=dilation * (kernel_size - 1) // 2,
)
if self.context:
self.context_proj = Conv1d(d_context, channels * 2, 1)
def forward(self, x: torch.Tensor, context: torch.Tensor = None):
r"""Calculate forward propagation.
Args:
x: input tensor with shape [B, C, T].
context: context tensor with shape [B, ``d_context``, T], default to None.
"""
h = self.conv(x)
if self.context:
h = h + self.context_proj(context)
h1, h2 = h.chunk(2, 1)
h = torch.tanh(h1) * torch.sigmoid(h2)
return h
# Path: modules/general/utils.py
def Conv1d(*args, **kwargs):
r"""Wrapper of ``nn.Conv1d`` with kaiming_normal_ initialization."""
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
# Path: modules/diffusion/bidilconv/residual_block.py
import math
import torch
import torch.nn as nn
from modules.activation_functions import GaU
from modules.general.utils import Conv1d
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
class ResidualBlock(nn.Module):
r"""Residual block with dilated convolution, main portion of ``BiDilConv``.
Args:
channels: The number of channels of input and output.
kernel_size: The kernel size of dilated convolution.
dilation: The dilation rate of dilated convolution.
d_context: The dimension of content encoder output, None if don't use context.
"""
def __init__(
self,
channels: int = 256,
kernel_size: int = 3,
dilation: int = 1,
d_context: int = None,
):
super().__init__()
self.context = d_context
| self.gau = GaU( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ise-uiuc/magicoder
# Path: src/magicoder/decontamination/benchmark_data.py
FILTER_OUT = {k: v() for k, v in LAZY_FILTER_OUT.items()}
# Path: src/magicoder/decontamination/utils.py
def add_dict(dict1: dict, dict2: dict) -> None:
"""
Add the values of dict2 to dict1. All values must be int, float or dictionaries that also verify this condition.
Will modify dict1 and return None
"""
for key, value in dict2.items():
if isinstance(value, (int, float)):
if key not in dict1:
dict1[key] = 0
dict1[key] += value
elif isinstance(value, dict):
if key not in dict1:
dict1[key] = {}
assert isinstance(dict1[key], dict)
add_dict(dict1[key], value)
else:
raise ValueError(f"Invalid type for key/value {key}: {value}")
# Path: src/magicoder/decontamination/utils.py
def shard_dataset(ds, shard_size, output_dir, num_proc):
if ds._indices is not None:
dataset_nbytes = ds.data.nbytes * len(ds._indices) / len(ds.data)
else:
dataset_nbytes = ds.data.nbytes
num_shards = int(dataset_nbytes / shard_size) + 1
print(f"Number of shards: {num_shards}")
print("sharding the dataset")
t_start = time.time()
shards = (
ds.shard(num_shards=num_shards, index=i, contiguous=True)
for i in range(num_shards)
)
# use f"{OUT_PATH}/data/train-{index:05d}-of-{num_shards:05d}.json" instead for json files
filenames = (
f"{output_dir}/train-{index:05d}-of-{num_shards:05d}.parquet"
for index in range(num_shards)
)
with Pool(num_proc) as p:
list(
tqdm(
p.imap_unordered(save_shard, zip(filenames, shards), chunksize=4),
total=num_shards,
)
)
print(f"Time to save dataset: {time.time()-t_start:.2f}")
# Path: src/magicoder/decontamination/find_substrings.py
import argparse
import json
import os
import shutil
from copy import deepcopy
from glob import glob
from pathlib import Path
from datasets import load_dataset
from magicoder.utils import write_jsonl
from .benchmark_data import FILTER_OUT
from .utils import add_dict, shard_dataset
# type: ignore
"""Migrated from: https://github.com/bigcode-project/bigcode-dataset. License: Apache 2.0"""
SHARD_SIZE = 1000 << 20 # 1GB
LANGUAGE_COL = "lang"
# LANGUAGES = ["Python", "Java", "JavaScript"]
def dump_benchmarks(file_path: str):
"""
Dump the dictionary of benchmark samples that are filtered out
"""
with open(file_path, "w") as f:
json.dump(FILTER_OUT, f, indent=2)
def filter_reason_to_benchmark_name(filter_reason: str):
assert filter_reason.endswith("_match")
return filter_reason[:-6]
def benchmark_name_to_filter_reason(benchmark_name: str):
return f"{benchmark_name}_match"
def update_benchmark_dict(
filter_out: dict, benchmark_cache: str, excluded_data_cache: str
):
"""
Iterates on current benchmark-samples. If a sample is found in the cached benchmark-samples, it is removed (it does not need to be searched),
and the corresponding data-samples from the cache are added to `exclude_data`
Returns:
- `updated`: an updated benchmark dict where samples from the cache are removed (they do not need to be searched anymore)
- `exclude_data`: a list of files to remove from the dataset
"""
updated = deepcopy(filter_out)
exclude_data = []
with open(benchmark_cache) as f:
benchmark_cache = json.load(f)
with open(excluded_data_cache) as f:
excluded_data_cache = json.load(f)
for bench, samples in filter_out.items():
for bench_sample in samples:
# Benchmark-sample was found in cache
if bench in benchmark_cache and bench_sample in benchmark_cache[bench]:
# No need to search for this sample in the dataset
updated[bench].remove(bench_sample)
# Corresponding data-samples will be excluded from the dataset.
exclude_data += [
data_sample
for data_sample in excluded_data_cache
if data_sample["filter_reason"]
== benchmark_name_to_filter_reason(bench)
and data_sample["matched_substring"] == bench_sample
]
print("After loading cache, will search for:")
for benchmark, values in updated.items():
print(f" num strings from {benchmark}: {len(values)}")
# Remove empty benchmarks
updated = {key: value for key, value in updated.items() if len(value) > 0}
return updated, exclude_data
def find_substrings(data, columns, filter_out, return_matched=False):
"""
filter_out: Dict[str, List[str]] mapping from benchmark name to list of strings that need to be
filtered-out.
Return True, None if the file should be included in the dataset.
Otherwise return False and some metadata about the file excluded
"""
content = "\n\n".join([data[col].lower() for col in columns])
# For each substring, try to find it in the file (case insensitive)
for benchmark, substrings in filter_out.items():
for substring in substrings:
if substring.lower() in content:
if return_matched:
return False, benchmark_name_to_filter_reason(benchmark), substring
else:
return False, benchmark_name_to_filter_reason(benchmark)
# Return True, None if none of the substrings was found
if return_matched:
return True, None, None
else:
return True, None
def aggregate_meta(tmp_meta_dir: str):
res = {}
for file in glob(f"{tmp_meta_dir}/*-meta.json"):
with open(file, "r") as f:
meta = json.load(f)
| add_dict(res, meta) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: KwaiKEG/KwaiAgents
# Path: kwaiagents/config.py
class Config(object):
def __init__(self) -> None:
"""Initialize the Config class"""
self.fast_llm_model = "gpt-3.5-turbo"
self.smart_llm_model = "gpt-4"
self.use_local_llm = False
self.local_llm_host = "localhost"
self.local_llm_port = 8888
self.browse_chunk_max_length = 4096
self.browse_summary_max_token = 300
self.selenium_web_browser = "chrome"
self.llm_max_retries = 5
self.temperature = 1.0
self.max_tokens_num = 4096
self.chain_logger = ChainMessageLogger()
def __str__(self):
s = "============ CONFIG ============\n"
for key, val in self.__dict__.items():
s += "· " + key.upper() + ":\t" + str(val) + '\n'
return s
def to_json_file(self, fname):
with open(fname, "w") as f:
json.dump({k:v for k, v in self.__dict__.items() if k not in ["chain_logger"]},f, ensure_ascii=False, indent=2)
def set_chain_logger(self, chain_logger):
self.chain_logger = chain_logger
# Path: kwaiagents/tools/base.py
class BaseResult(object):
def __init__(self, json_data):
self.json_data = json_data
def __str__(self):
return pprint.pformat(self.json_data)
@property
def answer(self):
return ""
@property
def answer_md(self):
return self.answer
@property
def urls(self):
return list()
@property
def prompt_responses(self):
return list()
# Path: kwaiagents/tools/base.py
class BaseTool(object):
def __init__(self, *args, **kwargs):
pass
def __call__(self):
return BaseResult({})
# Path: kwaiagents/tools/timedelta.py
from datetime import datetime
from dateutil.relativedelta import relativedelta
from kwaiagents.config import Config
from kwaiagents.tools.base import BaseResult, BaseTool
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: PAN Leyi
# Email: panleyi@kuaishou.com
class TimeDeltaResult(BaseResult):
@property
def answer(self):
item = self.json_data
rst = ""
for key in item.keys():
rst += f'{key}: {item[key]}\n'
return rst
| class TimeDeltaTool(BaseTool): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: EnVision-Research/LucidDreamer
# Path: utils/graphics_utils.py
def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):
Rt = np.zeros((4, 4))
Rt[:3, :3] = R.transpose()
Rt[:3, 3] = t
Rt[3, 3] = 1.0
C2W = np.linalg.inv(Rt)
cam_center = C2W[:3, 3]
cam_center = (cam_center + translate) * scale
C2W[:3, 3] = cam_center
Rt = np.linalg.inv(C2W)
return np.float32(Rt)
# Path: utils/graphics_utils.py
def focal2fov(focal, pixels):
return 2*math.atan(pixels/(2*focal))
# Path: utils/graphics_utils.py
def fov2focal(fov, pixels):
return pixels / (2 * math.tan(fov / 2))
# Path: utils/pointe_utils.py
def init_from_pointe(prompt):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M-textvec'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 0.0],
model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
)
# Produce a sample from the model.
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))):
samples = x
pc = sampler.output_to_point_clouds(samples)[0]
xyz = pc.coords
rgb = np.zeros_like(xyz)
rgb[:,0],rgb[:,1],rgb[:,2] = pc.channels['R'],pc.channels['G'],pc.channels['B']
return xyz,rgb
# Path: utils/sh_utils.py
def SH2RGB(sh):
return sh * C0 + 0.5
# Path: utils/general_utils.py
def inverse_sigmoid_np(x):
return np.log(x/(1-x))
# Path: scene/gaussian_model.py
class GaussianModel:
def setup_functions(self):
def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
def __init__(self, sh_degree : int):
def capture(self):
def restore(self, model_args, training_args):
def get_scaling(self):
def get_rotation(self):
def get_xyz(self):
def get_background(self):
def get_features(self):
def get_opacity(self):
def get_covariance(self, scaling_modifier = 1):
def oneupSHdegree(self):
def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
def training_setup(self, training_args):
def update_learning_rate(self, iteration):
def update_feature_learning_rate(self, iteration):
def update_rotation_learning_rate(self, iteration):
def update_scaling_learning_rate(self, iteration):
def construct_list_of_attributes(self):
def save_ply(self, path):
def reset_opacity(self):
def load_ply(self, path):
def replace_tensor_to_optimizer(self, tensor, name):
def _prune_optimizer(self, mask):
def prune_points(self, mask):
def cat_tensors_to_optimizer(self, tensors_dict):
def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):
def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
def densify_and_clone(self, grads, grad_threshold, scene_extent):
def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
def add_densification_stats(self, viewspace_point_tensor, update_filter):
L = build_scaling_rotation(scaling_modifier * scaling, rotation)
# Path: scene/dataset_readers.py
import os
import sys
import torch
import random
import torch.nn.functional as F
import numpy as np
import json
from PIL import Image
from typing import NamedTuple
from utils.graphics_utils import getWorld2View2, focal2fov, fov2focal
from pathlib import Path
from utils.pointe_utils import init_from_pointe
from plyfile import PlyData, PlyElement
from utils.sh_utils import SH2RGB
from utils.general_utils import inverse_sigmoid_np
from scene.gaussian_model import BasicPointCloud
#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class RandCameraInfo(NamedTuple):
uid: int
R: np.array
T: np.array
FovY: np.array
FovX: np.array
width: int
height: int
delta_polar : np.array
delta_azimuth : np.array
delta_radius : np.array
class SceneInfo(NamedTuple):
| point_cloud: BasicPointCloud |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: VRSEN/agency-swarm
# Path: agency_swarm/tools/base_tool.py
class BaseTool(OpenAISchema, ABC):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# # Exclude 'run' method from Pydantic model fields
# self.model_fields.pop("run", None)
@abstractmethod
def run(self, **kwargs):
pass
# Path: agency_swarm/tools/browsing/util/selenium.py
def get_web_driver():
try:
from selenium import webdriver
from selenium.webdriver.chrome.service import Service as ChromeService
except ImportError:
print("Selenium not installed. Please install it with pip install selenium")
raise ImportError
try:
from webdriver_manager.chrome import ChromeDriverManager
except ImportError:
print("webdriver_manager not installed. Please install it with pip install webdriver-manager")
raise ImportError
try:
from selenium_stealth import stealth
except ImportError:
print("selenium_stealth not installed. Please install it with pip install selenium-stealth")
raise ImportError
global wd
if wd:
return wd
global selenium_config
chrome_profile_path = selenium_config.get("chrome_profile_path", None)
profile_directory = None
user_data_dir = None
if isinstance(chrome_profile_path, str) and os.path.exists(chrome_profile_path):
profile_directory = os.path.split(chrome_profile_path)[-1].strip("\\").rstrip("/")
user_data_dir = os.path.split(chrome_profile_path)[0].strip("\\").rstrip("/")
print(f"Using Chrome profile: {profile_directory}")
print(f"Using Chrome user data dir: {user_data_dir}")
print(f"Using Chrome profile path: {chrome_profile_path}")
chrome_options = webdriver.ChromeOptions()
# Removed headless and other options for debugging purposes
chrome_driver_path = ChromeDriverManager().install()
if selenium_config.get("headless", False):
chrome_options.add_argument('--headless')
chrome_options.add_argument("--window-size=960,1080")
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--disable-dev-shm-usage')
chrome_options.add_argument("--disable-extensions")
chrome_options.add_argument("--disable-popup-blocking")
chrome_options.add_argument("--disable-web-security")
chrome_options.add_argument("--allow-running-insecure-content")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
chrome_options.add_experimental_option('useAutomationExtension', False)
if user_data_dir and profile_directory:
chrome_options.add_argument(f"user-data-dir={user_data_dir}")
chrome_options.add_argument(f"profile-directory={profile_directory}")
try:
wd = webdriver.Chrome(service=ChromeService(chrome_driver_path), options=chrome_options)
print("WebDriver initialized successfully.")
# Print the actual profile path being used
if wd.capabilities['chrome']['userDataDir']:
print(f"Profile path in use: {wd.capabilities['chrome']['userDataDir']}")
except Exception as e:
print(f"Error initializing WebDriver: {e}")
raise
stealth(
wd,
languages=["en-US", "en"],
vendor="Google Inc.",
platform="Win32",
webgl_vendor="Intel Inc.",
renderer="Intel Iris OpenGL Engine",
fix_hairline=True,
)
# wd.set_window_size(960, 1080)
wd.implicitly_wait(3)
return wd
# Path: agency_swarm/tools/browsing/util/selenium.py
def set_web_driver(new_wd):
global wd
wd = remove_highlight_and_labels(wd)
wd = new_wd
# Path: agency_swarm/tools/browsing/GoBack.py
import time
from agency_swarm.tools import BaseTool
from agency_swarm.tools.browsing.util.selenium import get_web_driver, set_web_driver
class GoBack(BaseTool):
"""
This tool allows you to go back 1 page in the browser history. Use it in case of a mistake or if a page shows you unexpected content.
"""
def run(self):
| wd = get_web_driver() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: resemble-ai/resemble-enhance
# Path: resemble_enhance/melspec.py
class MelSpectrogram(nn.Module):
def __init__(self, hp: HParams):
"""
Torch implementation of Resemble's mel extraction.
Note that the values are NOT identical to librosa's implementation
due to floating point precisions.
"""
super().__init__()
self.hp = hp
self.melspec = TorchMelSpectrogram(
hp.wav_rate,
n_fft=hp.n_fft,
win_length=hp.win_size,
hop_length=hp.hop_size,
f_min=0,
f_max=hp.wav_rate // 2,
n_mels=hp.num_mels,
power=1,
normalized=False,
# NOTE: Folowing librosa's default.
pad_mode="constant",
norm="slaney",
mel_scale="slaney",
)
self.register_buffer("stft_magnitude_min", torch.FloatTensor([hp.stft_magnitude_min]))
self.min_level_db = 20 * np.log10(hp.stft_magnitude_min)
self.preemphasis = hp.preemphasis
self.hop_size = hp.hop_size
def forward(self, wav, pad=True):
"""
Args:
wav: [B, T]
"""
device = wav.device
if wav.is_mps:
wav = wav.cpu()
self.to(wav.device)
if self.preemphasis > 0:
wav = torch.nn.functional.pad(wav, [1, 0], value=0)
wav = wav[..., 1:] - self.preemphasis * wav[..., :-1]
mel = self.melspec(wav)
mel = self._amp_to_db(mel)
mel_normed = self._normalize(mel)
assert not pad or mel_normed.shape[-1] == 1 + wav.shape[-1] // self.hop_size # Sanity check
mel_normed = mel_normed.to(device)
return mel_normed # (M, T)
def _normalize(self, s, headroom_db=15):
return (s - self.min_level_db) / (-self.min_level_db + headroom_db)
def _amp_to_db(self, x):
return x.clamp_min(self.hp.stft_magnitude_min).log10() * 20
# Path: resemble_enhance/denoiser/hparams.py
class HParams(HParamsBase):
batch_size_per_gpu: int = 128
distort_prob: float = 0.5
# Path: resemble_enhance/denoiser/unet.py
class UNet(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim=16, num_blocks=4, num_middle_blocks=2):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.input_proj = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.encoder_blocks = nn.ModuleList(
[
UNetBlock(input_dim=hidden_dim * 2**i, output_dim=hidden_dim * 2 ** (i + 1), scale_factor=0.5)
for i in range(num_blocks)
]
)
self.middle_blocks = nn.ModuleList(
[UNetBlock(input_dim=hidden_dim * 2**num_blocks) for _ in range(num_middle_blocks)]
)
self.decoder_blocks = nn.ModuleList(
[
UNetBlock(input_dim=hidden_dim * 2 ** (i + 1), output_dim=hidden_dim * 2**i, scale_factor=2)
for i in reversed(range(num_blocks))
]
)
self.head = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.GELU(),
nn.Conv2d(hidden_dim, output_dim, 1),
)
@property
def scale_factor(self):
return 2 ** len(self.encoder_blocks)
def pad_to_fit(self, x):
"""
Args:
x: (b c h w), input
Returns:
x: (b c h' w'), padded input
"""
hpad = (self.scale_factor - x.shape[2] % self.scale_factor) % self.scale_factor
wpad = (self.scale_factor - x.shape[3] % self.scale_factor) % self.scale_factor
return F.pad(x, (0, wpad, 0, hpad))
def forward(self, x):
"""
Args:
x: (b c h w), input
Returns:
o: (b c h w), output
"""
shape = x.shape
x = self.pad_to_fit(x)
x = self.input_proj(x)
s_list = []
for block in self.encoder_blocks:
x, s = block(x)
s_list.append(s)
for block in self.middle_blocks:
x, _ = block(x)
for block, s in zip(self.decoder_blocks, reversed(s_list)):
x, _ = block(x, s)
x = self.head(x)
x = x[..., : shape[2], : shape[3]]
return x
def test(self, shape=(3, 512, 256)):
import ptflops
macs, params = ptflops.get_model_complexity_info(
self,
shape,
as_strings=True,
print_per_layer_stat=True,
verbose=True,
)
print(f"macs: {macs}")
print(f"params: {params}")
# Path: resemble_enhance/denoiser/denoiser.py
import logging
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from ..melspec import MelSpectrogram
from .hparams import HParams
from .unet import UNet
logger = logging.getLogger(__name__)
def _normalize(x: Tensor) -> Tensor:
return x / (x.abs().max(dim=-1, keepdim=True).values + 1e-7)
class Denoiser(nn.Module):
@property
def stft_cfg(self) -> dict:
hop_size = self.hp.hop_size
return dict(hop_length=hop_size, n_fft=hop_size * 4, win_length=hop_size * 4)
@property
def n_fft(self):
return self.stft_cfg["n_fft"]
@property
def eps(self):
return 1e-7
def __init__(self, hp: HParams):
super().__init__()
self.hp = hp
self.net = UNet(input_dim=3, output_dim=3)
| self.mel_fn = MelSpectrogram(hp) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: PKU-YuanGroup/Chat-UniVi
# Path: ChatUniVi/constants.py
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
# Path: ChatUniVi/constants.py
DEFAULT_IM_START_TOKEN = "<im_start>"
# Path: ChatUniVi/constants.py
DEFAULT_IM_END_TOKEN = "<im_end>"
# Path: ChatUniVi/model/builder.py
import os
import shutil
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
from ChatUniVi.model import *
from ChatUniVi.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from transformers import AutoConfig, AutoModelForCausalLM
from huggingface_hub import hf_hub_download
from peft import PeftModel
from peft import PeftModel
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto"):
kwargs = {"device_map": device_map}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
)
else:
kwargs['torch_dtype'] = torch.float16
if 'chatunivi' in model_name.lower():
# Load ChatUniVi model
if 'lora' in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print('Loading ChatUniVi from base model...')
model = ChatUniViLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
print('Loading additional ChatUniVi weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
else:
# this is probably from HF Hub
def load_from_hf(repo_id, filename, subfolder=None):
cache_file = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder)
return torch.load(cache_file, map_location='cpu')
non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
if any(k.startswith('model.model.') for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
model.load_state_dict(non_lora_trainables, strict=False)
print('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Model is loaded...')
elif model_base is not None:
# this may be mm projector only
print('Loading ChatUniVi from base model...')
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = ChatUniViLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
# Load language model
if model_base is not None:
# PEFT model
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print('Convert to FP16...')
model.to(torch.float16)
else:
use_fast = False
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
image_processor = None
if 'chatunivi' in model_name.lower():
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
| tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: tatsu-lab/gpt_paper_assistant
# Path: arxiv_scraper.py
class Paper:
# paper class should track the list of authors, paper title, abstract, arxiv id
authors: List[str]
title: str
abstract: str
arxiv_id: str
# add a hash function using arxiv_id
def __hash__(self):
return hash(self.arxiv_id)
# Path: arxiv_scraper.py
class EnhancedJSONEncoder(json.JSONEncoder):
def default(self, o):
if dataclasses.is_dataclass(o):
return dataclasses.asdict(o)
return super().default(o)
# Path: filter_papers.py
import configparser
import dataclasses
import json
import os
import re
import retry
from collections import defaultdict
from typing import List
from openai import OpenAI
from tqdm import tqdm
from arxiv_scraper import Paper
from arxiv_scraper import EnhancedJSONEncoder
def filter_by_author(all_authors, papers, author_targets, config):
# filter and parse the papers
selected_papers = {} # pass to output
all_papers = {} # dict for later filtering
sort_dict = {} # dict storing key and score
# author based selection
for paper in papers:
all_papers[paper.arxiv_id] = paper
if config["FILTERING"].getboolean("author_match"):
for author in paper.authors:
if author in all_authors:
for alias in all_authors[author]:
if alias["authorId"] in author_targets:
selected_papers[paper.arxiv_id] = {
**dataclasses.asdict(paper),
**{"COMMENT": "Author match"},
}
sort_dict[paper.arxiv_id] = float(
config["SELECTION"]["author_match_score"]
)
break
return selected_papers, all_papers, sort_dict
def filter_papers_by_hindex(all_authors, papers, config):
# filters papers by checking to see if there's at least one author with > hcutoff hindex
paper_list = []
for paper in papers:
max_h = 0
for author in paper.authors:
if author in all_authors:
max_h = max(
max_h, max([alias["hIndex"] for alias in all_authors[author]])
)
if max_h >= float(config["FILTERING"]["hcutoff"]):
paper_list.append(paper)
return paper_list
def calc_price(model, usage):
if model == "gpt-4-1106-preview":
return (0.01 * usage.prompt_tokens + 0.03 * usage.completion_tokens) / 1000.0
if model == "gpt-4":
return (0.03 * usage.prompt_tokens + 0.06 * usage.completion_tokens) / 1000.0
if (model == "gpt-3.5-turbo") or (model == "gpt-3.5-turbo-1106"):
return (0.0015 * usage.prompt_tokens + 0.002 * usage.completion_tokens) / 1000.0
@retry.retry(tries=3, delay=2)
def call_chatgpt(full_prompt, openai_client, model, num_samples):
return openai_client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": full_prompt}],
temperature=0.0,
n=int(num_samples),
seed=0,
)
def run_and_parse_chatgpt(full_prompt, openai_client, config):
# just runs the chatgpt prompt, tries to parse the resulting JSON
completion = call_chatgpt(
full_prompt,
openai_client,
config["SELECTION"]["model"],
config["FILTERING"]["num_samples"],
)
json_dicts = defaultdict(list)
for choice in completion.choices:
out_text = choice.message.content
out_text = re.sub("```jsonl\n", "", out_text)
out_text = re.sub("```", "", out_text)
out_text = re.sub(r"\n+", "\n", out_text)
out_text = re.sub("},", "}", out_text).strip()
# split out_text line by line and parse each as a json.
for line in out_text.split("\n"):
# try catch block to attempt to parse json
try:
loaded_output = json.loads(line)
json_dicts[loaded_output["ARXIVID"]].append(loaded_output)
except Exception as ex:
if config["OUTPUT"].getboolean("debug_messages"):
print("Exception happened " + str(ex))
print("Failed to parse LM output as json")
print(out_text)
print("RAW output")
print(completion.choices[0].message.content)
continue
all_dict = []
for id, json_list in json_dicts.items():
rel_score = sum([float(jdict["RELEVANCE"]) for jdict in json_list]) / float(
len(json_list)
)
nov_score = sum([float(jdict["NOVELTY"]) for jdict in json_list]) / float(
len(json_list)
)
new_dict = {
"ARXIVID": json_list[0]["ARXIVID"],
"COMMENT": json_list[0]["COMMENT"],
"RELEVANCE": rel_score,
"NOVELTY": nov_score,
}
all_dict.append(new_dict)
return all_dict, calc_price(config["SELECTION"]["model"], completion.usage)
| def paper_to_string(paper_entry: Paper) -> str: |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: BobaZooba/xllm
# Path: src/xllm/enums.py
class General:
class Transformers:
class Registry:
class Datasets:
class Collators:
class Trainers:
class Experiments:
class EnvironmentVariables:
class LogLevel:
# Path: src/xllm/datasets/registry.py
# Path: src/xllm/datasets/soda.py
class SodaDataset(BaseDataset):
HEADER_KEY = "header"
DIALOG_KEY = "dialog"
_HF_DATASET_ID = "allenai/soda"
def __init__(self, data: List[RawSample], header_drop_probability: float = 0.05):
super().__init__(data=data)
self.header_drop_probability = header_drop_probability
@classmethod
def get_data(cls, config: Config) -> Optional[Tuple[List[RawSample], Optional[List[RawSample]]]]:
soda_dataset = datasets.load_dataset(cls._HF_DATASET_ID)
parsed_data: Dict[str, List[RawSample]] = dict()
known_indices = set()
for split in ["train", "test"]:
parsed_data[split] = list()
for sample in tqdm(soda_dataset[split], desc=f"Parsing SODA {split}"):
index = sample.get("original_index")
if index in known_indices:
continue
parsed_sample = {
cls.HEADER_KEY: sample.get("narrative"),
cls.DIALOG_KEY: [
f"{speaker}: {phrase}"
for speaker, phrase in zip(sample.get("speakers"), sample.get("dialogue"))
],
}
parsed_data[split].append(parsed_sample)
known_indices.add(index)
train = parsed_data["train"]
valid = parsed_data["test"]
return train, valid
def get_sample(self, index: int) -> RawSample:
sample = self.data[index]
dialog = sample[self.DIALOG_KEY]
phrases = list()
if not isinstance(dialog, list):
raise ValueError(f"{self.DIALOG_KEY} of sample is not a list: {type(dialog)}")
for phrase in dialog:
if isinstance(phrase, str):
phrases.append(phrase)
if self.HEADER_KEY in sample:
header = sample[self.HEADER_KEY]
is_drop_header = np.random.rand() <= self.header_drop_probability
if not is_drop_header and isinstance(header, str):
phrases.insert(0, header)
sample = {enums.General.text_parts: [phrase.replace("\n", " ").replace("\r", " ") for phrase in phrases]}
return sample
# Path: tests/helpers/dummy_data.py
DATA = [
{
enums.General.text_parts: [
"Person 1: Hello",
"Person 2: It's me",
"Person 1: I was wondering",
]
},
{
enums.General.text_parts: [
"You are a sith lord",
"Kenobi: Hello there",
"General Grievous: General Kenobi",
]
},
]
# Path: tests/unit/datasets/test_registry.py
from src.xllm import enums
from src.xllm.datasets.registry import datasets_registry
from src.xllm.datasets.soda import SodaDataset
from tests.helpers.dummy_data import DATA
# Copyright 2023 Boris Zubarev. 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.
def test_get_soda_dataset() -> None:
dataset_cls = datasets_registry.get(key=enums.Datasets.soda)
dataset = dataset_cls(data=DATA)
| assert isinstance(dataset, SodaDataset) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: banodoco/Steerable-Motion
# Path: imports/AdvancedControlNet/control.py
class LatentKeyframeImport:
def __init__(self, batch_index: int, strength: float) -> None:
self.batch_index = batch_index
self.strength = strength
# Path: imports/AdvancedControlNet/control.py
class LatentKeyframeGroupImport:
def __init__(self) -> None:
self.keyframes: list[LatentKeyframeImport] = []
def add(self, keyframe: LatentKeyframeImport) -> None:
added = False
# replace existing keyframe if same batch_index
for i in range(len(self.keyframes)):
if self.keyframes[i].batch_index == keyframe.batch_index:
self.keyframes[i] = keyframe
added = True
break
if not added:
self.keyframes.append(keyframe)
self.keyframes.sort(key=lambda k: k.batch_index)
def get_index(self, index: int) -> Union[LatentKeyframeImport, None]:
try:
return self.keyframes[index]
except IndexError:
return None
def __getitem__(self, index) -> LatentKeyframeImport:
return self.keyframes[index]
def is_empty(self) -> bool:
return len(self.keyframes) == 0
def clone(self) -> 'LatentKeyframeGroupImport':
cloned = LatentKeyframeGroupImport()
for tk in self.keyframes:
cloned.add(tk)
return cloned
# Path: imports/AdvancedControlNet/control.py
class StrengthInterpolationImport:
LINEAR = "linear"
EASE_IN = "ease-in"
EASE_OUT = "ease-out"
EASE_IN_OUT = "ease-in-out"
NONE = "none"
# Path: imports/AdvancedControlNet/logger.py
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
# Path: imports/AdvancedControlNet/latent_keyframe_nodes.py
from typing import Union
from collections.abc import Iterable
from .control import LatentKeyframeImport, LatentKeyframeGroupImport
from .control import StrengthInterpolationImport as SI
from .logger import logger
import numpy as np
class LatentKeyframeNodeImport:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
},
"optional": {
"prev_latent_kf": ("LATENT_KEYFRAME", ),
}
}
RETURN_NAMES = ("LATENT_KF", )
RETURN_TYPES = ("LATENT_KEYFRAME", )
FUNCTION = "load_keyframe"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
def load_keyframe(self,
batch_index: int,
strength: float,
prev_latent_kf: LatentKeyframeGroupImport=None,
prev_latent_keyframe: LatentKeyframeGroupImport=None, # old name
):
prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
if not prev_latent_keyframe:
prev_latent_keyframe = LatentKeyframeGroupImport()
else:
prev_latent_keyframe = prev_latent_keyframe.clone()
| keyframe = LatentKeyframeImport(batch_index, strength) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: innovatorved/subtitle
# Path: app/models/models.py
def download_model(model_name):
def download_file(url, filepath):
# Path: app/utils/checks.py
def check_models_exist(name: str):
try:
if model_names[name] in os.listdir(os.path.join(os.getcwd(), "models")):
print("Model {} exists".format(name))
else:
print("Model {} does not exist".format(name))
download_model(key)
return True
except Exception as exc:
print("Error in check_models_exist: {}".format(str(exc)))
return False
# Path: app/utils/utils.py
def generate_vtt_file(path: str = None, model="ggml-tiny.bin"):
"""./whisper -m models/ggml-tiny.en.bin -f Rev.mp3 out.wav -nt --output-vtt"""
try:
if path is None or not chack_file_exist(path):
raise Exception("PATH Error!")
rand = uuid.uuid4()
output_audio_path: str = f"data/{rand}.wav"
vtt_file_path: str = f"data/{rand}.wav.vtt"
command: str = f"./binary/whisper -t {NO_OF_THREADS} -p {NO_OF_PROCESSORS} -m models/{model} -f {path} {output_audio_path} -nt --output-vtt"
execute_command(command)
return [rand, output_audio_path, vtt_file_path]
except Exception as exc:
logging.error(exc)
raise Exception(exc.__str__())
# Path: app/utils/utils.py
def merge_video_and_vtt(video_path, vtt_path, output_path):
try:
if not chack_file_exist(video_path):
raise Exception("Video File Not Found!")
if not chack_file_exist(vtt_path):
raise Exception("VTT File Not Found!")
# Load the input files
video = ffmpeg.input(video_path)
subtitles = ffmpeg.input(vtt_path)
merged = ffmpeg.output(
video,
subtitles,
output_path,
vcodec="copy",
scodec="mov_text",
)
ffmpeg.run(merged, overwrite_output=True)
return True
except Exception as exc:
raise Exception(f"An error occurred: {exc}")
# Path: app/core/app.py
import logging
from app.models import download_model, model_names
from app.utils.checks import check_models_exist
from app.utils import generate_vtt_file, merge_video_and_vtt
# Configure logging
logger = logging.getLogger(__name__)
def process_video(file, model="base"):
"""
add_subtitle_in_video
@param file: video file path
@param model: model name
@return: [vtt_file_path , output_file]
"""
try:
if not check_models_exist(model):
download_model(model)
output_file = f"{file.split('.')[0]}_subtitled.{file.split('.')[1]}"
| process_id, output_audio_path, vtt_file_path = generate_vtt_file( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: x0rzavi/github-readme-terminal
# Path: gifos/utils/load_config.py
def load_toml(file_name: str) -> dict:
def __update_config_with_env_vars(config, prefix="GIFOS"):
# Path: gifos/utils/schemas/imagebb_image.py
class ImgbbImage:
"""A class to represent an image uploaded to ImgBB.
This class represents an image uploaded to ImgBB.
Attributes:
id: A string that represents the image's ID on ImgBB.
url: A string that represents the image's URL on ImgBB.
delete_url: A string that represents the URL to delete the image from ImgBB.
file_name: A string that represents the name of the image file.
expiration: A string that represents the expiration time of the image.
size: A string that represents the size of the image.
mime: A string that represents the MIME type of the image.
extension: A string that represents the extension of the image file.
"""
__slots__ = [
"id",
"url",
"delete_url",
"file_name",
"expiration",
"size",
"mime",
"extension",
]
id: str
url: str
delete_url: str
file_name: str
expiration: str
size: str
mime: str
extension: str
# Path: gifos/utils/upload_imgbb.py
from base64 import b64encode
from dotenv import load_dotenv
from gifos.utils.load_config import gifos_settings
from gifos.utils.schemas.imagebb_image import ImgbbImage
import os
import requests
import sys
"""This module contains a function for uploading an image to ImgBB."""
load_dotenv()
IMGBB_API_KEY = os.getenv("IMGBB_API_KEY")
ENDPOINT = "https://api.imgbb.com/1/upload"
def upload_imgbb(file_name: str, expiration: int = None) -> ImgbbImage:
"""Upload an image to ImgBB.
This function uploads an image to ImgBB using the ImgBB API. The function reads the
image file, encodes it in base64, and sends a POST request to the ImgBB API. The
function uses the `IMGBB_API_KEY` environment variable for authentication and the
`ENDPOINT` constant for the API endpoint. If the `debug` configuration value is
True, the function sets the image expiration time to 10 minutes.
:param file_name: The name of the image file to upload.
:type file_name: str
:param expiration: The expiration time for the image in seconds. If the `debug`
configuration value is True, this parameter is ignored and the expiration time
is set to 10 minutes. The value must be between 60 and 15552000 (6 months) if
provided.
:type expiration: int, optional
:return: An `ImgbbImage` object containing the uploaded image's information if the
upload is successful, otherwise None.
:rtype: ImgbbImage or None
"""
if not IMGBB_API_KEY:
print("ERROR: Please provide IMGBB_API_KEY")
sys.exit(1)
| if gifos_settings.get("general", {}).get("debug"): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Zaloog/kanban-python
# Path: src/kanban_python/config.py
class KanbanConfig:
def __init__(self, path=CONFIG_FILE_PATH) -> None:
def __repr__(self) -> str:
def save(self):
def config(self) -> configparser.ConfigParser:
def active_board(self) -> str:
def active_board(self, new_board):
def kanban_boards(self) -> list:
def kanban_boards_dict(self) -> dict:
def kanban_boards_dict(self, board_name: str) -> dict:
def active_board_path(self) -> str:
def show_footer(self):
def show_footer(self, visible):
def col_min_width(self) -> int:
def col_min_width(self, new_width: int) -> None:
def kanban_columns_dict(self) -> dict:
def kanban_columns_dict(self, updated_dict) -> dict:
def vis_cols(self) -> list:
def done_limit(self) -> int:
def done_limit(self, new_limit: int) -> None:
def scanned_files(self) -> list:
def scanned_files(self, new_files_to_scan: str) -> None:
def scanned_patterns(self) -> list:
def scanned_patterns(self, new_patterns_to_scan: str) -> None:
def create_init_config(conf_path=CONFIG_PATH, data_path=DATA_PATH):
def delete_current_folder_board_from_config(
cfg=cfg, curr_path: str = str(Path.cwd())
) -> None:
def check_if_board_name_exists_in_config(boardname: str, cfg=cfg) -> bool:
def check_if_current_active_board_in_board_list(cfg=cfg) -> bool:
def delete_board_from_config(board_name, cfg=cfg) -> None:
def check_config_exists(path=CONFIG_FILE_PATH) -> bool:
def get_json_path(boardname: str):
# Path: src/kanban_python/constants.py
BOARD_CAPTION_STRING = "Tasks have the following Structure:\
[[cyan]ID[/]] ([orange3]TAG[/]) [white]Task Title[/] |[red]Days Left[/]|"
# Path: src/kanban_python/constants.py
COLOR_DICT = {
"Ready": "[red]Ready[/]",
"Doing": "[yellow]Doing[/]",
"Done": "[green]Done[/]",
"Deleted": "[deep_pink4]Deleted[/]",
"Archived": "[dark_goldenrod]Archived[/]",
}
# Path: src/kanban_python/constants.py
CONFIG_FILE_PATH = CONFIG_PATH / CONFIG_FILE_NAME
# Path: src/kanban_python/constants.py
FOOTER = [FOOTER_FIRST, FOOTER_LAST]
# Path: src/kanban_python/constants.py
REPORT_COLORS = ["#161b22", "#0e4429", "#006d32", "#26a641", "#39d353"]
# Path: src/kanban_python/utils.py
def get_motivational_quote() -> str:
def current_time_to_str() -> str:
def calculate_time_delta_str(start_time_str: str, end_time_str: str) -> float:
def create_status_dict_for_rows(data: dict, vis_cols: list) -> dict:
def check_if_done_col_leq_X(cfg, data: dict) -> bool:
def check_if_there_are_visible_tasks_in_board(data: dict, vis_cols: list) -> bool:
def move_first_done_task_to_archive(data: dict):
def delete_json_file(db_path: str) -> None:
def check_board_name_valid(boardname: str):
def scan_files(path=Path.cwd(), endings: list = [".py"]):
def recursive_search(path, file_list: list, progress):
def scan_for_todos(
file_paths: list, rel_path=Path.cwd(), patterns: list = ["#TODO", "# TODO"]
) -> list:
def split_todo_in_tag_and_title(todo: str, patterns: list):
def get_tag_id_choices(data_dict: dict, vis_cols: list) -> list:
def check_scanner_files_valid(files: str) -> bool:
def check_scanner_patterns_valid(patterns: str) -> bool:
def get_iso_calender_info(date_str: str):
def create_dict_for_report_view(completed_tasks: list):
def create_color_mapping(amount_list: list, max_val: int):
def create_report_document(boards_dict: dict):
def check_due_date_format(date_str: str) -> bool:
def due_date_datetime_to_date(date_datetime: str) -> str:
def due_date_date_to_datetime(date_str: str) -> str:
def calculate_days_left_till_due(due_date: str):
# Path: src/kanban_python/interface.py
import calendar
from datetime import datetime
from itertools import zip_longest
from rich.prompt import Confirm, IntPrompt, Prompt
from rich.table import Table
from .config import cfg
from .constants import (
BOARD_CAPTION_STRING,
COLOR_DICT,
CONFIG_FILE_PATH,
FOOTER,
REPORT_COLORS,
)
from .utils import (
calculate_days_left_till_due,
calculate_time_delta_str,
check_due_date_format,
console,
create_color_mapping,
create_dict_for_report_view,
create_status_dict_for_rows,
current_time_to_str,
due_date_date_to_datetime,
due_date_datetime_to_date,
)
# Board
#####################################################################################
def create_table(data: dict) -> Table:
status_dict = create_status_dict_for_rows(data=data, vis_cols=cfg.vis_cols)
table_name = cfg.active_board
table = Table(
title=f"[blue]Active Board: {table_name}[/]",
highlight=True,
show_header=True,
show_footer=True if cfg.show_footer == "True" else False,
caption=BOARD_CAPTION_STRING,
)
for i, category in enumerate([COLOR_DICT.get(col, col) for col in cfg.vis_cols]):
table.add_column(
header=category + f"\t({len(status_dict[cfg.vis_cols[i]])} Task/s)",
header_style="bold",
justify="left",
overflow="fold",
footer=FOOTER[0]
if i == 0
else FOOTER[1]
if i == len(cfg.vis_cols) - 1
else "",
min_width=cfg.col_min_width,
)
for row_tasks in zip_longest(*status_dict.values()):
table.add_row(*row_tasks)
return table
# Board Action selection
def input_ask_for_action():
| console.print( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: AMAAI-Lab/mustango
# Path: audioldm/latent_diffusion/util.py
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt(
(1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
)
if verbose:
print(
f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
)
print(
f"For the chosen value of eta, which is {eta}, "
f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
)
return sigmas, alphas, alphas_prev
# Path: audioldm/latent_diffusion/util.py
def make_ddim_timesteps(
ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
):
if ddim_discr_method == "uniform":
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == "quad":
ddim_timesteps = (
(np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
).astype(int)
else:
raise NotImplementedError(
f'There is no ddim discretization method called "{ddim_discr_method}"'
)
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
if verbose:
print(f"Selected timesteps for ddim sampler: {steps_out}")
return steps_out
# Path: audioldm/latent_diffusion/util.py
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
shape[0], *((1,) * (len(shape) - 1))
)
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
# Path: audioldm/latent_diffusion/util.py
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t).contiguous()
return out.reshape(b, *((1,) * (len(x_shape) - 1))).contiguous()
# Path: audioldm/latent_diffusion/ddim.py
import torch
import numpy as np
from tqdm import tqdm
from audioldm.latent_diffusion.util import (
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
extract_into_tensor,
)
"""SAMPLING ONLY."""
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
def make_schedule(
self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
):
self.ddim_timesteps = make_ddim_timesteps(
ddim_discr_method=ddim_discretize,
num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,
verbose=verbose,
)
alphas_cumprod = self.model.alphas_cumprod
assert (
alphas_cumprod.shape[0] == self.ddpm_num_timesteps
), "alphas have to be defined for each timestep"
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
self.register_buffer("betas", to_torch(self.model.betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer(
"alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer(
"sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
)
self.register_buffer(
"sqrt_one_minus_alphas_cumprod",
to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod",
to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
)
# ddim sampling parameters
| ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: lxmusics/lx-music-api-server-python
# Path: common/Httpx.py
def is_valid_utf8(text):
def is_plain_text(text):
def convert_dict_to_form_string(dic):
def log_plaintext(text):
def request(url, options = {}):
def _json():
def checkcn():
def __init__(self, status, content, headers):
def json(self):
async def convert_to_requests_response(aiohttp_response):
async def AsyncRequest(url, options = {}):
class ClientResponse:
# Path: common/utils.py
def createBase64Encode(data_bytes):
def createHexEncode(data_bytes):
def createBase64Decode(data):
def createHexDecode(data):
def handleInflateRawSync(data):
def require(module):
def addToGlobalNamespace(key, data):
def filterFileName(filename):
def createMD5(s: str):
def readFile(path, mode = "text"):
def unique_list(list_in):
def encodeURIComponent(component):
def decodeURIComponent(component):
def encodeURI(uri):
def decodeURI(uri):
def sortDict(dictionary):
def mergeDict(dict1, dict2):
def __init__(self, d):
def __setattr__(self, key, value):
def to_dict(self):
def __getattr__(self, UNUSED):
def dump_xml(data):
def load_xml(data):
def sizeFormat(size):
def timeLengthFormat(t):
def timestamp_format(t):
class CreateObject(dict):
# Path: common/exceptions.py
class FailedException(Exception):
# 此错误用于处理代理API请求失败的情况
pass
# Path: modules/kg/utils.py
def buildRequestParams(dictionary):
joined_str = '&'.join([f'{k}={v}' for k, v in dictionary.items()])
return joined_str
# Path: modules/kg/search.py
from common import Httpx
from common import utils
from common.exceptions import FailedException
from .utils import buildRequestParams
# ----------------------------------------
# - mode: python -
# - author: helloplhm-qwq -
# - name: search.py -
# - project: lx-music-api-server -
# - license: MIT -
# ----------------------------------------
# This file is part of the "lx-music-api-server" project.
def formatSubResult(l):
res = []
for songinfo in l:
fileinfo = {}
if (songinfo['FileSize'] != 0):
fileinfo['128k'] = {
'hash': songinfo['FileHash'],
'size': utils.sizeFormat(songinfo['FileSize']),
}
if (songinfo['HQFileSize'] != 0):
fileinfo['320k'] = {
'hash': songinfo['HQFileHash'],
'size': utils.sizeFormat(songinfo['HQFileSize']),
}
if (songinfo['SQFileSize'] != 0):
fileinfo['flac'] = {
'hash': songinfo['SQFileHash'],
'size': utils.sizeFormat(songinfo['SQFileSize']),
}
if (songinfo['ResFileSize'] != 0):
fileinfo['flac24bit'] = {
'hash': songinfo['ResFileHash'],
'size': utils.sizeFormat(songinfo['ResFileSize']),
}
res.append({
'name': songinfo['SongName'],
'name_ori': songinfo['OriSongName'],
'name_extra': songinfo['SongName'].replace(songinfo['OriSongName'], ''),
'singer': songinfo['SingerName'],
'singer_list': [{'name': i['name'], 'id': i['id']} for i in songinfo['Singers']],
'isoriginal': True if (songinfo['IsOriginal'] == 1) else False,
'tag': songinfo.get('TagContent') if songinfo.get('TagContent') else '',
'format_length': utils.timeLengthFormat(songinfo['Duration']),
'length': songinfo['Duration'],
'hash': songinfo['FileHash'],
'file_info': fileinfo,
'songmid': songinfo['Audioid'],
'album_id': songinfo['AlbumID'],
'album': songinfo['AlbumName'],
'language': songinfo['trans_param'].get('language') if songinfo['trans_param'] else '',
'cover': songinfo['Image'].format(size = 1080),
'sizable_cover': songinfo['Image'],
'mvid': songinfo['MvHash'],
})
return res
async def getSongSearchResult(query, page, size):
req = await Httpx.AsyncRequest(utils.encodeURI(f'https://songsearch.kugou.com/song_search_v2?' + buildRequestParams({
"keyword": query,
"page": page,
"pagesize": size,
"userid": 0,
"clientver": "",
"platform": "WebFilter",
"filter": 2,
"iscorrection": 1,
"privilege_filter": 0
})), {
"headers": {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.142.86 Safari/537.36",
"Referer": "https://www.kugou.com",
}
})
body = req.json()
if (body['status'] != 1):
| raise FailedException('歌曲搜索失败') |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ai-forever/Kandinsky-3
# Path: kandinsky3/model/utils.py
def exist(item):
return item is not None
# Path: kandinsky3/model/utils.py
def set_default_layer(condition, layer_1, args_1=[], kwargs_1={}, layer_2=Identity, args_2=[], kwargs_2={}):
if condition:
return layer_1(*args_1, **kwargs_1)
else:
return layer_2(*args_2, **kwargs_2)
# Path: kandinsky3/model/nn.py
import math
import torch
from torch import nn, einsum
from einops import rearrange, repeat
from .utils import exist, set_default_layer
class Identity(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
@staticmethod
def forward(x, *args, **kwargs):
return x
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=x.device) * -emb)
emb = rearrange(x, 'i -> i 1') * rearrange(emb, 'j -> 1 j')
return torch.cat((emb.sin(), emb.cos()), dim=-1)
class ConditionalGroupNorm(nn.Module):
def __init__(self, groups, normalized_shape, context_dim):
super().__init__()
self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
self.context_mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(context_dim, 2 * normalized_shape)
)
self.context_mlp[1].weight.data.zero_()
self.context_mlp[1].bias.data.zero_()
def forward(self, x, context):
context = self.context_mlp(context)
ndims = ' 1' * len(x.shape[2:])
context = rearrange(context, f'b c -> b c{ndims}')
scale, shift = context.chunk(2, dim=1)
x = self.norm(x) * (scale + 1.) + shift
return x
class Attention(nn.Module):
def __init__(self, in_channels, out_channels, context_dim, head_dim=64):
super().__init__()
assert out_channels % head_dim == 0
self.num_heads = out_channels // head_dim
self.scale = head_dim ** -0.5
self.to_query = nn.Linear(in_channels, out_channels, bias=False)
self.to_key = nn.Linear(context_dim, out_channels, bias=False)
self.to_value = nn.Linear(context_dim, out_channels, bias=False)
self.output_layer = nn.Linear(out_channels, out_channels, bias=False)
def forward(self, x, context, context_mask=None):
query = rearrange(self.to_query(x), 'b n (h d) -> b h n d', h=self.num_heads)
key = rearrange(self.to_key(context), 'b n (h d) -> b h n d', h=self.num_heads)
value = rearrange(self.to_value(context), 'b n (h d) -> b h n d', h=self.num_heads)
attention_matrix = einsum('b h i d, b h j d -> b h i j', query, key) * self.scale
| if exist(context_mask): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: spfrommer/torchexplorer
# Path: torchexplorer/components/tooltip.py
class Tooltip:
"""The tooltip that pops up next to a Module."""
def __init__(self, title: str, keys: list[str], vals: list[str]):
self.title = title
self.keys = keys
self.vals = vals
@classmethod
def create_io(cls, tracker: SizeTracker) -> 'Tooltip':
name = tracker.type.split('.')[-1]
keys, vals = ['size'], [str(tracker.size).replace('None', dash)]
return Tooltip(name, keys, vals)
@classmethod
def create_moduleinvocation(
cls, module: Module, parent_module: Module, invocation_id: InvocationId
) -> 'Tooltip':
name_in_parent = cls._get_name_in_parent(module, parent_module)
io_shape_keys, io_shape_vals = cls._get_io_shape_keyvals(module, invocation_id)
extra_repr_keys, extra_repr_vals = cls._get_extra_repr_keyvals(module)
keys = io_shape_keys + extra_repr_keys
vals = io_shape_vals + extra_repr_vals
assert len(keys) == len(vals)
return Tooltip(name_in_parent, keys, vals)
@classmethod
def create_attach(cls, module: Module) -> 'Tooltip':
return cls.create_io(module.torchexplorer_metadata.input_sizes[0][0])
@classmethod
def _get_name_in_parent(cls, module: Module, parent_module: Module) -> str:
name_in_parent = ''
for name, m in parent_module.named_children():
if m == module:
name_in_parent = name
break
if isinstance(m, ModuleList):
for i, mm in enumerate(m):
if mm == module:
name_in_parent = f'{name}[{i}]'
break
if isinstance(m, ModuleDict):
for k, mm in m.items():
if mm == module:
name_in_parent = f'{name}[{k}]'
break
return name_in_parent
@classmethod
def _get_io_shape_keyvals(
cls, module: Module, invocation_id: InvocationId
) -> tuple[list[str], list[str]]:
metadata = module.torchexplorer_metadata
keys, vals = [], []
one_input = len(metadata.input_sizes[invocation_id]) == 1
for i, input_tracker in enumerate(metadata.input_sizes[invocation_id]):
keys.append('in_size' if one_input else f'in{i}_size')
vals.append(str(input_tracker.size).replace('None', dash))
one_output = len(metadata.output_sizes[invocation_id]) == 1
for i, output_tracker in enumerate(metadata.output_sizes[invocation_id]):
keys.append('out_size' if one_output else f'out{i}_size')
vals.append(str(output_tracker.size).replace('None', dash))
return keys, vals
@classmethod
def _get_extra_repr_keyvals(cls, module: Module) -> tuple[list[str], list[str]]:
try:
keys, vals = [], []
extra_rep = module.extra_repr()
pairs = re.split(r',\s*(?![^()]*\))(?![^[]]*\])', extra_rep)
for pair in pairs:
if pair == '':
continue
k, v = pair.split('=') if ('=' in pair) else (dash, pair)
keys.append(k.strip())
vals.append(v.strip())
except Exception:
keys, vals = [], []
return keys, vals
# Path: torchexplorer/core.py
class ModuleInvocationHistograms:
"""The histograms associated to a particular InvocationId on a module."""
input_hists: list[IncrementalHistogram] = field(default_factory=lambda: [])
output_hists: list[IncrementalHistogram] = field(default_factory=lambda: [])
# Path: torchexplorer/core.py
class ModuleSharedHistograms:
"""The histograms are shared across all InvocationId on a module."""
param_hists: dict[ParamName, IncrementalHistogram] = dict_field()
param_grad_hists: dict[ParamName, IncrementalHistogram] = dict_field()
# Path: torchexplorer/render/structs.py
from typing import Optional
from dataclasses import dataclass, field
from torchexplorer.components.tooltip import Tooltip
from torchexplorer.core import (
ModuleInvocationHistograms, ModuleSharedHistograms
)
from __future__ import annotations
@dataclass
class EdgeLayout:
path_points: list[list[float]]
arrowhead_points: list[list[float]]
downstream_input_index: Optional[int]
upstream_output_index: Optional[int]
@dataclass
class TooltipLayout:
tooltip: Tooltip
# Coordinates in parent of the layout this tooltip belongs to
bottom_left_corner: list[float] = field(default_factory=lambda: [0, 0])
top_right_corner: list[float] = field(default_factory=lambda: [0, 0])
# Either a specific module invocation or for IO
@dataclass
class NodeLayout:
display_name: Optional[str] = None
tooltip: Optional[TooltipLayout] = None
invocation_hists: Optional[ModuleInvocationHistograms] = None
invocation_grad_hists: Optional[ModuleInvocationHistograms] = None
| shared_hists: Optional[ModuleSharedHistograms] = None |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: namin/llm-verified-with-monte-carlo-tree-search
# Path: lang_config.py
STOP_WORD = "\n"
# Path: model_config.py
BASE_MODEL_NAME = args.base_model_name
# Path: model_config.py
PEFT_MODEL_PATH = args.peft_model_path
# Path: model_config.py
PPO_MODEL_PATH = args.ppo_model_path
# Path: model_config.py
CUSTOM_STOP = args.custom_stop
# Path: model_config.py
SAME_FOR_MANY_SAMPLES = args.same_for_many_samples
# Path: model_config.py
BEAM_SEARCH = args.beam_search
# Path: model_config.py
MODEL_ARG_TOP_K = args.model_arg_topk
# Path: model_config.py
MODEL_ARG_TOP_P = args.model_arg_topp
# Path: model_config.py
MODEL_ARG_TEMP = args.model_arg_temp
# Path: huggingface_generate.py
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from peft import PeftModel
from lang_config import STOP_WORD
from model_config import BASE_MODEL_NAME, PEFT_MODEL_PATH, PPO_MODEL_PATH, CUSTOM_STOP, SAME_FOR_MANY_SAMPLES, BEAM_SEARCH, MODEL_ARG_TOP_K, MODEL_ARG_TOP_P, MODEL_ARG_TEMP
from typing import List
def load_model(
base_model_name: str = BASE_MODEL_NAME,
ppo_model_path: str = PPO_MODEL_PATH,
peft_model_path: str = PEFT_MODEL_PATH,
) -> (AutoModelForCausalLM, PeftModel, AutoTokenizer):
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
if ppo_model_path is None:
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
use_auth_token=True,
)
tokenizer = AutoTokenizer.from_pretrained(
base_model_name, trust_remote_code=True
)
else:
base_model = AutoModelForCausalLMWithValueHead.from_pretrained(
ppo_model_path, quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(ppo_model_path)
tokenizer.pad_token = tokenizer.eos_token
model = (
PeftModel.from_pretrained(base_model, peft_model_path)
if peft_model_path
else base_model
)
return (base_model, model, tokenizer)
def stop_words_ids(tokenizer: AutoTokenizer) -> List[int]:
# Hack: we want the stop word as it is encoded glued to another word.
stop_word_id = tokenizer.encode("hello" + STOP_WORD, add_special_tokens=False)[-1]
quote_word_id = tokenizer.encode("```", add_special_tokens=False)[-1]
return [stop_word_id, quote_word_id]
def get_model_generation_token_args(
tokenizer: AutoTokenizer, custom_stop: bool = CUSTOM_STOP
):
return dict(
min_length=5,
max_new_tokens=100,
eos_token_id=stop_words_ids(tokenizer)
if custom_stop
else tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
def get_model_generation_search_args(
num: int,
beam_search: bool = BEAM_SEARCH
):
if beam_search:
return dict(
num_beams=num,
num_beam_groups=num,
diversity_penalty=0.9,
)
else:
return dict(
top_k=MODEL_ARG_TOP_K if MODEL_ARG_TOP_K is not None else 50 if num>1 and not SAME_FOR_MANY_SAMPLES else 7,
| top_p=MODEL_ARG_TOP_P if MODEL_ARG_TOP_P is not None else 0.9, |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: BraveGroup/Drive-WM
# Path: src/diffusers/dependency_versions_check.py
def dep_version_check(pkg, hint=None):
require_version(deps[pkg], hint)
# Path: src/diffusers/utils/import_utils.py
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
# Path: src/diffusers/utils/import_utils.py
def is_peft_available():
return _peft_available
# Path: src/diffusers/utils/import_utils.py
def is_transformers_available():
return _transformers_available
# Path: src/diffusers/utils/constants.py
import importlib
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
from packaging import version
from ..dependency_versions_check import dep_version_check
from .import_utils import ENV_VARS_TRUE_VALUES, is_peft_available, is_transformers_available
# 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.
default_cache_path = HUGGINGFACE_HUB_CACHE
MIN_PEFT_VERSION = "0.6.0"
MIN_TRANSFORMERS_VERSION = "4.34.0"
_CHECK_PEFT = os.environ.get("_CHECK_PEFT", "1") in ENV_VARS_TRUE_VALUES
CONFIG_NAME = "config.json"
WEIGHTS_NAME = "diffusion_pytorch_model.bin"
FLAX_WEIGHTS_NAME = "diffusion_flax_model.msgpack"
ONNX_WEIGHTS_NAME = "model.onnx"
SAFETENSORS_WEIGHTS_NAME = "diffusion_pytorch_model.safetensors"
ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb"
HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
DIFFUSERS_CACHE = default_cache_path
DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
# Below should be `True` if the current version of `peft` and `transformers` are compatible with
# PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are
# available.
# For PEFT it is has to be greater than or equal to 0.6.0 and for transformers it has to be greater than or equal to 4.34.0.
_required_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) >= version.parse(MIN_PEFT_VERSION)
| _required_transformers_version = is_transformers_available() and version.parse( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: basnijholt/unidep
# Path: unidep/_dependencies_parsing.py
def find_requirements_files(
base_dir: str | Path = ".",
depth: int = 1,
*,
verbose: bool = False,
) -> list[Path]:
"""Scan a directory for `requirements.yaml` and `pyproject.toml` files."""
base_path = Path(base_dir)
found_files = []
# Define a helper function to recursively scan directories
def _scan_dir(path: Path, current_depth: int) -> None:
if verbose:
print(f"🔍 Scanning in `{path}` at depth {current_depth}")
if current_depth > depth:
return
for child in path.iterdir():
if child.is_dir():
_scan_dir(child, current_depth + 1)
elif child.name == "requirements.yaml":
found_files.append(child)
if verbose:
print(f'🔍 Found `"requirements.yaml"` at `{child}`')
elif child.name == "pyproject.toml" and unidep_configured_in_toml(child):
if verbose:
print(f'🔍 Found `"pyproject.toml"` with dependencies at `{child}`')
found_files.append(child)
_scan_dir(base_path, 0)
return sorted(found_files)
# Path: unidep/_dependencies_parsing.py
def parse_local_dependencies(
*paths: Path,
check_pip_installable: bool = True,
verbose: bool = False,
) -> dict[Path, list[Path]]:
"""Extract local project dependencies from a list of `requirements.yaml` or `pyproject.toml` files.
Works by loading the specified `local_dependencies` list.
""" # noqa: E501
dependencies: dict[str, set[str]] = defaultdict(set)
for p in paths:
if verbose:
print(f"🔗 Analyzing dependencies in `{p}`")
base_path = p.resolve().parent
_extract_local_dependencies(
path=p,
base_path=base_path,
processed=set(),
dependencies=dependencies,
check_pip_installable=check_pip_installable,
verbose=verbose,
)
return {
Path(k): sorted({Path(v) for v in v_set})
for k, v_set in sorted(dependencies.items())
}
# Path: unidep/_pytest_plugin.py
import os
import sys
import pytest
from pathlib import Path
from typing import TYPE_CHECKING
from unidep._dependencies_parsing import (
find_requirements_files,
parse_local_dependencies,
)
from git import Repo
"""unidep - Unified Conda and Pip requirements management.
Pytest plugin for running only tests of changed files.
WARNING: Still experimental and not documented.
"""
from __future__ import annotations
if TYPE_CHECKING:
def pytest_addoption(parser: pytest.Parser) -> None: # pragma: no cover
"""Add options to the pytest command line."""
parser.addoption(
"--run-affected",
action="store_true",
default=False,
help="Run only tests from affected packages",
)
parser.addoption(
"--branch",
action="store",
default="origin/main",
help="Branch to compare with for finding affected tests",
)
parser.addoption(
"--repo-root",
action="store",
default=".",
type=Path,
help="Root of the repository",
)
def pytest_collection_modifyitems(
config: pytest.Config,
items: list[pytest.Item],
) -> None: # pragma: no cover
"""Filter tests based on the --run-affected option."""
if not config.getoption("--run-affected"):
return
try:
except ImportError:
print(
"🛑 You need to install `gitpython` to use the `--run-affected` option."
"run `pip install gitpython` to install it.",
)
sys.exit(1)
compare_branch = config.getoption("--branch")
repo_root = Path(config.getoption("--repo-root")).absolute()
repo = Repo(repo_root)
| found_files = find_requirements_files(repo_root) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: BAAI-DCAI/SegVol
# Path: segment_anything_volumetric/modeling/common.py
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
# Path: segment_anything_volumetric/modeling/common.py
class MLPBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
mlp_dim: int,
act: Type[nn.Module] = nn.GELU,
) -> None:
super().__init__()
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
self.act = act()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.lin2(self.act(self.lin1(x)))
# Path: segment_anything_volumetric/modeling/image_encoder.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common import LayerNorm2d, MLPBlock
from monai.networks.blocks import PatchEmbed
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
class ImageEncoderViT(nn.Module):
def __init__(
self,
img_size: int = 1024,
patch_size: int = 16,
in_chans: int = 1,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
out_chans: int = 256,
qkv_bias: bool = True,
norm_layer: Type[nn.Module] = nn.LayerNorm,
act_layer: Type[nn.Module] = nn.GELU,
use_abs_pos: bool = True,
use_rel_pos: bool = False,
rel_pos_zero_init: bool = True,
window_size: int = 0,
global_attn_indexes: Tuple[int, ...] = (),
) -> None:
"""
Args:
img_size (int): Input image size.
patch_size (int): Patch size.
in_chans (int): Number of input image channels.
embed_dim (int): Patch embedding dimension.
depth (int): Depth of ViT.
num_heads (int): Number of attention heads in each ViT block.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool): If True, add a learnable bias to query, key, value.
norm_layer (nn.Module): Normalization layer.
act_layer (nn.Module): Activation layer.
use_abs_pos (bool): If True, use absolute positional embeddings.
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
window_size (int): Window size for window attention blocks.
global_attn_indexes (list): Indexes for blocks using global attention.
"""
super().__init__()
self.img_size = img_size
# self.patch_embed = PatchEmbed(
# kernel_size=(patch_size, patch_size),
# stride=(patch_size, patch_size),
# in_chans=in_chans,
# embed_dim=embed_dim,
# )
self.patch_embed = PatchEmbed(
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
spatial_dims=3,
)
self.pos_embed: Optional[nn.Parameter] = None
if use_abs_pos:
# Initialize absolute positional embedding with pretrain image size.
self.pos_embed = nn.Parameter(
torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
)
self.blocks = nn.ModuleList()
for i in range(depth):
block = Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
norm_layer=norm_layer,
act_layer=act_layer,
use_rel_pos=use_rel_pos,
rel_pos_zero_init=rel_pos_zero_init,
window_size=window_size if i not in global_attn_indexes else 0,
input_size=(img_size // patch_size, img_size // patch_size),
)
self.blocks.append(block)
self.neck = nn.Sequential(
nn.Conv2d(
embed_dim,
out_chans,
kernel_size=1,
bias=False,
),
| LayerNorm2d(out_chans), |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: xk-huang/segment-caption-anything
# Path: scripts/tools/utils/git_utils/common.py
def qd_tqdm(*args, **kwargs):
desc = kwargs.get("desc", "")
import inspect
frame = inspect.currentframe()
frames = inspect.getouterframes(frame)
frame = frames[1].frame
line_number = frame.f_lineno
fname = op.basename(frame.f_code.co_filename)
message = "{}:{}".format(fname, line_number)
if "desc" in kwargs:
kwargs["desc"] = message + " " + desc
else:
kwargs["desc"] = message
if "mininterval" not in kwargs:
# every 2 secons; default is 0.1 second which is too frequent
kwargs["mininterval"] = 2
return tqdm(*args, **kwargs)
# Path: scripts/tools/utils/git_utils/common.py
def dict_update_path_value(d, p, v):
ps = p.split("$")
while True:
if len(ps) == 1:
d[ps[0]] = v
break
else:
if ps[0] not in d:
d[ps[0]] = {}
d = d[ps[0]]
ps = ps[1:]
# Path: scripts/tools/utils/git_utils/common.py
def dict_get_path_value(d, p, with_type=False):
ps = p.split("$")
cur_dict = d
while True:
if len(ps) > 0:
k = dict_parse_key(ps[0], with_type)
if isinstance(cur_dict, (tuple, list)):
cur_dict = cur_dict[int(k)]
else:
cur_dict = cur_dict[k]
ps = ps[1:]
else:
return cur_dict
# Path: scripts/tools/utils/git_utils/common.py
def get_all_path(d, with_type=False, leaf_only=True, with_list=True):
assert not with_type, "will not support"
all_path = []
if isinstance(d, dict):
for k, v in d.items():
all_sub_path = get_all_path(
v, with_type, leaf_only=leaf_only, with_list=with_list
)
all_path.extend([k + "$" + p for p in all_sub_path])
if not leaf_only or len(all_sub_path) == 0:
all_path.append(k)
elif (isinstance(d, tuple) or isinstance(d, list)) and with_list:
for i, _v in enumerate(d):
all_sub_path = get_all_path(
_v,
with_type,
leaf_only=leaf_only,
with_list=with_list,
)
all_path.extend(["{}$".format(i) + p for p in all_sub_path])
if not leaf_only or len(all_sub_path) == 0:
all_path.append("{}".format(i))
return all_path
# Path: scripts/tools/utils/git_utils/common.py
def load_from_yaml_str(s):
return yaml.load(s, Loader=yaml.UnsafeLoader)
# Path: scripts/tools/utils/git_utils/tsv_io.py
import numpy as np
import shutil
import mmap
import time
import logging
import types
import os
import os.path as op
import subprocess
import tempfile
import hashlib
import logging
import struct
from .common import qd_tqdm as tqdm
from .common import (
dict_update_path_value,
dict_get_path_value,
get_all_path,
load_from_yaml_str,
)
from azfuse import File
from contextlib import contextmanager
from datasets.utils.filelock import FileLock
from urllib.parse import urlparse, urlunparse
from pathos.multiprocessing import ProcessingPool as Pool
# NOTE(xiaoke): Modified. Try to use azfuse.File if possible.
try:
except ImportError:
File = types.SimpleNamespace()
File.open = open
File.get_file_size = lambda x: os.stat(x).st_size
logger = logging.getLogger(__name__)
def concat_files(ins, out):
File.prepare(ins)
with File.open(out, "wb") as fp_out:
for i, f in enumerate(ins):
logging.info("concating {}/{} - {}".format(i, len(ins), f))
with File.open(f, "rb") as fp_in:
shutil.copyfileobj(fp_in, fp_out, 1024 * 1024 * 10)
def concat_tsv_files(tsvs, out_tsv):
if len(tsvs) == 1 and tsvs[0] == out_tsv:
return
File.prepare(tsvs)
concat_files(tsvs, out_tsv)
sizes = [File.get_file_size(t) for t in tsvs]
sizes = np.cumsum(sizes)
sizes = [0] + sizes[:-1].tolist()
concate_lineidx_8b(sizes, tsvs, out_tsv)
def get_tmp_folder():
folder = os.environ.get("GIT_TMP_FOLDER", "/tmp")
return folder
def parallel_map(func, all_task, num_worker=16):
if num_worker > 0:
with Pool(num_worker) as m:
result = m.map(func, all_task)
return result
else:
result = []
for t in all_task:
result.append(func(t))
return result
def ensure_remove_file(d):
if op.isfile(d) or op.islink(d):
try:
os.remove(d)
except:
pass
def concate_lineidx_8b(sizes, tsvs, out_tsv):
File.prepare(tsvs)
folder = get_tmp_folder()
def row_processor_8b(row):
offset, in_tsv, out_tsv = row
| fbar = tqdm(unit_scale=True) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: fjzzq2002/is-my-problem-new
# Path: src/utils.py
def read_problems(filename):
# read as a json
with open(filename) as f:
problems = json.load(f)
return [x for x in problems if len(x["statement"].strip()) >= 5]
# Path: src/utils.py
def dump_json_safe(obj, filename):
import tempfile
with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
json.dump(obj, f)
shutil.move(f.name, filename)
# Path: src/utils.py
def get_text(tag: bs4.Tag) -> str:
_inline_elements = {
"a",
"span",
"em",
"strong",
"u",
"i",
"font",
"mark",
"label",
"s",
"sub",
"sup",
"tt",
"bdo",
"button",
"cite",
"del",
"b",
"a",
"font",
}
def _get_text(tag: bs4.Tag) -> typing.Generator:
for child in tag.children:
if isinstance(child, bs4.Tag):
# if the tag is a block type tag then yield new lines before after
is_block_element = child.name not in _inline_elements
if is_block_element:
yield "\n"
yield from ["\n"] if child.name == "br" else _get_text(child)
if is_block_element:
yield "\n"
elif isinstance(child, bs4.NavigableString):
yield child.string
return "".join(_get_text(tag))
# Path: src/scrapper/codeforces.py
from ..utils import read_problems, dump_json_safe, get_text
from bs4 import BeautifulSoup
from tqdm.auto import tqdm
import json
import os
import requests
import time
import random
scrapped_problems = []
try:
scrapped_problems = read_problems("problems/codeforces.json")
print(f"Recalled {len(scrapped_problems)} scrapped problems")
except:
print("Cannot find scrapped problems")
scrapped_uids = set(p["uid"] for p in scrapped_problems)
codeforces_endpoint = "https://codeforces.com/api/problemset.problems"
# get list of problems
list_problems = requests.get(codeforces_endpoint).json()["result"]["problems"]
# the website is down, read problems.txt instead
# with open('problems.txt') as f:
# list_problems = json.load(f)['result']['problems']
print("# problems:", len(list_problems))
# a scrapper for codeforces
def scrap_problem(contestId, index, rating, tags, uid):
url = f"https://codeforces.com/contest/{contestId}/problem/{index}"
response = requests.get(url, timeout=30)
soup = BeautifulSoup(response.content, "html.parser")
statement = soup.find(class_="problem-statement")
try:
statement.find(class_="header").decompose()
except:
pass
statement_body = statement.find("div")
statement_body = get_text(statement_body)
# \r -> \n, remove duplicate \n, strip
statement_body = (
statement_body.replace("\r", "\n")
.replace("\n\n", "\n")
.replace("$$$", "$")
.strip()
)
problem = {
"uid": uid,
"url": url,
"tags": tags,
# 'raw': str(response.content),
"statement": statement_body,
"contestId": contestId,
"index": index,
"rating": rating,
}
return problem
for problem in tqdm(list_problems):
contestId, index, rating, tags = (
problem["contestId"],
problem["index"],
problem.get("rating", -1),
problem["tags"],
)
uid = f"Codeforces{contestId}{index}"
if uid in scrapped_uids:
continue
print(f"Scrapping {uid}")
result = None
try:
result = scrap_problem(contestId, index, rating, tags, uid)
except Exception as e:
print("Error while scrapping:", e)
if result is not None:
scrapped_problems.append(result)
time.sleep(0.1)
# save to file every 10 problems
if random.random() < 0.1:
| dump_json_safe(scrapped_problems, "problems/codeforces.json") |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: p0p4k/pflowtts_pytorch
# Path: pflow/data/text_mel_datamodule.py
class TextMelDataModule(LightningDataModule):
def __init__( # pylint: disable=unused-argument
self,
name,
train_filelist_path,
valid_filelist_path,
batch_size,
num_workers,
pin_memory,
cleaners,
add_blank,
n_spks,
n_fft,
n_feats,
sample_rate,
hop_length,
win_length,
f_min,
f_max,
data_statistics,
seed,
):
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
def setup(self, stage: Optional[str] = None): # pylint: disable=unused-argument
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
careful not to execute things like random split twice!
"""
# load and split datasets only if not loaded already
self.trainset = TextMelDataset( # pylint: disable=attribute-defined-outside-init
self.hparams.train_filelist_path,
self.hparams.n_spks,
self.hparams.cleaners,
self.hparams.add_blank,
self.hparams.n_fft,
self.hparams.n_feats,
self.hparams.sample_rate,
self.hparams.hop_length,
self.hparams.win_length,
self.hparams.f_min,
self.hparams.f_max,
self.hparams.data_statistics,
self.hparams.seed,
)
self.validset = TextMelDataset( # pylint: disable=attribute-defined-outside-init
self.hparams.valid_filelist_path,
self.hparams.n_spks,
self.hparams.cleaners,
self.hparams.add_blank,
self.hparams.n_fft,
self.hparams.n_feats,
self.hparams.sample_rate,
self.hparams.hop_length,
self.hparams.win_length,
self.hparams.f_min,
self.hparams.f_max,
self.hparams.data_statistics,
self.hparams.seed,
)
def train_dataloader(self):
return DataLoader(
dataset=self.trainset,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=True,
collate_fn=TextMelBatchCollate(self.hparams.n_spks),
)
def val_dataloader(self):
return DataLoader(
dataset=self.validset,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
collate_fn=TextMelBatchCollate(self.hparams.n_spks),
)
def teardown(self, stage: Optional[str] = None):
"""Clean up after fit or test."""
pass # pylint: disable=unnecessary-pass
def state_dict(self): # pylint: disable=no-self-use
"""Extra things to save to checkpoint."""
return {}
def load_state_dict(self, state_dict: Dict[str, Any]):
"""Things to do when loading checkpoint."""
pass # pylint: disable=unnecessary-pass
# Path: pflow/utils/logging_utils.py
def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
# Path: pflow/utils/generate_data_statistics.py
import os
import sys
import argparse
import json
import sys
import rootutils
import torch
from pathlib import Path
from hydra import compose, initialize
from omegaconf import open_dict
from tqdm.auto import tqdm
from pflow.data.text_mel_datamodule import TextMelDataModule
from pflow.utils.logging_utils import pylogger
r"""
The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it
when needed.
Parameters from hparam.py will be used
"""
sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
| log = pylogger.get_pylogger(__name__) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: theroyallab/tabbyAPI
# Path: args.py
def convert_args_to_dict(args: argparse.Namespace, parser: argparse.ArgumentParser):
"""Broad conversion of surface level arg groups to dictionaries"""
arg_groups = {}
for group in parser._action_groups:
group_dict = {}
for arg in group._group_actions:
value = getattr(args, arg.dest, None)
if value is not None:
group_dict[arg.dest] = value
arg_groups[group.title] = group_dict
return arg_groups
# Path: args.py
def init_argparser():
"""Creates an argument parser that any function can use"""
parser = argparse.ArgumentParser(
epilog="These args are only for a subset of the config. "
+ "Please edit config.yml for all options!"
)
add_network_args(parser)
add_model_args(parser)
add_logging_args(parser)
add_config_args(parser)
return parser
# Path: start.py
import argparse
import os
import pathlib
import subprocess
from args import convert_args_to_dict, init_argparser
from main import entrypoint
"""Utility to automatically upgrade and start the API"""
def get_requirements_file():
"""Fetches the appropriate requirements file depending on the GPU"""
requirements_name = "requirements-nowheel"
ROCM_PATH = os.environ.get("ROCM_PATH")
CUDA_PATH = os.environ.get("CUDA_PATH")
# TODO: Check if the user has an AMD gpu on windows
if ROCM_PATH:
requirements_name = "requirements-amd"
# Also override env vars for ROCm support on non-supported GPUs
os.environ["ROCM_PATH"] = "/opt/rocm"
os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.0"
os.environ["HCC_AMDGPU_TARGET"] = "gfx1030"
elif CUDA_PATH:
cuda_version = pathlib.Path(CUDA_PATH).name
if "12" in cuda_version:
requirements_name = "requirements"
elif "11" in cuda_version:
requirements_name = "requirements-cu118"
return requirements_name
def add_start_args(parser: argparse.ArgumentParser):
"""Add start script args to the provided parser"""
start_group = parser.add_argument_group("start")
start_group.add_argument(
"-iu",
"--ignore-upgrade",
action="store_true",
help="Ignore requirements upgrade",
)
start_group.add_argument(
"-nw",
"--nowheel",
action="store_true",
help="Don't upgrade wheel dependencies (exllamav2, torch)",
)
if __name__ == "__main__":
subprocess.run(["pip", "-V"])
# Create an argparser and add extra startup script args
parser = init_argparser()
add_start_args(parser)
args = parser.parse_args()
if args.ignore_upgrade:
print("Ignoring pip dependency upgrade due to user request.")
else:
requirements_file = (
"requirements-nowheel" if args.nowheel else get_requirements_file()
)
subprocess.run(["pip", "install", "-U", "-r", f"{requirements_file}.txt"])
# Import entrypoint after installing all requirements
| entrypoint(convert_args_to_dict(args, parser)) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: zorazrw/filco
# Path: cxmi.py
def calc_cxmi_score(
model: AutoModelForSeq2SeqLM,
tokenizer: AutoTokenizer,
answer: str,
base_input: str,
ctx_input: str,
apply_sigmoid: bool = False,
) -> float:
"""Compute the CXMI score."""
base_probs = get_output_probs(model, tokenizer, base_input, answer)
ctx_probs = get_output_probs(model, tokenizer, ctx_input, answer)
diff = sent_wise_diff(base_scores=base_probs, ctx_scores=ctx_probs)
if apply_sigmoid:
diff = sigmoid(diff)
return diff
# Path: cxmi.py
def get_example_inputs(
question: str,
context: str,
answers: list[str],
question_prefix: str = "question",
context_prefix: str = "context",
) -> dict:
"""Get example inputs for the generation model."""
base_input = get_input_text(
question,
context=None,
question_prefix=question_prefix,
context_prefix=context_prefix,
)
ctx_input = get_input_text(
question,
context=context,
question_prefix=question_prefix,
context_prefix=context_prefix,
)
return {
"base_input": base_input,
"ctx_input": ctx_input,
"answers": answers,
}
# Path: eval.py
def calc_unigram_f1(text: str, answers: list[str], field: str = "f1") -> float:
"""Calculate unigram f1 score between the text and reference answers."""
norm_pred = normalize_text(text)
norm_answers = [normalize_text(ans) for ans in answers]
common_tokens = [
Counter(norm_pred) & Counter(norm_ans) for norm_ans in norm_answers
]
num_same = [sum(common.values()) for common in common_tokens]
score_list = []
for i, num in enumerate(num_same):
if num == 0:
score_list.append(0.0)
else:
p = 1.0 * num / len(norm_pred)
r = 1.0 * num / len(norm_answers[i])
f1 = 2 * p * r / (p + r)
if field == "precision":
score_list.append(p)
elif field == "recall":
score_list.append(r)
elif field == "f1":
score_list.append(f1)
else:
raise ValueError(f"Unknown field: {field}")
return max(score_list)
# Path: eval.py
def has_answer(text: str, answers: list[str]) -> float:
"""Check if text contains any of the answers."""
return float(any([(ans.lower() in text.lower()) for ans in answers]))
# Path: utils.py
def load_dataset(path: str) -> list[dict]:
"""Load dataset from JSON or JSONL file."""
if path.endswith(".json"):
return json.load(open(path, "r"))
elif path.endswith(".jsonl"):
return [json.loads(line.strip()) for line in open(path, "r")]
else:
extension = path.split(".")[-1]
raise ValueError(f"File extension [{extension}] not valid.")
# Path: utils.py
def write_dataset(path: str, dataset: list[dict]):
"""Write dataset to JSON or JSONL file."""
if path.endswith(".json"):
json.dump(dataset, open(path, "w"))
elif path.endswith(".jsonl"):
with open(path, "w") as fw:
for res_dict in dataset:
fw.write(json.dumps(res_dict) + "\n")
else:
extension = path.split(".")[-1]
raise ValueError(f"File extension [{extension}] not valid.")
# Path: measure_ctxs.py
import argparse
import torch
from nltk.tokenize import sent_tokenize
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from cxmi import calc_cxmi_score, get_example_inputs
from eval import calc_unigram_f1, has_answer
from utils import load_dataset, write_dataset
"""Calculate Scores of Individual Sentences in Retrieved Passages."""
def calc_cxmi(
text: str,
question: str,
answers: list[str],
tokenizer: AutoTokenizer,
model: AutoModelForSeq2SeqLM,
) -> float:
"""Calculate CXMI score for a context text."""
proc_inputs = get_example_inputs(
question=args.prefix + question,
context=text,
answers=answers,
)
cxmi_score = calc_cxmi_score(
model=model,
tokenizer=tokenizer,
answer=proc_inputs["answers"][0],
base_input=proc_inputs["base_input"],
ctx_input=proc_inputs["ctx_input"],
apply_sigmoid=True,
)
return cxmi_score
def main():
"""Run the main context measuring function."""
# load dataset
| dataset = load_dataset(args.dataset_path) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ShipBit/wingman-ai
# Path: gui/sections/context_switcher.py
class ContextSwitcher(ctk.CTkFrame):
# class ContextSwitcher(ctk.CTkScrollableFrame):
def __init__(self, master, **kwargs):
super().__init__(master, **kwargs)
self.grid_columnconfigure(0, weight=1)
self.master = master
self.contexts = master.core.config_manager.contexts
self.context_buttons = {}
self.active_context = ""
self.spacer = ctk.CTkLabel(self, text="")
self.spacer.grid(row=0, column=0)
for i, context_name in enumerate(self.contexts):
context_button = IconButton(self,
icon=f"context-icon_{CONTEXT_COLORS[i % len(CONTEXT_COLORS)]}" if context_name else "context-icon",
themed=False,
command=lambda c=context_name: self.activate_context(c))
context_button.grid(row=i+1, column=0, padx=14, pady=14)
self.context_buttons[context_name] = context_button
if not context_name:
self.__set_context_button_state("", False)
def __set_context_button_state(self, context, active=True):
context_button = self.context_buttons.get(context)
if context_button:
context_button.configure(state="normal" if active else "disabled",
fg_color="transparent" if active else ("grey60", "grey40"))
def activate_context(self, context):
self.__set_context_button_state(self.active_context, True)
if self.master:
update_context = getattr(self.master, "update_context", None)
if callable(update_context):
update_context(context)
self.active_context = context
self.__set_context_button_state(self.active_context, False)
# Path: gui/sections/context_runner.py
class ContextRunner(ctk.CTkFrame):
def __init__(self, master, context="", **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.core.load_context(context)
self.status_var = ctk.StringVar(self, "Inactive", "status")
tower = self.core.tower
auto_run = self.core.config_manager.gui_config.get("auto-run", "off") == "on"
self.grid_columnconfigure(0, weight=1)
self.grid_rowconfigure(3, weight=1)
context_title = (
context.title().replace("_", " ").strip() if context else "Default"
)
self.title = ctk.CTkLabel(
self,
text=context_title,
font=("TkHeadingFont", 20, "bold"),
text_color="#EB154D",
)
self.title.grid(row=0, column=0, padx=20, pady=10, sticky="w")
# TODO: Make this a component
self.status = ctk.CTkLabel(
self,
textvariable=self.status_var,
anchor="w",
fg_color=("grey70", "grey30"),
corner_radius=10,
width=65,
pady=3,
)
self.status.grid(row=0, column=0, padx=20, pady=10, sticky="e")
self.status_icon_active = Icon("state_active", 16, False)
self.status_icon_inactive = Icon("state_inactive", 16, False)
self.status_led = ctk.CTkLabel(
self, image=self.status_icon_inactive, text="", fg_color="transparent"
)
self.status_led.grid(row=0, column=0, padx=95, pady=10, sticky="e")
wingmen = []
if tower:
wingmen = tower.get_wingmen()
self.wingmen_list = WingmenList(self, wingmen=wingmen)
self.wingmen_list.grid(row=1, column=0, padx=20, pady=10, sticky="ew")
broken_wingmen = []
if tower:
broken_wingmen = tower.get_broken_wingmen()
if len(broken_wingmen) > 0:
self.broken_wingmen_list = WingmenList(
self, wingmen=broken_wingmen, broken=True
)
self.broken_wingmen_list.grid(
row=2, column=0, padx=20, pady=10, sticky="ew"
)
self.terminal = ctk.CTkTextbox(self)
self.terminal.grid(row=3, column=0, padx=20, pady=10, sticky="nesw")
self.terminal.configure(state="disabled", wrap="word")
printr.set_output("main", self.terminal)
if len(wingmen) and not auto_run:
printr.print(
f"Press 'Run' to start your wingm{'e' if len(wingmen) > 1 else 'a'}n!"
)
self.button = ctk.CTkButton(
self,
text="Run",
command=self.toggle_listener,
height=45,
font=("TkHeadingFont", 22, "bold"),
)
self.button.grid(row=4, column=0, padx=20, pady=10, sticky="ew")
if not tower:
printr.print_err(
f"Could not load context.\nPlease check your context configuration for `{context_title}`."
)
self.button.configure(state="disabled")
elif len(wingmen) <= 0:
printr.print_warn(f"No runnable Wingman found for `{context_title}`.")
self.button.configure(state="disabled")
elif auto_run:
self.toggle_listener()
def toggle_listener(self):
if self.core.active:
self.core.deactivate()
self.status_var.set("Inactive")
self.status_led.configure(image=self.status_icon_inactive)
self.button.configure(text="Run")
printr.print(
"Your Wingman is now inactive.\nPress 'Run' to start listening again."
)
else:
self.core.activate()
self.status_var.set("Active")
self.status_led.configure(image=self.status_icon_active)
self.button.configure(text="Stop")
printr.print(
"Your Wingman is now listening for commands.\nPress 'Stop' to stop listening."
)
# Path: gui/views/context_view.py
import customtkinter as ctk
from gui.sections.context_switcher import ContextSwitcher
from gui.sections.context_runner import ContextRunner
class ContextView(ctk.CTkFrame):
def __init__(self, master, **kwargs):
super().__init__(master, **kwargs)
self.core = master.core
self.grid_columnconfigure(1, weight=1)
self.grid_rowconfigure(0, weight=1)
| self.context_switcher = ContextSwitcher(self, width=88, corner_radius=0) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: OliverMao/FlaskAutoApiBuilder
# Path: Faab/Faab.py
class Faab(Flask):
_startup_message_printed = False
models = []
db_config = object()
need_register_bp = []
def __init__(self, **options):
# 初始化函数，接收一个字符串类型的参数import_name
super().__init__(**options)
def add_models(self, model: list):
# 添加模型函数，接收一个列表类型的参数model
self.models = model
def add_db_config(self, db_config: object):
# 添加数据库配置函数，接收一个对象类型的参数db_config
self.db_config = db_config
def add_blueprints(self, blueprint: list):
# 添加蓝图函数，接收一个列表类型的参数blueprint
self.need_register_bp = blueprint
def faab_ready(self):
create_app(app=self, models=self.models, db_config=self.db_config, url_prefix="/api",
blueprints=self.need_register_bp)
CORS(self, resources=r'/*')
self._print_startup_message()
def run(
self,
host: str | None = None,
port: int | None = None,
debug: bool | None = None,
load_dotenv: bool = True,
**options: t.Any,
) -> None:
super().run(host, port, debug, load_dotenv, **options)
def _print_startup_message(self):
if not getattr(self, '_startup_message_printed', False):
print("\033[1;32m * Faab Version:", version)
print('''
███████╗ █████╗ █████╗ ██████╗ ██╗ ██╗ ██████╗ ██████╗ ██████╗ ██╗████████╗ ██████╗███╗ ██╗
██╔════╝██╔══██╗██╔══██╗██╔══██╗ ╚██╗ ██╔╝██╔═══██╗██╔═══██╗██╔══██╗██║╚══██╔══╝██╔════╝████╗ ██║
█████╗ ███████║███████║██████╔╝█████╗╚████╔╝ ██║ ██║██║ ██║██████╔╝██║ ██║ ██║ ██╔██╗ ██║
██╔══╝ ██╔══██║██╔══██║██╔══██╗╚════╝ ╚██╔╝ ██║ ██║██║ ██║██╔══██╗██║ ██║ ██║ ██║╚██╗██║
██║ ██║ ██║██║ ██║██████╔╝ ██║ ╚██████╔╝╚██████╔╝██████╔╝██║ ██║██╗╚██████╗██║ ╚████║
''')
self._startup_message_printed = True
# Path: Faab/FaabJWT.py
def jwt_authentication():
"""
1.获取请求头Authorization中的token
2.判断是否以 Bearer开头
3.使用jwt模块进行校验
4.判断校验结果,成功就提取token中的载荷信息,赋值给g对象保存
"""
auth = request.headers.get('Authorization')
if auth and auth.startswith('Bearer '):
"提取token 0-6 被Bearer和空格占用 取下标7以后的所有字符"
token = auth[7:]
"校验token"
g.username = None
try:
"判断token的校验结果"
payload = jwt.decode(token, SALT, algorithms=['HS256'])
"获取载荷中的信息赋值给g对象"
g.username = payload.get('username')
except exceptions.ExpiredSignatureError: # 'token已失效'
g.username = -1
except jwt.DecodeError: # 'token认证失败'
g.username = -2
except jwt.InvalidTokenError: # '非法的token'
g.username = -3
# print(g.username)
# Path: demo/app.py
from Faab import Faab
from Faab.FaabJWT import jwt_authentication
from blueprints.test import test_bp
from blueprints.test.model import Users
import factory as fac
# Faab Project Demo
class DBConfig(object):
# 基础配置
user = 'faab'
host = 'localhost'
password = 'faab'
SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, 'faab')
SQLALCHEMY_BINDS = {
'test': 'mysql+pymysql://%s:%s@%s:3306/%s' % (user, password, host, 'test')
}
SECRET_KEY = 'session_key'
models = [
[
{
"model": Users,
"bp": test_bp,
"url_prefix": "Users"
}
]
]
app = Faab(import_name=__name__, static_url_path='/s')
app.add_models(models)
app.add_db_config(DBConfig)
fac.register(app)
app.faab_ready()
application = app # uWSGI启动必须有application
@app.before_request
def auth():
| jwt_authentication() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: leeyuentuen/polestar_api
# Path: custom_components/polestar_api/pypolestar/auth.py
class PolestarAuth:
"""base class for Polestar authentication."""
def __init__(self, username: str, password: str) -> None:
"""Initialize the Polestar authentication."""
self.username = username
self.password = password
self.access_token = None
self.refresh_token = None
self.token_expiry = None
self.latest_call_code = None
self._client_session = httpx.AsyncClient()
async def get_token(self, refresh=False) -> None:
"""Get the token from Polestar."""
headers = {"Content-Type": "application/json"}
operationName = "getAuthToken"
if not refresh:
code = await self._get_code()
if code is None:
return
params = {
"query": "query getAuthToken($code: String!) { getAuthToken(code: $code) { id_token access_token refresh_token expires_in }}",
"operationName": operationName,
"variables": json.dumps({"code": code}),
}
else:
if self.refresh_token is None:
return
token = self.refresh_token
operationName = "refreshAuthToken"
headers["Authorization"] = f"Bearer {self.access_token}"
params = {
"query": "query refreshAuthToken($token: String!) { refreshAuthToken(token: $token) { id_token access_token refresh_token expires_in }}",
"operationName": operationName,
"variables": json.dumps({"token": token}),
}
result = await self._client_session.get("https://pc-api.polestar.com/eu-north-1/auth/", params=params, headers=headers, timeout=HTTPX_TIMEOUT)
self.latest_call_code = result.status_code
resultData = result.json()
if result.status_code != 200 or ("errors" in resultData and len(resultData["errors"])):
_LOGGER.error(result)
raise PolestarAuthException("Error getting token", result.status_code)
_LOGGER.debug(resultData)
if resultData['data']:
self.access_token = resultData['data'][operationName]['access_token']
self.refresh_token = resultData['data'][operationName]['refresh_token']
self.token_expiry = datetime.now(
) + timedelta(seconds=resultData['data'][operationName]['expires_in'])
# ID Token
_LOGGER.debug(f"Response {self.access_token}")
async def _get_code(self) -> None:
query_params = await self._get_resume_path()
# check if code is in query_params
if query_params.get('code'):
return query_params.get('code')
# get the resumePath
if query_params.get('resumePath'):
resumePath = query_params.get('resumePath')
if resumePath is None:
return
params = {
'client_id': 'polmystar'
}
data = {
'pf.username': self.username,
'pf.pass': self.password
}
result = await self._client_session.post(
f"https://polestarid.eu.polestar.com/as/{resumePath}/resume/as/authorization.ping",
params=params,
data=data
)
self.latest_call_code = result.status_code
if result.status_code != 302:
raise PolestarAuthException("Error getting code", result.status_code)
# get the realUrl
url = result.url
code = result.next_request.url.params.get('code')
# sign-in-callback
result = await self._client_session.get(result.next_request.url, timeout=HTTPX_TIMEOUT)
self.latest_call_code = result.status_code
if result.status_code != 200:
_LOGGER.error(result)
raise PolestarAuthException("Error getting code callback", result.status_code)
# url encode the code
result = await self._client_session.get(url)
self.latest_call_code = result.status_code
return code
async def _get_resume_path(self):
"""Get Resume Path from Polestar."""
params = {
"response_type": "code",
"client_id": "polmystar",
"redirect_uri": "https://www.polestar.com/sign-in-callback"
}
result = await self._client_session.get("https://polestarid.eu.polestar.com/as/authorization.oauth2", params=params, timeout=HTTPX_TIMEOUT)
if result.status_code in (303, 302):
return result.next_request.url.params
_LOGGER.error(result.text)
raise PolestarAuthException("Error getting resume path ", result.status_code)
# Path: custom_components/polestar_api/pypolestar/const.py
BATTERY_DATA = "getBatteryData"
# Path: custom_components/polestar_api/pypolestar/const.py
CACHE_TIME = 30
# Path: custom_components/polestar_api/pypolestar/const.py
CAR_INFO_DATA = "getConsumerCarsV2"
# Path: custom_components/polestar_api/pypolestar/const.py
ODO_METER_DATA = "getOdometerData"
# Path: custom_components/polestar_api/pypolestar/exception.py
class PolestarApiException(Exception):
"""Base class for exceptions in this module."""
# Path: custom_components/polestar_api/pypolestar/exception.py
class PolestarAuthException(Exception):
"""Base class for exceptions in Auth module."""
error_code: int = None
message: str = None
def __init__(self, message, error_code) -> None:
"""Initialize the Polestar API."""
super().__init__(message)
self.error_code = error_code
# Path: custom_components/polestar_api/pypolestar/exception.py
class PolestarNoDataException(Exception):
"""Exception for no data."""
# Path: custom_components/polestar_api/pypolestar/exception.py
class PolestarNotAuthorizedException(Exception):
"""Exception for unauthorized call."""
# Path: custom_components/polestar_api/pypolestar/polestar.py
from datetime import datetime, timedelta
from .auth import PolestarAuth
from .const import BATTERY_DATA, CACHE_TIME, CAR_INFO_DATA, ODO_METER_DATA
from .exception import (
PolestarApiException,
PolestarAuthException,
PolestarNoDataException,
PolestarNotAuthorizedException,
)
import logging
import httpx
"""Asynchronous Python client for the Polestar API."""""
_LOGGER = logging.getLogger(__name__)
class PolestarApi:
"""Main class for handling connections with the Polestar API."""
def __init__(self, username: str, password: str) -> None:
"""Initialize the Polestar API."""
| self.auth = PolestarAuth(username, password) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: dubverse-ai/MahaTTS
# Path: maha_tts/config.py
class config:
semantic_model_centroids = 10000 + 1
seed_value = 3407
# Text to Semantic
t2s_position = 4096
langs = ['english','tamil', 'telugu', 'punjabi', 'marathi', 'hindi', 'gujarati', 'bengali', 'assamese']
lang_index = {i:j for j,i in enumerate(langs)}
# Semantic to acoustic
sa_timesteps_max = 1000
#Acoustic Properties
CLIP_LENGTH = 500
MAX_WAV_VALUE=32768.0
filter_length=1024
hop_length=256 #256
window = 'hann'
win_length=1024
n_mel_channels=80
sampling_rate=22050
mel_fmin=0.0
mel_fmax=8000.0
# Path: maha_tts/text/symbols.py
# Path: maha_tts/models/modules.py
class GST(nn.Module):
def __init__(self,model_channels=512,num_heads=8,in_channels=80,k=2):
super(GST,self).__init__()
self.model_channels=model_channels
self.num_heads=num_heads
self.reference_encoder=nn.Sequential(
nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
nn.Conv1d(model_channels, model_channels*k,3,padding=1,stride=2),
AttentionBlock(model_channels*k, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*k, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*k, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*k, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
AttentionBlock(model_channels*k, num_heads, relative_pos_embeddings=True, do_checkpoint=False)
)
def forward(self,x):
x=self.reference_encoder(x)
return x
# Path: maha_tts/models/autoregressive.py
import os,sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import functools
from typing import Any
from torch.utils.data import Dataset,DataLoader
from transformers import GPT2Tokenizer,GPT2Config, GPT2Model, GPT2LMHeadModel
from tqdm import tqdm
from maha_tts.config import config
from maha_tts.text.symbols import labels,code_labels,text_labels,text_labels_en
from maha_tts.models.modules import GST
'''
Inspiration taken from https://github.com/neonbjb/tortoise-tts/blob/main/tortoise/models/autoregressive.py
'''
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
class TS_model(nn.Module):
def __init__(self,n_embed = 512, n_layer = 16, n_head = 8, n_positions = 2048, name='Smolie-in'):
super(TS_model,self).__init__()
self.vocab_size=len(labels)
self.n_positions=n_positions
self.n_embed=n_embed
self.n_layer=n_layer
self.n_head=n_head
self.name=name
self.config = GPT2Config(vocab_size=self.vocab_size,n_positions=self.n_positions,n_embd=self.n_embed,n_layer=self.n_layer,n_head=self.n_head)
self.gpt = GPT2Model(self.config)
del self.gpt.wpe
self.gpt.wpe = functools.partial(null_position_embeddings, dim=self.n_embed)
# Built-in token embeddings are unused.
del self.gpt.wte
self.GST = GST(model_channels=self.n_embed,num_heads=self.n_head,in_channels=config.n_mel_channels,k=1)
if self.name == 'Smolie-en':
self.text_head = nn.Linear(self.n_embed,len(text_labels_en))
else:
| self.text_head = nn.Linear(self.n_embed,len(text_labels)) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: WCGKING/KINGUSERBOT
# Path: Branded/modules/data.py
async def is_gdel_user(user_id: int) -> bool:
user = await gdeldb.find_one({"user_id": user_id})
if not user:
return False
return True
# Path: Branded/modules/data.py
async def get_gdel_user() -> list:
results = []
async for user in gdeldb.find({"user_id": {"$gt": 0}}):
user_id = user["user_id"]
results.append(user_id)
return results
# Path: Branded/modules/data.py
async def get_gdel_count() -> int:
users = gdeldb.find({"user_id": {"$gt": 0}})
users = await users.to_list(length=100000)
return len(users)
# Path: Branded/modules/data.py
async def add_gdel_user(user_id: int):
is_gdel = await is_gdel_user(user_id)
if is_gdel:
return
return await gdeldb.insert_one({"user_id": user_id})
# Path: Branded/modules/data.py
async def del_gdel_user(user_id: int):
is_gdel = await is_gdel_user(user_id)
if not is_gdel:
return
return await gdeldb.delete_one({"user_id": user_id})
# Path: Branded/plugins/gdelete.py
import asyncio
from pyrogram import *
from pyrogram.types import Message
from .. import *
from ..modules.data import (is_gdel_user,
get_gdel_user, get_gdel_count,
add_gdel_user, del_gdel_user)
@app.on_message(commandx(["gdl", "gdel", "gdelete"]) & SUPUSER)
async def add_gdelete_user(client, message: Message):
if not message.reply_to_message:
if len(message.command) != 2:
return await message.reply_text("Reply to a user's message or give username/user_id.")
user = message.text.split(None, 1)[1]
user = await app.get_users(user)
user_id = user.id
mention = user.mention
else:
user_id = message.reply_to_message.from_user.id
mention = message.reply_to_message.from_user.mention
if user_id == message.from_user.id:
return await message.reply_text("You want to add Global Delete yourself? How Fool!")
elif user_id == SUPUSER:
return await message.reply_text("Should i activate Global Delete on myself? Lol")
elif user_id in SUDOERS:
return await message.reply_text("You want add Global Delete on sudo user?")
is_gdel = await is_gdel_user(user_id)
if is_gdel:
return await message.reply_text("{0} is already affected by **Global Delete**".format(mention))
if user_id not in GDELSUB:
GDELSUB.add(user_id)
| await add_gdel_user(user_id) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: kudelskisecurity/fuzzomatic
# Path: fuzzomatic/approaches/functions.py
def score_functions(functions):
interesting_function_names = ["parse", "load", "read", "str", "eval"]
# order functions by most interesting first
ordered_functions = []
for f in functions:
function_name = f[1]
args = f[2]
priority = 0
is_name_interesting = False
for pattern in interesting_function_names:
if pattern in function_name:
is_name_interesting = True
if len(args) == 1:
arg_type = args[0]
if arg_type == "&str":
priority = 100
elif arg_type == "&[u8]":
priority = 100
elif arg_type == "String":
priority = 100
elif arg_type == "bool":
priority = 0
elif arg_type == "unknown":
priority = 10
elif type(arg_type) == tuple and arg_type[0] == "&array":
priority = 100
elif is_name_interesting:
priority = 100
if args[0] == "self":
priority = -15
elif args[0] == "self":
# functions with "self" as first argument
priority = -50
else:
priority = 50
elif len(args) > 1:
known_types = 0
for arg in args:
if arg != "unknown":
known_types += 1
if known_types == len(args):
priority = 30
if "&str" in args or "&[u8]" in args or "String" in args:
priority = 75
if any(type(arg) == tuple and arg[0] == "&array" for arg in args):
priority = 75
else:
# functions with multiple arguments where not all types are known
priority = -10
if args[0] == "self":
# functions with "self" as first argument
priority = -50
else:
# skip functions with no arguments
priority = -100
# give low priority to functions that are likely to load something by filename
if "file" in function_name and arg_type == "&str":
priority = 0
augmented_function = [*f, priority]
ordered_functions.append(augmented_function)
ordered_functions = sorted(ordered_functions, key=lambda x: x[3], reverse=True)
return ordered_functions
# Path: fuzzomatic/tools/cargo_doc.py
def parse_cargo_doc_json(path):
with open(path) as f:
jso = json.loads(f.read())
# get functions that take only one parameter and that are public
root = jso["root"]
index = jso["index"]
root_elem = index[root]
root_inner_items = root_elem["inner"]["module"]["items"]
functions = []
for elem in root_inner_items:
path = []
e = index[elem]
funcs = parse_item(index, e, path)
functions.extend(funcs)
return functions
# Path: fuzzomatic/docparse.py
import argparse
from fuzzomatic.approaches.functions import score_functions
from fuzzomatic.tools.cargo_doc import parse_cargo_doc_json
#!/usr/bin/env python3
def get_parser():
prog_name = "docparse"
parser = argparse.ArgumentParser(
prog=prog_name,
description="Parse cargo doc json and print public functions",
)
parser.add_argument(
"json_path",
help="Path to cargo doc json file",
)
return parser
def main():
parser = get_parser()
args = parser.parse_args()
functions = parse_cargo_doc_json(args.json_path)
| ordered_functions = score_functions(functions) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: muyuworks/myla
# Path: myla/vectorstores/_base.py
class Record(Dict):
@staticmethod
def values_to_text(record: Dict, props: List[str] = None, separator: str = '\001'):
if props and not isinstance(props, list):
raise ValueError("props should be a list")
if props:
o = itemgetter(*props)
if len(props) == 1:
v = [o(record)]
else:
v = list(o(record))
else:
v = list(record.values())
vl = []
for i in v:
if not isinstance(i, str):
vl.append(json.dumps(i, ensure_ascii=False))
else:
vl.append(i)
v = vl
return separator.join(v)
# Path: myla/vectorstores/_base.py
class VectorStore(ABC):
def __init__(self) -> None:
pass
@abstractmethod
def create_collection(self, collection: str, schema: Dict[str, type] = None, mode="create"):
"""Create a new collection"""
@abstractmethod
def add(
self,
collection: str,
records: List[Record],
embeddings_columns: Optional[List[str]] = None,
vectors: Optional[List[List[float]]] = None,
**kwargs
):
"""Add record to the vectorsotre"""
@abstractmethod
def delete(self, collection: str, query: str):
"""Delete record from the vectorstore"""
@abstractmethod
def search(
self,
collection: str = None,
query: str = None,
vector: List = None,
filter: Any = None,
limit: int = 20,
columns: Optional[List[str]] = None,
with_vector: bool = False,
with_distance: bool = False,
**kwargs
) -> Optional[List[Record]]:
"""Search records"""
async def asearch(
self,
collection: str = None,
query: str = None,
vector: List = None,
filter: Any = None,
limit: int = 20,
columns: Optional[List[str]] = None,
with_vector: bool = False,
with_distance: bool = False,
**kwargs
):
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.search, **kwargs), collection, query, vector, filter, limit, columns, with_vector, with_distance
)
# Path: myla/vectorstores/_embeddings.py
class Embeddings(ABC):
@abstractmethod
def embed_batch(self, texts: List[str], **kwargs) -> List[List[float]]:
"""Embed text batch."""
def embed(self, text: str, **kwargs) -> List[float]:
"""Embed text."""
return self.embed_batch(texts=[text], **kwargs)[0]
async def aembed(self, text: str, **kwargs) -> List[float]:
"""Asynchronous Embed text."""
return await asyncio.get_running_loop().run_in_executor(
None, self.embed, text, **kwargs
)
async def aembed_batch(self, texts: [str], **kwargs) -> List[List[float]]:
"""Asynchronous Embed text."""
return await asyncio.get_running_loop().run_in_executor(
None, self.embed_batch, texts, **kwargs
)
# Path: myla/vectorstores/lancedb_vectorstore.py
from typing import Any, List, Optional, Dict
from ._base import Record, VectorStore
from ._embeddings import Embeddings
import pyarrow as pa
import lancedb as lancedb
import pyarrow as pa
VECTOR_COLUMN_NAME = "_vector"
class LanceDB(VectorStore):
def __init__(self, db_uri, embeddings: Embeddings = None) -> None:
super().__init__()
try:
pa.__version__
except ImportError as exc:
raise ImportError(
"Could not import pyarrow python package. "
"Please install it with `pip install pyarrow`."
) from exc
try:
# disable diagnostics
lancedb.utils.CONFIG['diagnostics'] = False
except ImportError as exc:
raise ImportError(
"Could not import lancedb python package. "
"Please install it with `pip install lancedb`."
) from exc
self._db_uri = db_uri
self._embeddings = embeddings
self._db = lancedb.connect(self._db_uri)
self._tables = {}
def create_collection(self, collection: str, schema: Dict[str, type] = None, mode="create"):
if schema is None:
raise ValueError("Invalid schema to create LanceDB table.")
s = self._convert_schema(schema=schema)
self._db.create_table(collection, schema=s, mode=mode)
def add(
self,
collection: str,
| records: List[Record], |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: OSU-NLP-Group/TableLlama
# Path: llama_attn_replace.py
def replace_llama_attn(use_flash_attn=True, use_full=False):
if use_flash_attn:
cuda_major, cuda_minor = torch.cuda.get_device_capability()
if cuda_major < 8:
warnings.warn(
"Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward."
"ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593"
)
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
_prepare_decoder_attention_mask
)
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_flashattn_full if use_full else forward_flashattn
else:
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_noflashattn
# Path: supervised_fine_tune.py
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input_seg}\n\n### Question:\n{question}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
# Path: inference_row_pop.py
import os
import json
import sys
import math
import torch
import argparse
import transformers
from peft import PeftModel
from transformers import GenerationConfig
from llama_attn_replace import replace_llama_attn
from supervised_fine_tune import PROMPT_DICT
from tqdm import tqdm
# import textwrap
# from queue import Queue
# from threading import Thread
# import gradio as gr
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--base_model', type=str, default="/data1/pretrained-models/llama-7b-hf")
parser.add_argument('--cache_dir', type=str, default="./cache")
parser.add_argument('--context_size', type=int, default=-1, help='context size during fine-tuning')
parser.add_argument('--flash_attn', type=bool, default=False, help='')
parser.add_argument('--temperature', type=float, default=0.6, help='')
parser.add_argument('--top_p', type=float, default=0.9, help='')
parser.add_argument('--max_gen_len', type=int, default=512, help='')
parser.add_argument('--input_data_file', type=str, default='input_data/', help='')
parser.add_argument('--output_data_file', type=str, default='output_data/', help='')
args = parser.parse_args()
return args
def generate_prompt(instruction, question, input_seg=None):
if input:
| return PROMPT_DICT["prompt_input"].format(instruction=instruction, input_seg=input_seg, question=question) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: pytorch-labs/torchfix
# Path: torchfix/torchfix.py
class TorchChecker:
name = "TorchFix"
version = __version__
# The parameters need to have these exact names.
# See https://flake8.pycqa.org/en/latest/plugin-development/plugin-parameters.html
# `tree` is unused, but the plugin doesn't work without it.
def __init__(self, tree, lines):
# Filter out files that don't have "torch" string in them.
# This avoids expensive parsing.
MARKER = "torch" # this will catch import torch or functorch
has_marker = False
self.module = None
for line in lines:
if MARKER in line:
has_marker = True
break
if has_marker:
module = cst.parse_module("".join(lines))
self.module = cst.MetadataWrapper(module, unsafe_skip_copy=True)
self.violations = []
self.visitors = GET_ALL_VISITORS()
def run(self):
if self.module:
self.module.visit_batched(self.visitors)
for v in self.visitors:
self.violations += v.violations
for violation in self.violations:
yield violation.flake8_result()
@staticmethod
def add_options(optmanager):
optmanager.extend_default_ignore(DISABLED_BY_DEFAULT)
# Path: torchfix/torchfix.py
class TorchCodemod(codemod.Codemod):
def __init__(
self,
context: codemod.CodemodContext,
config: Optional[TorchCodemodConfig] = None,
) -> None:
super().__init__(context)
self.config = config
def transform_module_impl(self, module: cst.Module) -> cst.Module:
# We use `unsafe_skip_copy`` here not only to save some time, but
# because `deep_replace`` is identity-based and will not work on
# the original module if the wrapper does a deep copy:
# in that case we would need to use `wrapped_module.module`
# instead of `module`.
wrapped_module = cst.MetadataWrapper(module, unsafe_skip_copy=True)
visitors = GET_ALL_VISITORS()
violations = []
needed_imports = []
wrapped_module.visit_batched(visitors)
for v in visitors:
violations += v.violations
needed_imports += v.needed_imports
fixes_count = 0
replacement_map = {}
assert self.context.filename is not None
for violation in violations:
skip_violation = False
if self.config is None or self.config.select != "ALL":
for disabled_code in DISABLED_BY_DEFAULT:
if violation.error_code.startswith(disabled_code):
skip_violation = True
break
if skip_violation:
continue
if violation.replacement is not None:
replacement_map[id(violation.node)] = violation.replacement
fixes_count += 1
try:
path = Path(self.context.filename).relative_to(Path.cwd())
except ValueError:
# Not a subpath of a current dir, use absolute path
path = Path(self.context.filename)
print(f"{path}{violation.codemod_result()}")
new_module = deep_multi_replace(module, replacement_map)
add_imports_visitor = codemod.visitors.AddImportsVisitor(
self.context, needed_imports
)
new_module = new_module.visit(add_imports_visitor)
update_functorch_imports_visitor = _UpdateFunctorchImports()
new_module = new_module.visit(update_functorch_imports_visitor)
if fixes_count == 0 and not update_functorch_imports_visitor.changed:
raise codemod.SkipFile("No changes")
return new_module
# Path: torchfix/torchfix.py
class TorchCodemodConfig:
select: Optional[str] = None
# Path: torchfix/torchfix.py
def GET_ALL_VISITORS():
return [
TorchDeprecatedSymbolsVisitor(DEPRECATED_CONFIG_PATH),
TorchRequireGradVisitor(),
TorchSynchronizedDataLoaderVisitor(),
TorchVisionDeprecatedPretrainedVisitor(),
TorchVisionDeprecatedToTensorVisitor(),
TorchUnsafeLoadVisitor(),
TorchReentrantCheckpointVisitor(),
]
# Path: tests/test_torchfix.py
from pathlib import Path
from torchfix.torchfix import (
TorchChecker,
TorchCodemod,
TorchCodemodConfig,
GET_ALL_VISITORS,
)
import logging
import libcst.codemod as codemod
FIXTURES_PATH = Path(__file__).absolute().parent / "fixtures"
LOGGER = logging.getLogger(__name__)
def _checker_results(s):
checker = TorchChecker(None, s)
return [f"{line}:{col} {msg}" for line, col, msg, _ in checker.run()]
def _codemod_results(source_path):
with open(source_path) as source:
code = source.read()
| config = TorchCodemodConfig(select="ALL") |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: FISHers6/CodeLearn-Agent
# Path: codelearn/project/project.py
class Project:
def __init__(self, id: str, local_dir: str, source_content: FileTree, repo_url: str = None, last_updated_time = None):
"""
:param name: 项目名称
:param contents: 一个字典，其中键是文件路径，值是文件内容
"""
self.id = id
self.local_dir = local_dir
self.repo_url = repo_url
self.contents = source_content
self.last_updated_time = last_updated_time
# Path: codelearn/utils/file_util.py
def process_file_paths(file_paths: str) -> List[str]:
# 用于存储处理后的路径
processed_paths = []
# 使用正则表达式来分割字符串，处理多种可能的分隔符
paths = re.split(r'[ ,;]+', file_paths)
for path in paths:
# 删除路径两侧可能存在的多余空格
path = path.strip()
if not path:
continue # 跳过空字符串
# 将路径分割为组件
path_components = re.split(r'[\\/]', path)
# 使用 os.path.join 和 os.sep 来连接路径组件
normalized_path = os.path.join(*path_components)
# 将处理后的路径添加到结果列表中
processed_paths.append(normalized_path)
# 移除重复的路径
processed_paths = list(set(processed_paths))
return processed_paths
# Path: codelearn/tools/file_content_view.py
import json
from typing import List, Optional
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.tools import BaseTool
from codelearn.project.project import Project
from codelearn.utils.file_util import process_file_paths
class FileContentViewTool(BaseTool):
"""Tool to fetch and display detailed content of project files."""
name: str = "get_file_content"
description: str = (
"The 'get_file_content' tool fetches and displays detailed content of specified files within the project, including both source code and documentation. It's an important tool for users who need detailed from code source."
"Input a comma-separated list of file names (without folder or path names) to view. Incomplete paths are not accepted. For example swim-main/src/example.txt is a full path file, but 'src/example' is incomplete directory folder not allowed"
"Output is a dictionary with 'files' key containing a list of dictionaries for each file, "
"**Ensure you've requested the repository structure before asking for file contents.The requested file must exist in the project**"
"Useful for users diving deep into a project's codebase or documentation to understand its intricacies."
)
| project: Project |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: kaixinol/twitter_user_tweet_crawler
# Path: twitter_user_tweet_crawler/browser.py
def get_browser(headless: bool = False) -> WebDriver:
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--blink-settings=imagesEnabled=false')
chrome_options.add_argument('--disable-remote-fonts')
chrome_options.add_argument('--disable-gpu')
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--window-size=1200x600"')
if headless:
chrome_options.add_argument('--headless')
driver = webdriver.Chrome(options=chrome_options)
driver.__dict__['is_using'] = False
return driver
# Path: twitter_user_tweet_crawler/browser.py
def get_multiple_browsers(count: int, headless: bool = False) -> list[WebDriver]:
return [get_browser(headless) for _ in range(count)]
# Path: twitter_user_tweet_crawler/pool.py
class ThreadPool:
browser: list[WebDriver]
jobs: list[Callable] = []
pool: ThreadPoolExecutor
def __init__(self, browser: list[WebDriver], pool: ThreadPoolExecutor):
self.browser = browser
self.pool = pool
def check_and_work(self):
if not self.jobs:
return
for i in self.browser:
if not i.__dict__['is_using']:
i: WebDriver
i.__dict__['is_using'] = True
job = self.jobs.pop(0)
callback: Future = self.pool.submit(job, i)
callback.add_done_callback(lambda future: self._on_job_complete(i, callback))
return
def _on_job_complete(self, index, future):
elements = self.browser.index(index)
try:
future.result()
# By default, `concurrent.futures` will silently log errors but will not raise them
# Throw the error directly
finally:
if slow_mode:
sleep(30)
self.browser[elements].__dict__['is_using'] = False
self.check_and_work()
# Path: twitter_user_tweet_crawler/util/config.py
class Config:
def load(self, setting: dict | str | Path):
def save(self):
def __getitem__(self, item):
def set_work_directory(path: Path):
# Path: twitter_user_tweet_crawler/__main__.py
import concurrent.futures
import json
from pathlib import Path
from time import sleep
from urllib.parse import urlparse
from loguru import logger
from rich.prompt import Confirm
from selenium.webdriver.chrome.webdriver import WebDriver
from selenium.webdriver.common.by import By
from .browser import get_browser, get_multiple_browsers
from .pool import ThreadPool
from .util.config import config, work_directory, set_work_directory
from .tweet import Tweet
def main():
cookie: list[dict]
work_list: list[WebDriver]
driver: WebDriver
def read_config() -> list[dict]:
with open(work_directory / 'cookie.json', 'r') as f:
return json.load(f)
def write_config(data: list[dict]):
with open(work_directory / 'cookie.json', 'w') as f:
json.dump(data, f)
def set_cookie(browser: WebDriver):
for i in cookie:
browser.add_cookie(i)
def get_executor(count: int | None = None):
return concurrent.futures.ThreadPoolExecutor(max_workers=count)
def get_items_need_handle():
return driver.find_elements(*selector)
selector = (By.XPATH, '//*/div[2]/div/div[3]/a[@role="link"]')
| (Path(config.save) / 'res').mkdir(exist_ok=True, parents=True) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: kirill-vish/Beyond-INet
# Path: utils/misc.py
def load_model_transform(model_name, pretrained_dir, img_size=224):
print(f"Loading {model_name}")
checkpoint_path = None
transform_val = None
if model_name == "deit3_21k":
model = models_deit.deit_base_patch16_LS(img_size=img_size)
checkpoint_path = os.path.join(pretrained_dir,
"deit_3_base_224_21k.pth")
elif model_name == "convnext_base_21k":
model = models_convnextv1.convnext_base()
checkpoint_path = os.path.join(pretrained_dir,
"convnext_base_22k_1k_224.pth")
elif model_name == "vit_clip":
model, _, transform_val = open_clip.create_model_and_transforms(
'ViT-B-16', pretrained='laion400m_e31', force_image_size=img_size)
model = models_clip.CLIPModel(model=model, model_name='ViT-B-16')
checkpoint_path = None
elif model_name == "convnext_clip":
model, _, transform_val = open_clip.create_model_and_transforms(
'convnext_base',
pretrained='laion400m_s13b_b51k',
force_image_size=img_size)
model = models_clip.CLIPModel(model=model, model_name='convnext_base')
checkpoint_path = None
if checkpoint_path is not None:
checkpoint = torch.load(checkpoint_path)
state_dict = checkpoint['model']
if img_size != 224 and model_name == 'deit3_21k':
state_dict = interpolate_pos_embed(model, state_dict)
msg = model.load_state_dict(state_dict, strict=False)
print(msg)
assert set(checkpoint['model'].keys()) == set(
model.state_dict().keys())
assert len(msg.missing_keys) == 0 and len(
msg.unexpected_keys
) == 0, "Some keys in the state dict do not match"
return model, transform_val
# Path: inference/modelvshuman/evaluation/evaluate.py
IMAGENET_LABEL_FILE = pjoin(c.CODE_DIR, "evaluation", "imagenet_labels.txt")
def print_performance_to_csv(model_name,
dataset_name,
performance,
metric_name,
data_parent_dir=c.PERFORMANCES_DIR):
def print_predictions_to_console(softmax_output,
top_n=5,
labels_path=IMAGENET_LABEL_FILE):
def __init__(self, model_name, dataset, data_parent_dir=c.RAW_DATA_DIR):
def create_session_csv(self, session):
def print_batch_to_csv(self, object_response, batch_targets, paths):
class ResultPrinter():
# Path: inference/modelvshuman/utils.py
def load_dataset(name, *args, **kwargs):
default_kwargs = {"batch_size": 16, "num_workers": 4}
kwargs = {**default_kwargs, **kwargs}
logger.info(f"Loading dataset {name}")
supported_datasets = dataset_module.list_datasets()
module_name = supported_datasets.get(name, None)
if module_name is None:
raise NameError(
f"Dataset {name} is not supported, "
f"please select from {list(supported_datasets.keys())}")
elif os.path.exists(join(c.DATASET_DIR, name)):
return eval(f"dataset_module.{module_name}")(*args, **kwargs)
elif try_download_dataset_from_github(name):
return eval(f"dataset_module.{module_name}")(*args, **kwargs)
else:
raise NotImplementedError(
f"Dataset {name} not available for download, please obtain the dataset "
f"yourself and save it to {c.DATASET_DIR}")
# Path: inference/modelvshuman/utils.py
def load_model(model_name, *args):
if model_name in zoomodels.__dict__:
model = eval("pytorch_model_zoo.model_pytorch")(model_name, *args)
framework = 'pytorch'
else:
model = eval(f"pytorch_model_zoo.model_timm")(model_name, *args)
framework = 'pytorch'
return model, framework
# Path: inference/modelvshuman/model_evaluator.py
import copy
import datetime
import logging
import os
import matplotlib as mpl
import torch
from torch.nn.functional import softmax
from tqdm import tqdm
from utils.misc import load_model_transform
from .evaluation import evaluate as e
from .utils import load_dataset, load_model
logger = logging.getLogger(__name__)
MAX_NUM_MODELS_IN_CACHE = 3
mpl.rcParams['font.size'] = 22
def device():
return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class ModelEvaluator:
def _pytorch_evaluator(self, model_name, model, dataset, *args, **kwargs):
"""
Evaluate Model on the given dataset and return the accuracy.
Args:
model_name:
model:
dataset:
*args:
**kwargs:
"""
logging_info = f"Evaluating model {model_name} on dataset {dataset.name} using Pytorch Evaluator"
logger.info(logging_info)
print(logging_info)
for metric in dataset.metrics:
metric.reset()
with torch.no_grad():
result_writer = e.ResultPrinter(model_name=model_name,
dataset=dataset)
for images, target, paths in tqdm(dataset.loader):
images = images.to(device())
if "forward_batch" in dir(model):
logits = model.forward_batch(images)
softmax_output = model.softmax(logits)
else:
logits = model(images)
softmax_output = softmax(logits,
dim=1).detach().cpu().numpy()
if isinstance(target, torch.Tensor):
batch_targets = model.to_numpy(target)
else:
batch_targets = target
predictions = dataset.decision_mapping(softmax_output)
for metric in dataset.metrics:
metric.update(predictions, batch_targets, paths)
if kwargs["print_predictions"]:
result_writer.print_batch_to_csv(
object_response=predictions,
batch_targets=batch_targets,
paths=paths)
def _get_datasets(self, dataset_names, *args, **kwargs):
dataset_list = []
for dataset in dataset_names:
| dataset = load_dataset(dataset, *args, **kwargs) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: shengliu66/ICV
# Path: utils/forward_tracer.py
class ForwardTracer:
def __init__(self, model: PreTrainedModel, forward_trace: ForwardTrace, with_submodules: bool = False):
self._model = model
self._forward_trace = forward_trace
self._with_submodules = with_submodules
self._layers = get_layers(model)
self._attn_layers = get_attention_layers(model)
self._mlp_layers = get_mlp_layers(model)
self._hooks = []
def __enter__(self):
self._register_forward_hooks()
def __exit__(self, exc_type, exc_value, traceback):
for hook in self._hooks:
hook.remove()
if exc_type is None:
residual_stream = self._forward_trace.residual_stream
if residual_stream.hidden[0] == []:
residual_stream.hidden.pop(0)
for key in residual_stream.__dataclass_fields__.keys():
acts = getattr(residual_stream, key)
# TODO: this is a hack, fix it
if key != "hidden" and not self._with_submodules:
continue
nonempty_layer_acts = [layer_acts for layer_acts in acts if layer_acts != []][0]
final_shape = torch.cat(nonempty_layer_acts, dim=0).shape
for i, layer_acts in enumerate(acts):
if layer_acts == []:
acts[i] = torch.zeros(final_shape)
else:
acts[i] = torch.cat(layer_acts, dim=0)
acts = torch.stack(acts).transpose(0, 1)
setattr(residual_stream, key, acts)
# if self._with_submodules:
# self._forward_trace.attentions = torch.stack(self._forward_trace.attentions).transpose(0, 1)
# else:
self._forward_trace.attentions = None
def _register_forward_hooks(self):
model = self._model
hooks = self._hooks
residual_stream = self._forward_trace.residual_stream
def store_activations(residual_stream: ResidualStream, acts_type: str, layer_num: int):
def hook(model, inp, out):
if isinstance(out, tuple):
out = out[0]
out = out.float().to("cpu", non_blocking=True)
acts = getattr(residual_stream, acts_type)
while len(acts) < layer_num + 1:
acts.append([])
try:
acts[layer_num].append(out)
except IndexError:
print(len(acts), layer_num)
return hook
def store_attentions(layer_num):
def hook(model, inp, out):
attention_maps = out[1]
attention_maps = attention_maps.to("cpu", non_blocking=True).float()
self._forward_trace.attentions[layer_num] = attention_maps
return hook
embedding_hook = get_embedding_layer(self._model).register_forward_hook(
store_activations(residual_stream, "hidden", 0)
)
hooks.append(embedding_hook)
for i, layer in enumerate(self._layers):
hidden_states_hook = layer.register_forward_hook(store_activations(residual_stream, "hidden", i + 1))
hooks.append(hidden_states_hook)
if self._with_submodules:
for i, mlp_layer in enumerate(self._mlp_layers):
mlp_res_hook = mlp_layer.register_forward_hook(store_activations(residual_stream, "mlp", i))
hooks.append(mlp_res_hook)
for i, attn_layer in enumerate(self._attn_layers):
attn_res_hook = attn_layer.register_forward_hook(store_activations(residual_stream, "attn", i))
hooks.append(attn_res_hook)
# attn_attentions_hook = attn_layer.register_forward_hook(store_attentions(i))
# hooks.append(attn_attentions_hook)
# Path: utils/forward_tracer.py
class ForwardTrace:
def __init__(self):
self.residual_stream: Optional[ResidualStream] = ResidualStream(
hidden=[],
attn=[],
mlp=[],
)
self.attentions: Optional[torch.Tensor] = None
# Path: utils/context_manager.py
import os
from contextlib import AbstractContextManager, ExitStack
from typing import Iterable
from utils.forward_tracer import ForwardTracer, ForwardTrace
class CombinedContextManager(AbstractContextManager):
def __init__(self, context_managers):
self.context_managers = context_managers
self.stack = None
def __enter__(self):
self.stack = ExitStack()
for cm in self.context_managers:
self.stack.enter_context(cm)
return self.stack
def __exit__(self, exc_type, exc_val, exc_tb):
if self.stack is not None:
self.stack.__exit__(exc_type, exc_val, exc_tb)
def modified_forward_context_manager(model, forward_modifiers=()):
context_manager = CombinedContextManager([*forward_modifiers])
return context_manager
def traced_forward_context_manager(model, with_submodules=False):
| forward_trace = ForwardTrace() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Mohamad-Hussein/speech-assistant
# Path: src/funcs.py
def find_gpu_config(logger):
"""
Finds the GPU config and returns the device, device name and torch_dtype
based on GPU platform and availability.
Args:
logger (logging.Logger): Logger instance to log messages onto model.log (for Windows)
Returns:
device (str): Device type, either cuda:0, cpu, or ...
device_name (str): Device name
torch_dtype (torch.dtype): Data type for torch, float16 for GPU, float32 for CPU
"""
import torch
from torch import cuda
from torch import float16, float32
logger.debug("Checking for GPU config")
# Assume, then check
device = torch.device("cuda:0" if cuda.is_available() else "cpu")
torch_dtype = float16 if cuda.is_available() else float32
device_name = ""
# CUDA
if cuda.is_available():
# Debugging made easier
device_name = cuda.get_device_name()
logger.debug("GPU detected from cuda")
logger.info(f"Device: {device}")
logger.info(f"Device name: {cuda.get_device_name()}")
logger.info(f"Device properties: {cuda.get_device_properties(device)}")
logger.info(f"Device count: {cuda.device_count()}")
logger.info(f"Device capability: {cuda.get_device_capability()}")
logger.info(f"Current memory allocated: {cuda.mem_get_info()}")
# AMD
else:
try:
import torch_directml as dml
if dml.is_available():
torch_dtype = float16
device = dml.device()
device_name = dml.device_name(dml.default_device())
logger.debug("GPU detected from torch_directml")
logger.info(f"Available: {dml.is_available()}")
logger.info(f"Devices Available: {dml.device_count()}")
logger.info(f"Device: {device}")
logger.info(f"Default device: {dml.default_device()}")
logger.info(f"Device name: {dml.device_name(0)}")
logger.info(f"GPU memory: {dml.gpu_memory()}")
else:
torch_dtype = float32
logger.debug("No GPU detected, using cpu")
logger.warning(
"Attention, using the CPU is not recommended! Computation time will be long."
)
# Use CPU if directml is not installed
except Exception:
logger.debug(f"Package directml not found")
torch_dtype = float32
logger.debug("No GPU detected, using cpu")
logger.warning(
"Attention, using the CPU is not recommended! Computation time will be long."
)
logger.info(
f"GPU config -- device: {device}, device name: {device_name}, torch_dtype: {torch_dtype}"
)
return device, device_name, torch_dtype
# Path: src/funcs.py
def process_text(text: str):
"""
Processes the text to not type dictation
in which the user has not said anything
Args:
text (str): The text to be processed
Returns:
text (str): The processed text
"""
processed = text
if text.strip().lower() in "you're not.":
processed = ""
return processed
# Path: src/funcs.py
def type_writing(text):
"""
Types the text onto the screen.
Downside is that it is slow and activates
other hotkeys if you hold windows
due to it being real keystrokes.
Args:
text (str): The text to be typed
Returns:
None
"""
typewrite(text)
# Path: src/funcs.py
def copy_writing(text):
"""
Copies the text to the clipboard and writes it.
Args:
text (str): The text to be copied and written
Returns:
None
"""
copy(text)
hotkey("ctrl", "v")
# Path: src/model_inference.py
from sys import exit
from os.path import join
from time import sleep, time
from src.funcs import find_gpu_config, process_text
from src.funcs import type_writing, copy_writing
from transformers.pipelines import pipeline
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from optimum.bettertransformer import BetterTransformer
import logging
# from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
# from optimum.nvidia.pipelines import pipeline
# MODEL_ID = "openai/whisper-tiny.en" # ~400 MiB of GPU memory
MODEL_ID = "distil-whisper/distil-small.en" # ~500-700 MiB of GPU memory
# MODEL_ID = "distil-whisper/distil-medium.en" # ~900-1500 MiB of GPU memory
# MODEL_ID = "distil-whisper/distil-large-v2" # ~1700-2000 MiB of GPU memory
# MODEL_ID = "openai/whisper-large-v3" # ~4000 MiB of GPU memory
# MODEL_ID = "optimum/whisper-tiny.en" # ~400 MiB of GPU memory
# Choosing which way to write text.
WRITE = type_writing
def service(queue, event):
# Configure the logging settings
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(levelname)s - %(message)s",
filename=join("logs", "model.log"),
filemode="w",
)
logger = logging.getLogger(__name__)
# Checking for GPU
| device, device_name, torch_dtype = find_gpu_config(logger) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Fraunhofer-SCAI/corr_shap
# Path: corr_shap/sampling/SamplingStrategy.py
class SamplingStrategy:
def __init__(self, explainer, **kwargs):
""" Construct all necessary attributes for the SamplingStrategy object."""
self.data = explainer.data.data
self.data_weights = explainer.data.weights
self.data_weight_sum = np.sum(self.data_weights)
self.N = explainer.N # num samples in self.data
def sample(self, m):
"""
Return prepared sample data.
These data have fixed features for those contained in subset (m=1) and normalized weights.
:param m: given mask of subset
:return: samples with fixed masked features and normalized weights
"""
x = self.x
samples = self.data.copy()
samples = self.set_masked_features_to_instance(m, x, samples)
weights = self.normalize(self.data_weights)
return samples, weights
def normalize(self, weights):
""" Normalize weights by their sum"""
if self.data_weight_sum != 0:
weights = weights/self.data_weight_sum
return weights
def set_masked_features_to_instance(self, m, x, samples):
"""
Set masked features for subset to given instance.
:param m: given mask of subset
:param x: given instance to be explained
:param samples: background data that are the basis for the sample
:return: samples with fixed masked features
"""
if isinstance(self.varyingFeatureGroups, (list,)):
for j in range(self.varyingFeatureGroups.shape[0]):
for k in self.varyingFeatureGroups[j]:
if m[j] == 1.0:
samples[:, k] = x[0, k]
else:
# for non-jagged numpy array we can significantly boost performance
mask = m == 1.0
groups = self.varyingFeatureGroups[mask]
if len(groups.shape) == 2:
for group in groups:
samples[:, group] = x[0, group]
else:
# further performance optimization in case each group has a single feature
evaluation_data = x[0, groups]
samples[:, groups] = evaluation_data
return samples
def set_instance(self, instance):
""" Set instance to x. """
self.x = instance.x.copy()
def set_varying_feature_groups(self, varying_groups):
""" Set indicies of varying feature groups."""
self.varyingFeatureGroups = varying_groups
# Path: corr_shap/sampling/sampling_factory.py
def get_sampling_strategy(type, explainer, kwargs):
"""Assign the sampling strategy method to the explainer based on the given type. """
sampling_strategies = {"default": SamplingStrategy, "gauss": GaussStrategy, "copula": CopulaStrategy,
"empirical": EmpiricalStrategy, "gauss+empirical": GaussEmpiricalStrategy,
"copula+empirical": CopulaEmpiricalStrategy}
return sampling_strategies[type](explainer=explainer, **kwargs)
# Path: corr_shap/CorrExplainer.py
from scipy.special import binom
from scipy import sparse
from shap.utils._legacy import convert_to_instance, match_instance_to_data, IdentityLink
from shap.explainers._explainer import Explainer
from shap.explainers._kernel import KernelExplainer
from shap.explainers._kernel import Kernel as KernelExplainer
from corr_shap.sampling.SamplingStrategy import SamplingStrategy
from corr_shap.sampling.sampling_factory import get_sampling_strategy
import numpy as np
import pandas as pd
import logging
import copy
import itertools
import typing
import warnings
try:
except ImportError:
log = logging.getLogger('corr_shap')
class CorrExplainer(KernelExplainer):
"""Uses the modified Kernel SHAP method to explain the output of any function.
The modifications (based on the paper 'Explaining individual predictions when features are dependent:
More accurate approximations to Shapley values' by Kjersti Aas, Martin Jullum and Anders Løland)
offer the possibility to include dependencies between features.
There are 3 different approaches, which are described in the following sampling strategies.
"""
| def __init__(self, model, data, link=IdentityLink(), sampling: typing.Union[str, SamplingStrategy]="default", sampling_kwargs={}, **kwargs): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: codereport/jello
# Path: grid.py
class Grid:
def __init__(self, n):
self.n = n * 2
self.grid = [[" "] * self.n, [" "] * self.n]
def add_level(self):
self.grid.append([" "] * self.n)
self.grid.append([" "] * self.n)
def add_subtree(self, level, start, end, s):
if s in ["W", "m", "d"]:
self.grid[level * 2 ][start] = VERT
self.grid[level * 2 + 1][start] = s
return
if (level + 1) * 2 > len(self.grid):
self.add_level()
mid = (start + end) // 2
self.grid[level * 2][start ] = START
self.grid[level * 2][end ] = END
self.grid[level * 2][start + 1:end ] = list(HORIZ * (end - start -1 ))
self.grid[level * 2][(start + end) // 2] = MID
self.grid[level * 2 + 1][mid - len(s) // 2:mid - len(s) // 2 + len(s)] = list(s)
def fill_in_vertical_bars(self):
for column in range(0, self.n):
found_start_end = False
for row in reversed(range(len(self.grid))):
c = self.grid[row][column]
if c in [START, END]:
found_start_end = True
elif found_start_end:
if c == " ":
self.grid[row][column] = "⋮" # │ alternative
else:
found_start_end = False
# combinator chain sequence
def ccs(self):
first_two = "".join("".join(row).strip()[0:2] for row in self.grid)
no_bars = "".join(c for c in first_two if c not in "─└ ⋮┬│")
while "h₁" in no_bars:
no_bars = no_bars.replace("h₁", "")
return no_bars
def display(self, indent = 0):
for row in self.grid:
print(" " * indent + "".join(row))
# Path: utils.py
class Chain(Enum):
MONADIC = 1
DYADIC = 2
# Path: utils.py
class Quick(Enum):
QUICK = 3
EACH = 10
FLIP = 50
# Path: utils.py
class Separator(Enum):
MONADIC = 20
DYADIC = 21
# Path: jello.py
import subprocess
import algorithm
import arity_notation
import draw
import tokens
import utils
from colorama import Fore, init
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.history import FileHistory
from prompt_toolkit.shortcuts import CompleteStyle
from grid import Grid
from utils import Chain, Quick, Separator
#!/usr/bin/env python3
def clear_screen():
subprocess.call("clear", shell=True)
def run_jelly(expr: str, args: list[str]):
try:
command = ["jelly", "eun", expr, *args]
result = subprocess.run(command, text=True, capture_output=True, check=True)
output_text = result.stdout.strip()
draw.cprint(output_text, Fore.GREEN, True)
except subprocess.CalledProcessError as e:
# Print the stderr output for more information about the error
print(Fore.RED + f"Error: {e}")
print(Fore.RED + "stderr:", e.stderr)
completer = WordCompleter(
[k for k in sorted(
list(tokens.niladic.keys()) +
list(tokens.monadic.keys()) +
list(tokens.dyadic.keys()) +
list(tokens.quick.keys()) +
list(tokens.separators.keys())) if len(k) > 1])
history = FileHistory("jello_history.txt")
def is_nilad_array(s: str) -> bool:
return set(list(s)).issubset(list("0123456789,[]"))
def to_jelly(token: str) -> str:
if token in tokens.monadic: return tokens.monadic[token]
if token in tokens.dyadic: return tokens.dyadic[token]
if token in tokens.niladic: return tokens.niladic[token]
if token in tokens.quick: return tokens.quick[token]
if token in tokens.separators: return tokens.separators[token]
if is_nilad_array(token): return token
raise Exception(f"{token} is not a valid Jello keyword.")
def convert(expr: list[str]) -> str:
return "".join([to_jelly(t) for t in expr])
def keyword_arity(k: str) -> int:
if k in tokens.niladic: return 0
if k in tokens.monadic: return 1
if k in tokens.dyadic: return 2
if k == "each": return Quick.EACH
if k == "c": return Quick.FLIP
if k in tokens.quick: return Quick.QUICK
| if k == ".": return Separator.MONADIC |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: mMrBun/Chat2BI
# Path: llms/chatglm3/conversation.py
def preprocess_text(
system: str | None,
tools: list[dict] | None,
history: list[Conversation],
) -> str:
if tools:
tools = json.dumps(tools, indent=4, ensure_ascii=False)
prompt = f"{Role.SYSTEM}\n"
prompt += system if not tools else TOOL_PROMPT
if tools:
tools = json.loads(tools)
prompt += json.dumps(tools, ensure_ascii=False)
for conversation in history:
prompt += f'{conversation}'
prompt += f'{Role.ASSISTANT}\n'
return prompt
# Path: llms/chatglm3/conversation.py
class Conversation:
role: Role
content: str
tool: str | None = None
image: Image | None = None
def __str__(self) -> str:
print(self.role, self.content, self.tool)
match self.role:
case Role.SYSTEM | Role.USER | Role.ASSISTANT | Role.OBSERVATION:
return f'{self.role}\n{self.content}'
case Role.TOOL:
return f'{self.role}{self.tool}\n{self.content}'
case Role.INTERPRETER:
return f'{self.role}interpreter\n{self.content}'
# Human readable format
def get_text(self) -> str:
text = postprocess_text(self.content)
match self.role.value:
case Role.TOOL.value:
text = f'Calling tool `{self.tool}`:\n{text}'
case Role.INTERPRETER.value:
text = f'{text}'
case Role.OBSERVATION.value:
text = f'Observation:\n```\n{text}\n```'
return text
# Path: llms/chatglm3/conversation.py
class Role(Enum):
SYSTEM = auto()
USER = auto()
ASSISTANT = auto()
TOOL = auto()
INTERPRETER = auto()
OBSERVATION = auto()
def __str__(self):
match self:
case Role.SYSTEM:
return "<|system|>"
case Role.USER:
return "<|user|>"
case Role.ASSISTANT | Role.TOOL | Role.INTERPRETER:
return "<|assistant|>"
case Role.OBSERVATION:
return "<|observation|>"
# Path: core/build_tools/utils.py
def extract_code(text: str):
try:
pattern = r'```([^\n]*)\n(.*?)```'
matches = re.findall(pattern, text, re.DOTALL)
return matches[-1][1]
except Exception as e:
return None
# Path: llms/chatglm3/code_interpreter.py
from llms.chatglm3.conversation import preprocess_text, Conversation, Role
from core.build_tools.utils import extract_code
SYSTEM_PROMPT = ('你是一位智能AI助手，你叫ChatGLM，你连接着一台电脑，但请注意不能联网。在使用Python'
'解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件，文件默认存储路径是/mnt/data/。')
MAX_LENGTH = 8192
TRUNCATE_LENGTH = 1024
def is_valid_python(code: str) -> bool:
try:
| code = extract_code(code) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: compphoto/Intrinsic
# Path: intrinsic/ordinal_util.py
def base_resize(img, base_size=384):
"""TODO DESCRIPTION
params:
img (TODO): TODO
base_size (int) optional: TODO (default 384)
returns:
net_input (TODO): TODO
"""
h, w, _ = img.shape
max_dim = max(h, w)
scale = base_size / max_dim
new_h, new_w = scale * h, scale * w
new_h, new_w = round_32(new_h), round_32(new_w)
net_input = resize(img, (new_h, new_w, 3), anti_aliasing=True)
return net_input
# Path: intrinsic/ordinal_util.py
def equalize_predictions(img, base, full, p=0.5):
"""TODO DESCRIPTION
params:
img (TODO): TODO
base (TODO): TODO
full (TODO): TODO
p (int) optional: TODO (default 0.5)
returns:
base (TODO): TODO
new_full (TODO): TODO
"""
h, w, _ = img.shape
full_shd = (1. / full.clip(1e-5)) - 1.
base_shd = (1. / base.clip(1e-5)) - 1.
full_alb = get_brightness(img) / full_shd.clip(1e-5)
base_alb = get_brightness(img) / base_shd.clip(1e-5)
rand_msk = (np.random.randn(h, w) > p).astype(np.uint8)
flat_full_alb = full_alb[rand_msk == 1]
flat_base_alb = base_alb[rand_msk == 1]
scale, _, _, _ = np.linalg.lstsq(flat_full_alb.reshape(-1, 1), flat_base_alb, rcond=None)
new_full_alb = scale * full_alb
new_full_shd = get_brightness(img) / new_full_alb.clip(1e-5)
new_full = 1.0 / (1.0 + new_full_shd)
return base, new_full
# Path: intrinsic/pipeline.py
import torch
import numpy as np
from skimage.transform import resize
from chrislib.resolution_util import optimal_resize
from chrislib.general import round_32, uninvert
from intrinsic.ordinal_util import base_resize, equalize_predictions
def run_pipeline(
models,
img_arr,
output_ordinal=False,
resize_conf=0.0,
base_size=384,
maintain_size=False,
linear=False,
device='cuda',
lstsq_p=0.0,
inputs='all'):
"""Runs the complete pipeline for shading and albedo prediction
params:
models (dict): models dictionary returned by model_util.load_models()
img_arr (np.array): RGB input image as numpy array between 0-1
output_ordinal (bool) optional: whether or not to output intermediate ordinal estimations
(default False)
resize_conf (float) optional: confidence to use for resizing (between 0-1) if None maintain
original size (default None)
base_size (int) optional: size of the base resolution estimation (default 384)
maintain_size (bool) optional: whether or not the results match the input image size
(default False)
linear (bool) optional: whether or not the input image is already linear (default False)
device (str) optional: string representing device to use for pipeline (default "cuda")
lstsq_p (float) optional: subsampling factor for computing least-squares fit
when matching the scale of base and full estimations (default 0.0)
inputs (str) optional: network inputs ("full", "base", "rgb", "all") the rgb image is
always included (default "all")
returns:
results (dict): a result dictionary with albedo, shading and potentiall ordinal estimations
"""
results = {}
orig_h, orig_w, _ = img_arr.shape
# if no confidence value set, just round original size to 32 for model input
if resize_conf is None:
img_arr = resize(img_arr, (round_32(orig_h), round_32(orig_w)), anti_aliasing=True)
# if a the confidence is an int, just rescale image so that the large side
# of the image matches the specified integer value
elif isinstance(resize_conf, int):
scale = resize_conf / max(orig_h, orig_w)
img_arr = resize(
img_arr,
(round_32(orig_h * scale), round_32(orig_w * scale)),
anti_aliasing=True)
# if the confidence is a float use the optimal resize code from Miangoleh et al.
elif isinstance(resize_conf, float):
img_arr = optimal_resize(img_arr, conf=resize_conf)
fh, fw, _ = img_arr.shape
# if the image is in sRGB we do simple linearization using gamma=2.2
if not linear:
lin_img = img_arr ** 2.2
else:
lin_img = img_arr
with torch.no_grad():
# ordinal shading estimation --------------------------
# resize image for base and full estimations and send through ordinal net
base_input = base_resize(lin_img, base_size)
full_input = lin_img
base_input = torch.from_numpy(base_input).permute(2, 0, 1).to(device).float()
full_input = torch.from_numpy(full_input).permute(2, 0, 1).to(device).float()
base_out = models['ordinal_model'](base_input.unsqueeze(0)).squeeze(0)
full_out = models['ordinal_model'](full_input.unsqueeze(0)).squeeze(0)
# the ordinal estimations come out of the model with a channel dim
base_out = base_out.permute(1, 2, 0).cpu().numpy()
full_out = full_out.permute(1, 2, 0).cpu().numpy()
base_out = resize(base_out, (fh, fw))
# if we are using all inputs, we scale the input estimations using the base estimate
if inputs == 'all':
| ord_base, ord_full = equalize_predictions(lin_img, base_out, full_out, p=lstsq_p) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: davep/tinboard
# Path: tinboard/messages/tags.py
class ClearTags(Message):
"""Clear any tags being used to filter."""
# Path: tinboard/messages/tags.py
class ShowAlsoTaggedWith(TagMessage):
"""Message to say bookmarks also of this tag should be shown."""
# Path: tinboard/messages/tags.py
class ShowTaggedWith(TagMessage):
"""Message to say bookmarks of this tag should be shown."""
# Path: tinboard/widgets/extended_option_list.py
class OptionListEx(OptionList):
"""The Textual `OptionList` with more navigation keys."""
BINDINGS = [
Binding("s, j", "cursor_down", show=False),
Binding("w, k", "cursor_up", show=False),
]
def clear_options(self) -> Self:
"""Workaround for https://github.com/Textualize/textual/issues/3714"""
super().clear_options()
self._clear_content_tracking()
return self
# Path: tinboard/widgets/tags.py
from typing_extensions import Final, Self
from textual import on
from textual.binding import Binding
from textual.events import Focus
from textual.reactive import var
from textual.widgets.option_list import Option, OptionDoesNotExist
from rich.console import RenderableType
from rich.emoji import Emoji
from rich.table import Table
from ..messages import ClearTags, ShowAlsoTaggedWith, ShowTaggedWith
from .extended_option_list import OptionListEx
"""Defines a widget for picking tags."""
##############################################################################
# Backward compatibility.
from __future__ import annotations
##############################################################################
# Python imports.
##############################################################################
# Textual imports.
##############################################################################
# Rich imports.
##############################################################################
# Local imports.
##############################################################################
class Tags(OptionListEx):
"""A menu of tags."""
CONTEXT_HELP = """
## Tag list keys
The following keys are available in the list of tags:
| Key | Description |
| - | - |
| <kbd>Enter</kbd> | Show bookmarks with this tag in the bookmark list. |
| <kbd>+</kbd> | Add this tag to any tag filter active in the bookmark list. |
"""
DEFAULT_CSS = """
Tags {
&:focus {
border: blank;
}
&> .option-list--option {
padding: 0 1;
}
}
"""
BINDINGS = [
Binding("enter", "select", "Show tagged", show=True),
Binding("+", "also_tagged", "Show also tagged"),
]
def _prompt(self, tag: str, count: int) -> RenderableType:
"""A prompt for the given tag.
Args:
tag: The tag to build a prompt for.
count: The count for that tag.
Returns:
The prompt for the tag.
"""
prompt = Table.grid(expand=True)
prompt.add_column(ratio=1)
prompt.add_column(justify="right")
prompt.add_row(tag, f"[dim i]{count}[/]")
return prompt
def _sorted(self, tags: list[tuple[str, int]]) -> list[tuple[str, int]]:
"""Sort the tags.
Args:
tags: The tags to sort.
Returns:
The tags in the desired sort order.
"""
return tags
def show(self, tags: list[tuple[str, int]]) -> Self:
"""Show the given list of tags.
Args:
tags: The tags to show in the widget.
Returns:
Self.
"""
self.can_focus = bool(tags)
highlighted_tag = (
self.get_option_at_index(self.highlighted).id
if self.highlighted is not None
else None
)
try:
return self.clear_options().add_options(
[
Option(self._prompt(tag, count), id=tag)
for tag, count in self._sorted(tags)
]
)
finally:
if tags:
try:
self.highlighted = self.get_option_index(highlighted_tag or "")
except OptionDoesNotExist:
self.highlighted = 0
def _on_focus(self, _: Focus) -> None:
"""Highlight the first item on focus, if none highlighted."""
if self.option_count and self.highlighted is None:
self.highlighted = 0
@on(OptionListEx.OptionSelected)
def _show_tagged(self, event: OptionListEx.OptionSelected) -> None:
"""Request that bookmarks of a given tag are shown.
Args:
event: The event to handle.
"""
if event.option.id is not None:
| self.post_message(ShowTaggedWith(event.option.id)) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: buptlihang/CVLM
# Path: model/utils.py
IMAGE_TOKEN_INDEX = -200
# Path: model/utils.py
DEFAULT_IMAGE_TOKEN = "<image>"
# Path: model/utils.py
DEFAULT_IM_START_TOKEN = "<im_start>"
# Path: model/utils.py
DEFAULT_IM_END_TOKEN = "<im_end>"
# Path: model/utils.py
def build_conversation():
conversation = Conversation(
system=
"A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=["USER", "ASSISTANT"],
version="TWO",
messages=[],
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
return conversation
# Path: model/utils.py
def load_pretrained_model(model_path,
load_8bit=False,
load_4bit=False,
device_map="auto",
device="cuda"):
kwargs = {"device_map": device_map}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4')
else:
kwargs['torch_dtype'] = torch.float16
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model, output_loading_info = AutoModelForCausalLM.from_pretrained(
model_path, output_loading_info=True, **kwargs)
model.resize_token_embeddings(len(tokenizer))
image_processor = model.model.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
# Path: model/utils.py
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
# Path: model/utils.py
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1]
# Path: model/utils.py
def tokenizer_image_token(prompt,
tokenizer,
image_token_index=IMAGE_TOKEN_INDEX,
return_tensors=None):
prompt_chunks = [
tokenizer(chunk).input_ids for chunk in prompt.split('<image>')
]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep] * len(X))
for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(
prompt_chunks[0]
) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks,
[image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
# Path: model/utils.py
def process_images(images, image_processor, model_cfg):
new_images = []
for image in images:
image = expand2square(
image, tuple(int(x * 255) for x in image_processor.image_mean))
image = image_processor.preprocess(
image, return_tensors='pt')['pixel_values'][0]
new_images.append(image)
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images
# Path: evaluation/MME/evaluate.py
import argparse
import torch
import os
import json
import math
from tqdm import tqdm
from model.utils import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from model.utils import build_conversation, load_pretrained_model, disable_torch_init, get_model_name_from_path
from model.utils import tokenizer_image_token, process_images
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from collections import defaultdict
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def get_gt(data_path):
GT = {}
for category in os.listdir(data_path):
category_dir = os.path.join(data_path, category)
if not os.path.isdir(category_dir):
continue
if os.path.exists(os.path.join(category_dir, 'images')):
image_path = os.path.join(category_dir, 'images')
qa_path = os.path.join(category_dir, 'questions_answers_YN')
else:
image_path = qa_path = category_dir
assert os.path.isdir(image_path), image_path
assert os.path.isdir(qa_path), qa_path
for file in os.listdir(qa_path):
if not file.endswith('.txt'):
continue
for line in open(os.path.join(qa_path, file)):
question, answer = line.strip().split('\t')
GT[(category, file, question)] = answer
return GT
# Custom dataset class
class CustomDataset(Dataset):
def __init__(self, questions, image_folder, tokenizer, image_processor,
model_config):
self.questions = questions
self.image_folder = image_folder
self.tokenizer = tokenizer
self.image_processor = image_processor
self.model_config = model_config
def __getitem__(self, index):
line = self.questions[index]
image_file = line["image"]
qs = line["text"]
| qs = DEFAULT_IMAGE_TOKEN + '\n' + qs |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: vvvm23/TchAIkovsky
# Path: data/tokenizer.py
def get_pretrained_tokenizer(path: str = "tokenizer.json"):
return miditok.REMI.from_pretrained(path)
# Path: model/model.py
class TchAIkovskyModel(eqx.Module):
id_embeddings: eqx.Module
pos_embeddings: eqx.Module
decoder: eqx.Module
norm_out: eqx.Module
out_head: eqx.Module
dtype: jnp.dtype = eqx.field(static=True)
output_dtype: jnp.dtype = eqx.field(static=True)
def __init__(
self,
dim: int,
num_heads: int,
num_layers: int,
vocab_size: int,
max_positions: int,
head_dim: Optional[int] = None,
dropout: float = 0.0,
key: PRNGKey = None,
dtype: jnp.dtype = jnp.float32,
output_dtype: jnp.dtype = jnp.float32,
):
self.dtype = dtype
self.output_dtype = output_dtype
id_embeddings_key, pos_embeddings_key, decoder_key, out_key = jax.random.split(key, 4)
self.id_embeddings = eqx.nn.Embedding(vocab_size, dim, key=id_embeddings_key)
self.pos_embeddings = eqx.nn.Embedding(max_positions, dim, key=pos_embeddings_key)
self.decoder = Decoder(
decoder_key,
dim,
num_heads,
num_layers,
head_dim=head_dim,
dropout=dropout,
dtype=dtype,
)
self.norm_out = eqx.nn.LayerNorm(dim)
self.out_head = eqx.nn.Linear(dim, vocab_size, use_bias=True, key=out_key)
def __call__(self, input_ids, position_ids, mask, key=None):
causal_mask = make_causal_mask(input_ids)[0]
mask = jnp.where(mask, causal_mask, 0)
x = jax.vmap(self.id_embeddings)(input_ids) + jax.vmap(self.pos_embeddings)(position_ids)
x = self.decoder(x, mask, key)
x = jax.vmap(self.norm_out)(x)
logits = jax.vmap(self.out_head)(x)
logits = logits.astype(self.output_dtype)
return logits
# Path: utils.py
def seed_others(seed):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# Path: generate.py
import json
import equinox as eqx
import jax
import jax.numpy as jnp
import numpy as np
import orbax.checkpoint as ocp
import tqdm
from argparse import ArgumentParser
from datetime import datetime
from pathlib import Path
from types import SimpleNamespace
from typing import Optional
from loguru import logger
from miditoolkit import MidiFile
from data.tokenizer import get_pretrained_tokenizer
from model import TchAIkovskyModel
from utils import seed_others
def load_config(config_path):
with open(config_path, mode="r") as f:
data = f.read()
json_dict = json.loads(data)
return SimpleNamespace(**json_dict)
@eqx.filter_jit
@eqx.debug.assert_max_traces(max_traces=1)
def generate_step(model, inputs, length, key, temperature):
logits = model(**inputs)
logits = jnp.take(logits, length - 1, axis=0)
if temperature == 0.0:
# argmax sampling
return jnp.argmax(logits, axis=-1)
logits = logits / temperature
return jax.random.categorical(key, logits, axis=-1)
def generate_loop(
model,
initial_input,
temperature,
key,
max_to_generate: Optional[int] = None,
model_max_positions: int = 1024,
output_generated_only: bool = False,
) -> np.array:
sample_idx = initial_input.shape[0]
if output_generated_only:
output = []
else:
output = initial_input.tolist()
if max_to_generate is None:
DEFAULT_MAX = 1000
max_to_generate = DEFAULT_MAX
input_length = sample_idx + max_to_generate
if input_length > model_max_positions - 1:
input_length = model_max_positions - 1
position_ids = np.arange(input_length)
mask = np.concatenate(
[
np.ones((sample_idx,), dtype=bool),
np.zeros((input_length - sample_idx,), dtype=bool),
],
axis=-1,
dtype=bool,
)
input_ids = np.pad(initial_input, ((0, input_length - sample_idx),))
# TODO: maybe replace with jax.lax.scan loop for faster generation
for _ in tqdm.trange(max_to_generate):
key, subkey = jax.random.split(key)
inputs = dict(input_ids=input_ids, position_ids=position_ids, mask=mask)
token = generate_step(model, inputs, np.array(sample_idx), subkey, temperature).item()
output.append(token)
if sample_idx < input_length:
input_ids[sample_idx] = token
mask[sample_idx] = True
else:
input_ids = np.concatenate([input_ids[1:], np.array([token])], axis=-1)
sample_idx = min(input_length - 1, sample_idx + 1)
return np.array(output)
# tokenizes initial prompt
def tokenize_prompt(midi, tokenizer):
return tokenizer(midi)
# loads prompt MIDI file
def file_prompt(path):
midi = MidiFile(path)
return midi
def main(args):
logger.info("Beginning generation script.")
key = jax.random.PRNGKey(args.seed)
logger.info(f"Using PRNG key {args.seed}")
seed_others(args.seed)
logger.info("Loading config.")
config = load_config(args.config)
logger.info(f"Loading tokenizer from '{args.tokenizer}'")
| tokenizer = get_pretrained_tokenizer(args.tokenizer) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: dazhangyu123/ACMIL
# Path: architecture/network.py
class Classifier_1fc(nn.Module):
def __init__(self, n_channels, n_classes, droprate=0.0):
super(Classifier_1fc, self).__init__()
self.fc = nn.Linear(n_channels, n_classes)
self.droprate = droprate
if self.droprate != 0.0:
self.dropout = torch.nn.Dropout(p=self.droprate)
def forward(self, x):
if self.droprate != 0.0:
x = self.dropout(x)
x = self.fc(x)
return x
# Path: architecture/network.py
class DimReduction(nn.Module):
def __init__(self, n_channels, m_dim=512, numLayer_Res=0):
super(DimReduction, self).__init__()
self.fc1 = nn.Linear(n_channels, m_dim, bias=False)
self.relu1 = nn.ReLU(inplace=True)
self.numRes = numLayer_Res
self.resBlocks = []
for ii in range(numLayer_Res):
self.resBlocks.append(residual_block(m_dim))
self.resBlocks = nn.Sequential(*self.resBlocks)
def forward(self, x):
x = self.fc1(x)
x = self.relu1(x)
if self.numRes > 0:
x = self.resBlocks(x)
return x
# Path: architecture/ibmil.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from architecture.network import Classifier_1fc, DimReduction
class Attention_Gated(nn.Module):
def __init__(self, L=512, D=128, K=1):
super(Attention_Gated, self).__init__()
self.L = L
self.D = D
self.K = K
self.attention_V = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Tanh()
)
self.attention_U = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Sigmoid()
)
self.attention_weights = nn.Linear(self.D, self.K)
def forward(self, x):
## x: N x L
A_V = self.attention_V(x) # NxD
A_U = self.attention_U(x) # NxD
A = self.attention_weights(A_V * A_U) # NxK
A = torch.transpose(A, 1, 0) # KxN
return A ### K x N
class IBMIL(nn.Module):
def __init__(self, conf, confounder_dim=128, confounder_merge='cat'):
super(IBMIL, self).__init__()
self.confounder_merge = confounder_merge
assert confounder_merge in ['cat', 'add', 'sub']
self.dimreduction = DimReduction(conf.D_feat, conf.D_inner)
self.attention = Attention_Gated(conf.D_inner, 128, 1)
| self.classifier = Classifier_1fc(conf.D_inner, conf.n_class, 0)
|
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Kav-K/Described
# Path: services/environment_service.py
class EnvService:
# To be expanded upon later!
def __init__(self):
self.env = {}
@staticmethod
def environment_path_with_fallback(env_name, relative_fallback=None):
directory = os.getenv(env_name)
if directory is not None:
return Path(directory).resolve()
if relative_fallback:
app_relative = (app_root_path() / relative_fallback).resolve()
if app_relative.exists():
return app_relative
return Path.cwd()
@staticmethod
def save_path():
share_dir = os.getenv("SHARE_DIR")
if share_dir is not None:
return Path(share_dir)
return app_root_path()
@staticmethod
def find_shared_file(file_name):
share_file_paths = []
share_dir = os.getenv("SHARE_DIR")
if share_dir is not None:
share_file_paths.append(Path(share_dir) / file_name)
share_file_paths.extend(
[
app_root_path() / "share" / file_name,
app_root_path() / file_name,
Path(file_name),
]
)
for share_file_path in share_file_paths:
if share_file_path.exists():
return share_file_path.resolve()
raise ValueError(f"Unable to find shared data file {file_name}")
@staticmethod
def get_allowed_guilds():
# ALLOWED_GUILDS is a comma separated list of guild ids
# It can also just be one guild ID
# Read these allowed guilds and return as a list of ints
try:
allowed_guilds = os.getenv("ALLOWED_GUILDS")
except Exception:
allowed_guilds = None
if allowed_guilds is None:
raise ValueError(
"ALLOWED_GUILDS is not defined properly in the environment file!"
"Please copy your server's guild ID and put it into ALLOWED_GUILDS in the .env file."
'For example a line should look like: `ALLOWED_GUILDS="971268468148166697"`'
)
allowed_guilds = (
allowed_guilds.split(",") if "," in allowed_guilds else [allowed_guilds]
)
allowed_guilds = [int(guild) for guild in allowed_guilds]
return allowed_guilds
@staticmethod
def get_described_channels():
# ALLOWED_GUILDS is a comma separated list of guild ids
# It can also just be one guild ID
# Read these allowed guilds and return as a list of ints
try:
described_channels = os.getenv("DESCRIBED_CHANNELS")
except Exception:
described_channels = None
if described_channels is None:
raise ValueError(
"DESCRIBED_CHANNELS is not properly defined in your environment file. All channels will be enabled for image descriptions"
)
described_channels = (
described_channels.split(",")
if "," in described_channels
else [described_channels]
)
return described_channels
@staticmethod
def get_discord_token():
try:
e2b_key = os.getenv("DISCORD_TOKEN")
return e2b_key
except Exception:
return None
@staticmethod
def get_openai_api_key():
try:
openai_key = os.getenv("OPENAI_API_KEY")
return openai_key
except Exception:
return None
@staticmethod
def get_admin_roles():
# ADMIN_ROLES is a comma separated list of string roles
# It can also just be one role
# Read these allowed roles and return as a list of strings
try:
admin_roles = os.getenv("ADMIN_ROLES")
except Exception:
admin_roles = None
if admin_roles is None:
print(
"ADMIN_ROLES is not defined properly in the environment file!"
"Please copy your server's role and put it into ADMIN_ROLES in the .env file."
'For example a line should look like: `ADMIN_ROLES="Admin"`'
)
print("Defaulting to allowing all users to use admin commands...")
return [None]
admin_roles = (
admin_roles.lower().split(",")
if "," in admin_roles
else [admin_roles.lower()]
)
return admin_roles
# Path: services/prompts/image_analysis_prompt.py
IMAGE_ANALYSIS_PROMPT = """
You are an image describer. You will be given one or more images and your goal is to describe all of the details in incredible, verbose detail. Pretend as if you are describing an image for a user that is visually impaired, thinking what information would most be useful for them to understand the image holistically.
Make note to describe and talk about:
- The colors of the image
- The shapes of the objects in the image
- The objects themselves in the image and what they are
- Actions happening in the image
- The scenery and landscape of the image
- The emotions of the people in the image
- The weather of the image
- The time of day of the image
- The set and setting of the image holistically.
- Always perform OCR and extract all the text from the image when possible.
Always respond in third person, talk about the image provided in third person and describe it as if you are describing it to someone who is visually impaired.
Be incredibly, very brief and concise while still conveying all the information possible.
Now, describe an image. They will be given to you:
"""
# Path: services/openai_service.py
import traceback
import aiohttp
import backoff
from services.environment_service import EnvService
from services.prompts.image_analysis_prompt import IMAGE_ANALYSIS_PROMPT
def backoff_handler_request(details):
print(
f"Backing off {details['wait']:0.1f} seconds after {details['tries']} tries calling function {details['target']} | "
f"{details['exception'].args[0]}"
)
class OpenAIExecutor:
def __init__(self):
self.openai_api_key = EnvService.get_openai_api_key()
try:
| self.ANALYSIS_PRETEXT = IMAGE_ANALYSIS_PROMPT |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: juftin/hatch-pip-compile
# Path: hatch_pip_compile/exceptions.py
class HatchPipCompileError(Exception):
"""
Base exception for hatch-pip-compile
"""
# Path: hatch_pip_compile/installer.py
class PluginInstaller(ABC):
"""
Package Installer for the plugin
This abstract base class is used to define the interface for
how the plugin should install packages and dependencies.
"""
environment: "PipCompileEnvironment"
@abstractmethod
def install_dependencies(self) -> None:
"""
Install the dependencies
"""
def sync_dependencies(self) -> None:
"""
Sync the dependencies - same as `install_dependencies`
"""
self.install_dependencies()
def install_project(self) -> None:
"""
Install the project (`--no-deps`)
"""
with self.environment.safe_activation():
self.environment.plugin_check_command(
self.environment.construct_pip_install_command(
args=["--no-deps", str(self.environment.root)]
)
)
def install_project_dev_mode(self) -> None:
"""
Install the project in editable mode (`--no-deps`)
"""
with self.environment.safe_activation():
self.environment.plugin_check_command(
self.environment.construct_pip_install_command(
args=["--no-deps", "--editable", str(self.environment.root)]
)
)
# Path: tests/conftest.py
class PipCompileFixture:
"""
Testing Fixture Data Container
"""
__test__ = False
isolation: pathlib.Path
toml_doc: tomlkit.TOMLDocument
pyproject: pathlib.Path
project: Project
platform: Platform
isolated_data_dir: pathlib.Path
application: Application = field(init=False)
default_environment: PipCompileEnvironment = field(init=False)
test_environment: PipCompileEnvironment = field(init=False)
def __post_init__(self) -> None:
"""
Post Init
"""
self.application = Application(
exit_func=lambda x: None, # noqa: ARG005
verbosity=0,
interactive=False,
enable_color=False,
)
self.application.data_dir = self.isolated_data_dir
self.application.project = self.project
self.default_environment = self.reload_environment("default")
self.test_environment = self.reload_environment("test")
def reload_environment(self, environment: str) -> PipCompileEnvironment:
"""
Reload a new environment given the current state of the isolated project
"""
new_project = Project(self.isolation)
return PipCompileEnvironment(
root=self.isolation,
metadata=new_project.metadata,
name=environment,
config=new_project.config.envs[environment],
matrix_variables={},
data_directory=self.isolated_data_dir,
isolated_data_directory=self.isolated_data_dir,
platform=self.platform,
verbosity=0,
)
def update_pyproject(self) -> None:
"""
Update pyproject.toml
"""
tomlkit.dump(self.toml_doc, self.pyproject.open("w"))
# Path: tests/test_installer.py
from typing import Dict, Type
from unittest.mock import Mock
from hatch_pip_compile.exceptions import HatchPipCompileError
from hatch_pip_compile.installer import PluginInstaller
from tests.conftest import PipCompileFixture
import pytest
"""
Installation Tests
"""
def test_pip_install_dependencies(mock_check_command: Mock, pip_compile: PipCompileFixture) -> None:
"""
Assert the `pip` installation command is called with the expected arguments
"""
pip_compile.default_environment.create()
pip_compile.default_environment.installer.install_dependencies()
expected_call = [
"python",
"-u",
"-m",
"pip",
"install",
"--disable-pip-version-check",
"--no-python-version-warning",
"-q",
"--requirement",
]
call_args = list(mock_check_command.call_args)[0][0][:-1]
assert call_args == expected_call
@pytest.mark.parametrize("installer", ["pip", "pip-sync"])
def test_installer_type(
installer: str, installer_dict: Dict[str, Type[PluginInstaller]], pip_compile: PipCompileFixture
) -> None:
"""
Test the `pip-compile-installer` configuration option
"""
pip_compile.toml_doc["tool"]["hatch"]["envs"]["default"]["pip-compile-installer"] = installer
pip_compile.update_pyproject()
updated_environment = pip_compile.reload_environment("default")
assert isinstance(updated_environment.installer, installer_dict[installer])
def test_installer_unknown(pip_compile: PipCompileFixture) -> None:
"""
Test that an exception is raised when an unknown installer is configured
"""
pip_compile.toml_doc["tool"]["hatch"]["envs"]["default"]["pip-compile-installer"] = "unknown"
pip_compile.update_pyproject()
| with pytest.raises(HatchPipCompileError): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: google-deepmind/pix2act
# Path: pix2act/common/tf_utils.py
def add_bytes_feature(
example: tf.train.Example, key: str, value: bytes
) -> None:
def add_text_feature(example: tf.train.Example, key: str, value: str) -> None:
def get_bytes_feature(example: tf.train.Example, key: str) -> bytes:
def get_text_feature(example: tf.train.Example, key: str) -> str:
def _get_hash(key: str) -> int:
def _increment_counter(item, counter):
def __init__(
self,
output_dir: str,
validation_percent: Optional[int] = 10,
train_file_name: str = "train.tfr",
val_file_name: str = "val.tfr",
):
def _partition_index(
self, example: tf.train.Example, unused_num_partitions: int
) -> int:
def expand(self, pcoll):
class SplitAndWriteTFRecords(beam.PTransform):
# Path: pix2act/tasks/miniwob/search/reward_utils.py
STEP_PENALTY = -1.0 / 30
VALUE_FN_SCALAR = 30
_NORMALIZED_REWARD_THRESHOLD = 0.9
def compute_surrogate_reward(raw_reward, steps_to_go=0):
def surrogate_reward_to_value_fn_target(surrogate_reward):
def value_fn_output_to_surrogate_reward(value_fn_output):
# Path: pix2act/tasks/miniwob/search/write_value_fn_tf_examples.py
from absl import app
from absl import flags
from pix2act.common import tf_utils
from pix2act.tasks.miniwob import episode_pb2
from pix2act.tasks.miniwob.search import reward_utils
import apache_beam as beam
import tensorflow as tf
# Copyright 2023 The pix2act Authors.
#
# 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.
r"""Converts episodes to tf examples for training value function approximator.
"""
FLAGS = flags.FLAGS
flags.DEFINE_list("inputs", "", "Input tfrecord files of Episodes.")
flags.DEFINE_string("output_dir", "", "Output location for tf examples.")
flags.DEFINE_float(
"reward_threshold",
0.8,
"Demonstrations below this threshold will be discarded.",
)
class ConvertEpisode(beam.DoFn):
"""Convert episode to tf examples."""
def process(self, episode):
if not episode.task_name:
beam.metrics.Metrics.counter("ConvertEpisode", "no_task_name").inc()
elif not episode.steps:
beam.metrics.Metrics.counter("no_steps", episode.task_name).inc()
elif episode.raw_reward < FLAGS.reward_threshold:
beam.metrics.Metrics.counter(
"failed_demonstration", episode.task_name
).inc()
else:
beam.metrics.Metrics.counter("num_demos", episode.task_name).inc()
try:
total_steps = len(episode.steps)
for step_idx, step in enumerate(episode.steps):
steps_to_go = total_steps - step_idx
surrogate_reward = reward_utils.compute_surrogate_reward(
episode.raw_reward, steps_to_go
)
value_fn_target = reward_utils.surrogate_reward_to_value_fn_target(
surrogate_reward
)
example = tf.train.Example()
| tf_utils.add_bytes_feature(example, "image", step.screenshot_png) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: zhang-tao-whu/DVIS_Plus
# Path: mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py
def build_transformer_decoder(cfg, in_channels, mask_classification=True):
"""
Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.
"""
name = cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME
return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)
# Path: mask2former/modeling/pixel_decoder/fpn.py
def build_pixel_decoder(cfg, input_shape):
"""
Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
"""
name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME
model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
forward_features = getattr(model, "forward_features", None)
if not callable(forward_features):
raise ValueError(
"Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
f"Please implement forward_features for {name} to only return mask features."
)
return model
# Path: mask2former/modeling/meta_arch/mask_former_head.py
import logging
import fvcore.nn.weight_init as weight_init
from copy import deepcopy
from typing import Callable, Dict, List, Optional, Tuple, Union
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
from ..transformer_decoder.maskformer_transformer_decoder import build_transformer_decoder
from ..pixel_decoder.fpn import build_pixel_decoder
# Copyright (c) Facebook, Inc. and its affiliates.
@SEM_SEG_HEADS_REGISTRY.register()
class MaskFormerHead(nn.Module):
_version = 2
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
version = local_metadata.get("version", None)
if version is None or version < 2:
# Do not warn if train from scratch
scratch = True
logger = logging.getLogger(__name__)
for k in list(state_dict.keys()):
newk = k
# if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
# newk = k.replace(prefix, prefix + "pixel_decoder.")
# # logger.debug(f"{k} ==> {newk}")
if newk != k:
state_dict[newk] = state_dict[k]
del state_dict[k]
scratch = False
if not scratch:
logger.warning(
f"Weight format of {self.__class__.__name__} have changed! "
"Please upgrade your models. Applying automatic conversion now ..."
)
@configurable
def __init__(
self,
input_shape: Dict[str, ShapeSpec],
*,
num_classes: int,
pixel_decoder: nn.Module,
loss_weight: float = 1.0,
ignore_value: int = -1,
return_transformer_feature: bool = False,
# extra parameters
transformer_predictor: nn.Module,
transformer_in_feature: str,
):
"""
NOTE: this interface is experimental.
Args:
input_shape: shapes (channels and stride) of the input features
num_classes: number of classes to predict
pixel_decoder: the pixel decoder module
loss_weight: loss weight
ignore_value: category id to be ignored during training.
transformer_predictor: the transformer decoder that makes prediction
transformer_in_feature: input feature name to the transformer_predictor
"""
super().__init__()
input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
self.in_features = [k for k, v in input_shape]
feature_strides = [v.stride for k, v in input_shape]
feature_channels = [v.channels for k, v in input_shape]
self.ignore_value = ignore_value
self.common_stride = 4
self.loss_weight = loss_weight
self.return_transformer_feature = return_transformer_feature
self.pixel_decoder = pixel_decoder
self.predictor = transformer_predictor
self.transformer_in_feature = transformer_in_feature
self.num_classes = num_classes
@classmethod
def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
# figure out in_channels to transformer predictor
if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder":
transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding":
transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder": # for maskformer2
transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
else:
transformer_predictor_in_channels = input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels
return {
"input_shape": {
k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
},
"ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
"return_transformer_feature": cfg.MODEL.SEM_SEG_HEAD.RETURN_TRANSFORMER_FEATURE,
"num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
"pixel_decoder": build_pixel_decoder(cfg, input_shape),
"loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
"transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE,
| "transformer_predictor": build_transformer_decoder( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: teamreboott/data-modori
# Path: data_modori/utils/constant.py
class Fields(object):
stats = DEFAULT_PREFIX + 'stats__'
meta = DEFAULT_PREFIX + 'meta__'
context = DEFAULT_PREFIX + 'context__'
suffix = DEFAULT_PREFIX + 'suffix__'
# Path: data_modori/utils/constant.py
class StatsKeys(object):
alpha_token_ratio = 'alpha_token_ratio'
korean_token_ratio = 'korean_token_ratio'
alnum_ratio = 'alnum_ratio'
avg_line_length = 'avg_line_length'
char_rep_ratio = 'char_rep_ratio'
flagged_words_ratio = 'flagged_words_ratio'
lang = 'lang'
lang_score = 'lang_score'
max_line_length = 'max_line_length'
perplexity = 'perplexity'
special_char_ratio = 'special_char_ratio'
stopwords_ratio = 'stopwords_ratio'
text_len = 'text_len'
num_token = 'num_token'
num_words = 'num_words'
word_rep_ratio = 'word_rep_ratio'
# Path: data_modori/utils/model_utils.py
def prepare_model(lang='en', model_type='sentencepiece', model_key=None):
"""
Prepare and load a model or a tokenizer from MODEL_ZOO.
:param lang: which lang model to load
:param model_type: model or tokenizer type
:param model_key: tokenizer name, only used when prepare HuggingFace
tokenizer
:return: a model or tokenizer instance
"""
type_to_name = {
'fasttext': ('lid.176.bin', prepare_fasttext_model),
'sentencepiece': ('%s.sp.model', prepare_sentencepiece_model),
'kenlm': ('%s.arpa.bin', prepare_kenlm_model),
'nltk': ('punkt.%s.pickle', prepare_nltk_model),
'huggingface': ('%s', prepare_huggingface_tokenizer),
'spacy': ('%s_core_web_md-3.5.0', prepare_diversity_model),
'spacy_ko': ('%s_core_news_md-3.7.0', prepare_diversity_model),
'konlpy': ('%s', prepare_konlpy_model),
}
assert model_type in type_to_name.keys(
), 'model_type must be one of the following: {}'.format(
list(type_to_name.keys()))
if model_key is None:
model_key = model_type + '_' + lang
if model_key not in MODEL_ZOO.keys():
model_name, model_func = type_to_name[model_type]
if model_type == 'fasttext':
MODEL_ZOO[model_key] = model_func(model_name)
elif model_type == 'huggingface':
MODEL_ZOO[model_key] = model_func(model_key)
elif model_type == 'konlpy':
MODEL_ZOO[model_key] = model_func()
else:
MODEL_ZOO[model_key] = model_func(model_name, lang)
return model_key
# Path: data_modori/utils/model_utils.py
def get_model(model_key, lang='en', model_type='sentencepiece'):
"""
Get a model or a tokenizer from MODEL_ZOO.
:param model_key: name of the model or tokenzier
"""
if model_key not in MODEL_ZOO:
prepare_model(lang=lang, model_type=model_type, model_key=model_key)
return MODEL_ZOO.get(model_key, None)
# Path: data_modori/ops/base_op.py
OPERATORS = Registry('Operators')
# Path: data_modori/ops/base_op.py
class Filter:
def __init__(self, text_key: str = None):
"""
Base class that removes specific info.
:param text_key: the key name of field that stores sample texts
to be processed
"""
if text_key is None:
text_key = 'text'
self.text_key = text_key
from data_modori.core.data import wrap_func_with_nested_access
self.process = wrap_func_with_nested_access(self.process)
self.compute_stats = wrap_func_with_nested_access(self.compute_stats)
def compute_stats(self, sample, context=False):
"""
Compute stats for the sample which is used as a metric to decide
whether to filter this sample.
:param sample: input sample.
:param context: whether to store context information of intermediate
vars in the sample temporarily.
:return: sample with computed stats
"""
raise NotImplementedError
def process(self, sample):
"""
For sample level, sample --> Boolean.
:param sample: sample to decide whether to filter
:return: true for keeping and false for filtering
"""
raise NotImplementedError
# Path: data_modori/ops/filter/language_id_score_filter.py
from jsonargparse.typing import ClosedUnitInterval
from loguru import logger
from data_modori.utils.constant import Fields, StatsKeys
from data_modori.utils.model_utils import prepare_model, get_model
from ..base_op import OPERATORS, Filter
@OPERATORS.register_module('language_id_score_filter')
class LanguageIDScoreFilter(Filter):
"""Filter to keep samples in a specific language with confidence score
larger than a specific min value."""
def __init__(self,
lang: str = '',
min_score: ClosedUnitInterval = 0.8,
*args,
**kwargs):
"""
Initialization method.
:param lang: Samples in which language to keep.
:param min_score: The min language identification confidence
scores of samples to keep.
:param args: extra args
:param kwargs: extra args
"""
super().__init__(*args, **kwargs)
self.lang = lang
self.min_score = min_score
self.model_key = prepare_model(lang=lang, model_type='fasttext')
def compute_stats(self, sample):
# check if it's computed already
if StatsKeys.lang in sample[
Fields.stats] and StatsKeys.lang_score in sample[Fields.stats]:
return sample
text = sample[self.text_key].lower().replace('\n', ' ')
| ft_model = get_model(self.model_key, lang=self.lang, model_type='fasttext') |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: 52phm/pylmkit
# Path: pylmkit/utils/data_utils.py
class Document(BaseModel):
page_content: str
metadata: dict = Field(default_factory=dict)
type: str = "Document"
def __str__(self):
return f"Document(page_content='{self.page_content}', metadata={self.metadata})"
# Path: pylmkit/core/base.py
class BaseKnowledgeBase(object):
def __init__(self, init_documents=None):
self.documents = []
self.splitter_documents = []
if isinstance(init_documents, list):
self.documents = init_documents
@classmethod
def load(cls, filepath, is_return=True, return_mode="doc", extend=True):
if filepath.endswith('.json'):
data = read_json(filepath)
elif filepath.endswith('.yaml') or filepath.endswith('yml'):
data = read_yaml(filepath) # data=[{},{}]
else:
raise Exception(f"The file type is not supported")
data_dict_as_document = dict_as_document(data)
result = cls()._base(documents=data_dict_as_document, return_mode=return_mode, is_return=is_return,
extend=extend)
if is_return:
return result
@classmethod
def add(cls, texts, metadatas=None, is_return=True, return_mode="doc", extend=True, types="Document"):
data_dict_as_document = text_as_document(texts=texts, metadatas=metadatas, types=types)
result = cls()._base(documents=data_dict_as_document, return_mode=return_mode, is_return=is_return,
extend=extend)
if is_return:
return result
def split(self, splitter=None, chunk_size=500, chunk_overlap=100, return_mode='doc', **kwargs):
if splitter is None:
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap, **kwargs)
else:
splitter = splitter
self.splitter_documents = splitter.split_documents(self.documents)
if return_mode == 'doc':
return self.splitter_documents
else:
return document_as_dict(self.splitter_documents)
def to_csv_loader(self, filepath, index=False, **kwargs):
data = document_as_dict(self.documents)
pd.DataFrame(data).to_csv(filepath, index=index, **kwargs)
def to_csv_splitter(self,
filepath,
splitter=None,
chunk_size=500,
chunk_overlap=100,
index=False,
splitter_kwargs={},
csv_kwargs={}
):
if not self.splitter_documents:
self.splitter_documents = self.split(splitter=splitter, chunk_size=chunk_size,
chunk_overlap=chunk_overlap, **splitter_kwargs)
data = document_as_dict(self.splitter_documents)
pd.DataFrame(data).to_csv(filepath, index=index, **csv_kwargs)
def clear(self, mode='doc'):
if mode == 'doc':
self.documents = []
else:
self.splitter_documents = []
def _base(self, documents, is_return=True, return_mode='doc', extend=True):
if extend:
self.documents.extend(documents) # # dict -> Document
if is_return:
if return_mode == 'doc':
return self.documents
else:
return document_as_dict(self.documents)
else:
# self.documents = documents # when extend is False, just reset documents
if is_return:
if return_mode == 'doc':
return documents
else:
return document_as_dict(documents)
# Path: pylmkit/tools/search.py
from duckduckgo_search import DDGS
from pylmkit.utils.data_utils import Document
from pylmkit.core.base import BaseKnowledgeBase
class WebSearch(DDGS, BaseKnowledgeBase):
def __init__(
self,
topk=5,
backend="api",
region="wt-wt",
timelimit=None,
safesearch="moderate",
init_documents=None,
timeout=10,
headers=None,
proxies=None
):
DDGS.__init__(
self,
timeout=timeout,
headers=headers,
proxies=proxies
)
BaseKnowledgeBase.__init__(self, init_documents=init_documents)
self.topk = int(topk)
self.backend = backend
self.region = region
self.timelimit = timelimit
self.safesearch = safesearch
def get(self, keyword):
if keyword:
search_gen = super().text(keywords=keyword,
backend=self.backend,
region=self.region,
max_results=self.topk,
timelimit=self.timelimit,
safesearch=self.safesearch
)
for i, page in enumerate(list(search_gen)):
if page:
| self.documents.append(Document( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: hadican/failedkite
# Path: config.py
class Config:
def __init__(self):
self.slack_token = self._get_env_variable('SLACK_TOKEN')
self.default_slack_email = self._get_env_variable('DEFAULT_SLACK_EMAIL')
self.author_mapping = self._load_author_mapping('/config/author_mapping.yml')
@staticmethod
def _get_env_variable(name):
value = os.environ.get(name)
if not value:
raise ValueError(f"{name} environment variable not set")
return value
@staticmethod
def _load_author_mapping(file_path):
with open(file_path, 'r') as mapping_file:
return yaml.safe_load(mapping_file)
# Path: notification_service.py
class NotificationService:
def __init__(self, slack_client, config):
self.slack_client = slack_client
self.config = config
self.default_user_id = self.slack_client.get_user_id_by_email(self.config.default_slack_email)
if self.default_user_id is None:
raise Exception(f"Failed to retrieve default Slack user ID for the email: {self.config.default_slack_email}")
self.logger = logging.getLogger(self.__class__.__name__)
def notify(self, build):
build_web_url = build['web_url']
build_source = build.get("source")
if build_source == "schedule":
trigger_message = f"Not a human-triggered job, no action was taken for the build url={build_web_url}"
self.logger.info(trigger_message)
return trigger_message, 200
fail_statuses = ["failing", "failed"]
build_status = build.get("state")
if build_status not in fail_statuses:
build_status_message = f"Not a build failure, no action taken for the build url={build_web_url}."
return build_status_message, 200
build_creator = build.get("creator")
build_author = build.get("author")
if build_creator:
email = build_creator.get("email")
elif build_author:
username = build_author.get("username")
slack_email = self.config.author_mapping.get(username)
if slack_email:
email = slack_email
else:
build_author_message = f"No user was found in the author mapping with the username={username} for the failing build url={build_web_url}"
self.slack_client.send_message(build_author_message, self.default_user_id)
return build_author_message, 500
else:
build_user_message = f"No user was found for the failing build url={build_web_url}"
self.slack_client.send_message(build_user_message, self.default_user_id)
return build_user_message, 500
user_id = self.slack_client.get_user_id_by_email(email)
if not user_id:
user_message = f"Failed to fetch user ID from Slack for email={email} for build url={build_web_url}"
return user_message, 500
buildkite_message = f"Your build has `failed`. Here is the URL to check: {build_web_url}"
status = self.slack_client.send_message(buildkite_message, user_id)
if status:
return buildkite_message, 200
else:
return "Failed to send Slack message.", 500
# Path: slack_client.py
class SlackClient:
def __init__(self, token):
self.client = WebClient(token=token)
self.logger = logging.getLogger(self.__class__.__name__)
def get_user_id_by_email(self, email):
try:
response = self.client.users_lookupByEmail(email=email)
if response["ok"]:
return response["user"]["id"]
else:
self.logger.error("Failed to fetch user ID for email=%s error=%s", email, response["error"])
return None
except Exception as e:
self.logger.error("Error fetching user ID for email=%s error=%s", email, str(e))
return None
def send_message(self, message, user_id):
try:
response = self.client.chat_postMessage(channel=user_id, text=message)
if response["ok"]:
return True
else:
self.logger.error("Failed to send Slack message with error=%s", response["error"])
return False
except Exception as e:
self.logger.error("Failed to send Slack message with error=%s", str(e))
return False
# Path: app.py
import logging
from flask import Flask, request
from config import Config
from notification_service import NotificationService
from slack_client import SlackClient
app = Flask(__name__)
logging.basicConfig(level=logging.INFO)
config = Config()
slack_client = SlackClient(token=config.slack_token)
| notification_service = NotificationService(slack_client, config) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: PufferAI/pokegym
# Path: pokegym/pyboy_binding.py
ACTIONS = (Down, Left, Right, Up, A, B, Start, Select)
# Path: pokegym/pyboy_binding.py
def make_env(gb_path, headless=True, quiet=False, **kwargs):
gb_path='pokemon_red.gb'
game = PyBoy(
gb_path,
debugging=False,
window_type='headless' if headless else 'SDL2',
hide_window=quiet,
**kwargs,
)
screen = game.botsupport_manager().screen()
if not headless:
game.set_emulation_speed(6)
return game, screen
# Path: pokegym/pyboy_binding.py
def open_state_file(path):
'''Load state file with BytesIO so we can cache it'''
with open(path, 'rb') as f:
initial_state = BytesIO(f.read())
return initial_state
# Path: pokegym/pyboy_binding.py
def load_pyboy_state(pyboy, state):
'''Reset state stream and load it into PyBoy'''
state.seek(0)
pyboy.load_state(state)
# Path: pokegym/pyboy_binding.py
def run_action_on_emulator(pyboy, screen, action,
headless=True, fast_video=True, frame_skip=24):
'''Sends actions to PyBoy'''
press, release = action.PRESS, action.RELEASE
pyboy.send_input(press)
if headless or fast_video:
pyboy._rendering(False)
frames = []
for i in range(frame_skip):
if i == 8: # Release button after 8 frames
pyboy.send_input(release)
if not fast_video: # Save every frame
frames.append(screen.screen_ndarray())
if i == frame_skip - 1:
pyboy._rendering(True)
pyboy.tick()
if fast_video: # Save only the last frame
frames.append(screen.screen_ndarray())
# Path: pokegym/ram_map.py
HP_ADDR = [0xD16C, 0xD198, 0xD1C4, 0xD1F0, 0xD21C, 0xD248]
MAX_HP_ADDR = [0xD18D, 0xD1B9, 0xD1E5, 0xD211, 0xD23D, 0xD269]
PARTY_SIZE_ADDR = 0xD163
PARTY_ADDR = [0xD164, 0xD165, 0xD166, 0xD167, 0xD168, 0xD169]
PARTY_LEVEL_ADDR = [0xD18C, 0xD1B8, 0xD1E4, 0xD210, 0xD23C, 0xD268]
POKE_XP_ADDR = [0xD179, 0xD1A5, 0xD1D1, 0xD1FD, 0xD229, 0xD255]
CAUGHT_POKE_ADDR = range(0xD2F7, 0xD309)
SEEN_POKE_ADDR = range(0xD30A, 0xD31D)
OPPONENT_LEVEL_ADDR = [0xD8C5, 0xD8F1, 0xD91D, 0xD949, 0xD975, 0xD9A1]
X_POS_ADDR = 0xD362
Y_POS_ADDR = 0xD361
MAP_N_ADDR = 0xD35E
BADGE_1_ADDR = 0xD356
OAK_PARCEL_ADDR = 0xD74E
OAK_POKEDEX_ADDR = 0xD74B
OPPONENT_LEVEL = 0xCFF3
ENEMY_POKE_COUNT = 0xD89C
EVENT_FLAGS_START_ADDR = 0xD747
EVENT_FLAGS_END_ADDR = 0xD761
MUSEUM_TICKET_ADDR = 0xD754
MONEY_ADDR_1 = 0xD347
MONEY_ADDR_100 = 0xD348
MONEY_ADDR_10000 = 0xD349
def bcd(num):
def bit_count(bits):
def read_bit(game, addr, bit) -> bool:
def read_uint16(game, start_addr):
def position(game):
def party(game):
def opponent(game):
def oak_parcel(game):
def pokedex_obtained(game):
def pokemon_seen(game):
def pokemon_caught(game):
def hp(game):
def money(game):
def badges(game):
def events(game):
# Path: pokegym/game_map.py
MAP_PATH = __file__.rstrip('game_map.py') + 'map_data.json'
MAP_DATA = json.load(open(MAP_PATH, 'r'))['regions']
MAP_DATA = {int(e['id']): e for e in MAP_DATA}
def local_to_global(r, c, map_n):
# Path: pokegym/environment.py
from pdb import set_trace as T
from gymnasium import Env, spaces
from pokegym.pyboy_binding import (ACTIONS, make_env, open_state_file,
load_pyboy_state, run_action_on_emulator)
from pokegym import ram_map, game_map
import numpy as np
import os
def play():
'''Creates an environment and plays it'''
env = Environment(rom_path='pokemon_red.gb', state_path=None, headless=False,
disable_input=False, sound=False, sound_emulated=False, verbose=True
)
env.reset()
env.game.set_emulation_speed(1)
# Display available actions
print("Available actions:")
| for idx, action in enumerate(ACTIONS): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: AlexandrErohin/home-assistant-flightradar24
# Path: custom_components/flightradar24/models.py
class BoundingBox:
"""Bounding box for retrieving state vectors."""
min_latitude: float
max_latitude: float
min_longitude: float
max_longitude: float
def validate(self) -> None:
"""Validate if the latitude and longitude are correct."""
self._check_latitude(self.min_latitude)
self._check_latitude(self.max_latitude)
self._check_longitude(self.min_longitude)
self._check_longitude(self.max_longitude)
def get_string(self) -> str:
return "{},{},{},{}".format(self.max_latitude, self.min_latitude, self.min_longitude, self.max_longitude)
@staticmethod
def _check_latitude(degrees: float) -> None:
if degrees < -90 or degrees > 90:
msg = f"Invalid latitude {degrees}! Must be in [-90, 90]."
raise Exception(msg)
@staticmethod
def _check_longitude(degrees: float) -> None:
if degrees < -180 or degrees > 180:
msg = f"Invalid longitude {degrees}! Must be in [-180, 180]."
raise Exception(msg)
# Path: custom_components/flightradar24/const.py
DOMAIN = "flightradar24"
# Path: custom_components/flightradar24/const.py
URL = 'https://www.flightradar24.com/'
# Path: custom_components/flightradar24/const.py
DEFAULT_NAME = "FlightRadar24"
# Path: custom_components/flightradar24/const.py
EVENT_FLIGHTRADAR24_ENTRY = f"{DOMAIN}_entry"
# Path: custom_components/flightradar24/const.py
EVENT_FLIGHTRADAR24_EXIT = f"{DOMAIN}_exit"
# Path: custom_components/flightradar24/coordinator.py
from typing import Any
from datetime import timedelta
from homeassistant.core import HomeAssistant
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
from homeassistant.helpers.device_registry import DeviceInfo
from .models import BoundingBox
from .const import (
DOMAIN,
URL,
DEFAULT_NAME,
EVENT_FLIGHTRADAR24_ENTRY,
EVENT_FLIGHTRADAR24_EXIT,
)
from logging import Logger
from FlightRadar24 import FlightRadar24API
import math
import pycountry
from __future__ import annotations
class FlightRadar24Coordinator(DataUpdateCoordinator[int]):
def __init__(
self,
hass: HomeAssistant,
bound: BoundingBox,
client: FlightRadar24API,
update_interval: int,
logger: Logger,
) -> None:
self._bound = bound
self._client = client
self._logger = logger
self.tracked: dict[int, dict[str, Any]] | None = None
self.entered = {}
self.exited = {}
self.device_info = DeviceInfo(
configuration_url=URL,
| identifiers={(DOMAIN, DEFAULT_NAME)}, |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ej0cl6/TextEE
# Path: TextEE/models/QueryAndExtract/metadata.py
class Metadata(object):
def __init__(self, metadata_path, dataset, type_set):
self.pos_set = ['ADJ', 'ADP', 'ADV', 'AUX', 'CCONJ', 'DET', 'INTJ', 'NOUN', 'NUM', 'PART', 'PRON', 'PROPN',
'PUNCT', 'SCONJ', 'SYM', 'VERB', 'X']
self.pos2id = dict((v, i) for v, i in zip(sorted(self.pos_set), range(len(self.pos_set))))
self.entity_to_ids = {'FAC': 0, 'GPE': 1, 'LOC': 2, 'ORG': 3, 'PER': 4, 'VEH': 5, 'WEA': 6, 'O': 7, '[PAD]': 8}
with open(metadata_path, 'r') as j:
meta = json.loads(j.read())
self.dataset = dataset
self.type_set = type_set
self.metadata = DatasetFactContainer(meta[dataset], type_set=type_set)
def __str__(self):
return '\n'.join(['%s:%s' % item for item in self.__dict__.items()])
# Path: TextEE/models/QueryAndExtract/utils.py
def pad_seq(data, pad_value=0, dtype='long'):
N = len(data)
for i in range(N):
data[i] = np.array(data[i])
maxlen = max([len(x) for x in data])
data = pad_sequences(data, maxlen=maxlen, dtype=dtype, truncating="post", padding="post", value=pad_value)
return torch.Tensor(data).cuda()
# Path: TextEE/models/QueryAndExtract/EAEmodel.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import ipdb
import ipdb
from transformers import BertConfig, RobertaConfig, BertModel, RobertaModel
from .metadata import Metadata
from .utils import pad_seq
from keras_preprocessing.sequence import pad_sequences
class QueryAndExtractEAEModel(nn.Module):
def __init__(self, config, tokenizer, type_set):
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.type_set = type_set
self.earl_model = EARLModel(config, tokenizer, type_set)
self.ner_model = NERModel(config, tokenizer)
def forward(self, batch):
ner_loss = self.ner_model(batch)
loss, score = self.earl_model(batch)
return loss, score, ner_loss
class EARLModel(nn.Module):
def __init__(self, config, tokenizer, type_set):
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.tokenizer_pad_value = self.tokenizer.convert_tokens_to_ids([self.tokenizer.pad_token])[0]
self.type_set = type_set
| self.metadata = Metadata(config.metadata_path, self.config.dataset, type_set) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: fofr/cog-sdxl-multi-controlnet-lora
# Path: controlnet_preprocess.py
class ControlNetPreprocessor:
ANNOTATOR_CLASSES = {
"none": None,
"edge_canny": CannyDetector,
"depth_leres": LeresDetector,
"depth_midas": MidasDetector,
"soft_edge_pidi": PidiNetDetector,
"soft_edge_hed": HEDdetector,
"lineart": LineartDetector,
"lineart_anime": LineartAnimeDetector,
"openpose": OpenposeDetector,
# "straight_edge_mlsd": None,
# "face_detector": None,
# "content_shuffle": None,
# "normal_bae": None,
# "segementation_sam": None,
}
ANNOTATOR_NAMES = list(ANNOTATOR_CLASSES.keys())
def __init__(self, predictor):
WeightsDownloader.download_if_not_exists(
CONTROLNET_PREPROCESSOR_URL, CONTROLNET_PREPROCESSOR_MODEL_CACHE
)
self.annotators = {}
self.predictor = predictor
torch.device("cuda")
@staticmethod
def get_annotator_names():
return ControlNetPreprocessor.ANNOTATOR_NAMES
def initialize_detector(
self, detector_class, model_name="lllyasviel/Annotators", **kwargs
):
print(f"Initializing {detector_class.__name__}")
if hasattr(detector_class, 'from_pretrained'):
return detector_class.from_pretrained(
model_name,
cache_dir=CONTROLNET_PREPROCESSOR_MODEL_CACHE,
**kwargs,
)
else:
return detector_class(**kwargs)
def annotators_list(self):
return list(self.annotators.keys())
def process_image(self, image, annotator):
print(f"Processing image with {annotator}")
if annotator not in self.annotators:
self.annotators[annotator] = self.initialize_detector(
self.ANNOTATOR_CLASSES[annotator]
)
return self.annotators[annotator](image)
# Path: weights_downloader.py
class WeightsDownloader:
@staticmethod
def download_if_not_exists(url, dest):
if not os.path.exists(dest):
WeightsDownloader.download(url, dest)
@staticmethod
def download(url, dest):
start = time.time()
print("downloading url: ", url)
print("downloading to: ", dest)
subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
print("downloading took: ", time.time() - start)
# Path: controlnet.py
import torch
from diffusers import ControlNetModel
from controlnet_preprocess import ControlNetPreprocessor
from weights_downloader import WeightsDownloader
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"soft_edge_pidi",
"soft_edge_hed",
"lineart",
"lineart_anime",
"openpose",
# Preprocessors without an XL model yet
# "straight_edge_mlsd",
# "face_detector",
# "content_shuffle",
# "normal_bae",
# "segementation_sam",
]
def __init__(self, predictor):
WeightsDownloader.download_if_not_exists(CONTROLNET_URL, CONTROLNET_MODEL_CACHE)
self.predictor = predictor
self.controlnet_preprocessor = None
self.models = {}
def initialize_controlnet(self, model_name):
print("Initializing", model_name)
return ControlNetModel.from_pretrained(
model_name, cache_dir=CONTROLNET_MODEL_CACHE, torch_dtype=torch.float16
)
def get_model(self, controlnet_name):
if controlnet_name not in self.models:
if controlnet_name.startswith("edge_"):
self.models[controlnet_name] = self.initialize_controlnet("diffusers/controlnet-canny-sdxl-1.0")
elif controlnet_name.startswith("depth_"):
self.models[controlnet_name] = self.initialize_controlnet("diffusers/controlnet-depth-sdxl-1.0-small")
elif controlnet_name.startswith("soft_edge") or controlnet_name.startswith("lineart"):
self.models[controlnet_name] = self.initialize_controlnet("SargeZT/controlnet-sd-xl-1.0-softedge-dexined")
elif controlnet_name == "openpose":
self.models[controlnet_name] = self.initialize_controlnet("thibaud/controlnet-openpose-sdxl-1.0")
elif controlnet_name == "illusion":
self.models[controlnet_name] = self.initialize_controlnet("monster-labs/control_v1p_sdxl_qrcode_monster")
return self.models.get(controlnet_name)
def get_models(self, controlnet_names):
models = [
self.get_model(controlnet_name) for controlnet_name in controlnet_names
]
return list(filter(None, models))
def preprocess(self, image, controlnet_name):
# Illusion model needs no preprocessing
if controlnet_name == "illusion" or controlnet_name == "none":
return image
if self.controlnet_preprocessor is None:
| self.controlnet_preprocessor = ControlNetPreprocessor(self.predictor) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ahayler/s4c
# Path: utils/array_operations.py
def to(data, device, non_blocking=True):
if isinstance(data, dict):
return {k: to(data[k], device, non_blocking=non_blocking) for k in data.keys()}
elif isinstance(data, list):
return [to(v, device, non_blocking=non_blocking) for v in data]
else:
return data.to(device, non_blocking=non_blocking)
# Path: utils/metrics.py
class MeanMetric(Metric):
def __init__(self, output_transform=lambda x: x["output"], device="cpu"):
self._sum = None
self._num_examples = None
self.required_output_keys = ()
super(MeanMetric, self).__init__(output_transform=output_transform, device=device)
@reinit__is_reduced
def reset(self):
self._sum = torch.tensor(0, device=self._device, dtype=float)
self._num_examples = 0
super(MeanMetric, self).reset()
@reinit__is_reduced
def update(self, value):
if torch.any(torch.isnan(torch.tensor(value))):
return
self._sum += value
self._num_examples += 1
@sync_all_reduce("_num_examples:SUM", "_sum:SUM")
def compute(self):
if self._num_examples == 0:
raise NotComputableError('CustomAccuracy must have at least one example before it can be computed.')
return self._sum.item() / self._num_examples
@torch.no_grad()
def iteration_completed(self, engine: Engine) -> None:
output = self._output_transform(engine.state.output)
self.update(output)
# Path: utils/base_trainer.py
import json
import time
import ignite
import ignite.distributed as idist
import torch
from datetime import datetime
from pathlib import Path
from typing import Union
from omegaconf import OmegaConf
from ignite.contrib.engines import common
from ignite.contrib.handlers import TensorboardLogger
from ignite.contrib.handlers.base_logger import BaseHandler
from ignite.engine import Engine, Events, EventEnum
from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine
from ignite.utils import manual_seed, setup_logger
from torch.cuda.amp import autocast, GradScaler
from utils.array_operations import to
from utils.metrics import MeanMetric
from torch.backends import cudnn
# used for debugging
torch.autograd.set_detect_anomaly(True)
def base_training(local_rank, config, get_dataflow, initialize, get_metrics, visualize):
# copy the segmentation mode to the data and model_conf part of the config
config['data']['segmentation_mode'] = config.get("segmentation_mode", None)
config['model_conf']['segmentation_mode'] = config.get("segmentation_mode", None)
rank = idist.get_rank()
manual_seed(config["seed"] + rank)
device = idist.device()
logger = setup_logger(name=config["name"])
log_basic_info(logger, config)
output_path = config["output_path"]
if rank == 0:
if config["stop_iteration"] is None:
now = datetime.now().strftime("%Y%m%d-%H%M%S")
else:
now = f"stop-on-{config['stop_iteration']}"
folder_name = f"{config['name']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}"
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output_path"] = output_path.as_posix()
logger.info(f"Output path: {config['output_path']}")
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
# Setup dataflow, model, optimizer, criterion
loaders = get_dataflow(config, logger)
if len(loaders) == 2:
train_loader, test_loader = loaders
vis_loader = None
else:
train_loader, test_loader, vis_loader = loaders
if hasattr(train_loader, "dataset"):
logger.info(f"Dataset length: Train: {len(train_loader.dataset)}, Test: {len(test_loader.dataset)}")
config["num_iters_per_epoch"] = len(train_loader)
model, optimizer, criterion, lr_scheduler = initialize(config, logger)
logger.info(f"Model parameters: {sum(p.numel() for p in model.parameters())}")
# Let's now setup evaluator engine to perform model's validation and compute metrics
metrics = get_metrics(config, device)
| metrics_loss = {k: MeanMetric((lambda y: lambda x: x["loss_dict"][y])(k)) for k in criterion.get_loss_metric_names()} |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Emmo00/alxcheck
# Path: alxcheck/utils/error_logging.py
def print_no_module_docstring(file_path):
print(Fore.RED + f"{file_path} does not have Module DocString" + Fore.RESET)
# Path: alxcheck/utils/error_logging.py
def print_no_function_docstring(file_path, function_name):
print(
Fore.RED
+ f"In {file_path}, the {function_name} function has no Function DocString"
+ Fore.RESET
)
# Path: alxcheck/utils/error_logging.py
def print_no_class_docstring(file_path, class_name):
print(
Fore.RED
+ f"In {file_path}, the {class_name} class has no Class DocString"
+ Fore.RESET
)
# Path: alxcheck/utils/error_logging.py
def print_check_docstrings(file_path):
print(Fore.RED + f"Error: Check docstrings in {file_path}" + Fore.RESET)
# Path: alxcheck/utils/error_logging.py
def print_error_parsing_file(file_path):
import ast
try:
with open(file_path, "r") as f:
ast.parse(f.read())
except SyntaxError as syntax_error:
print(
Fore.RED
+ f"SyntaxError\n\tFile: {file_path}\n\tLine: {syntax_error.lineno}\tMessage: {syntax_error.msg}"
+ Fore.RESET
)
except Exception as e:
print(Fore.RED + f"Error Parsing File:\n\t{type(e)}" + Fore.RESET)
# Path: alxcheck/checks/python.py
import os
import ast
import subprocess
from ..utils.error_logging import (
print_no_module_docstring,
print_no_function_docstring,
print_no_class_docstring,
print_check_docstrings,
print_error_parsing_file,
)
def check_file_is_executable(file_path):
flag = True
if not os.access(file_path, os.X_OK):
flag = False
return flag
def check_python_shebang(file_path):
flag = True
with open(file_path, "rb") as f:
first_line = f.readline().strip()
if first_line not in (b"#!/usr/bin/python3", b"#!/usr/bin/env python3"):
flag = False
return flag
def check_module_function_class_documentation(file_path):
flag = True
with open(file_path, "rb") as f:
content = f.read()
# remove shebang
if content.startswith(b"#!"):
if len(content.split(b"\n")) < 2:
content = ""
else:
content = content.split(b"\n", 1)[1]
tree = None
try:
tree = ast.parse(content)
except Exception:
print_error_parsing_file(file_path)
try:
if tree is None:
return
for node in ast.walk(tree):
# check module docstring
if isinstance(node, ast.Module):
if not isinstance(node.body[0].value, ast.Str):
flag = False
print_no_module_docstring(file_path)
return
# check function docstring
if isinstance(node, ast.FunctionDef) and not isinstance(
node.body[0].value, ast.Str
):
flag = False
print_no_function_docstring(file_path, node.name)
# check class docstring
if isinstance(node, ast.ClassDef) and not isinstance(
node.body[0].value, ast.Str
):
flag = False
print_no_class_docstring(file_path, node.name)
except Exception:
| print_check_docstrings(file_path) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: TimbreWatermarking/TimbreWatermarking
# Path: voice.clone/Fastspeech2/TTS/tts/utils/text/english/abbreviations.py
# Path: voice.clone/Fastspeech2/TTS/tts/utils/text/english/number_norm.py
def normalize_numbers(text):
text = re.sub(_comma_number_re, _remove_commas, text)
text = re.sub(_currency_re, _expand_currency, text)
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
text = re.sub(_ordinal_re, _expand_ordinal, text)
text = re.sub(_number_re, _expand_number, text)
return text
# Path: voice.clone/Fastspeech2/TTS/tts/utils/text/english/time_norm.py
def expand_time_english(text: str) -> str:
return re.sub(_time_re, _expand_time_english, text)
# Path: voice.clone/Fastspeech2/TTS/tts/utils/text/french/abbreviations.py
# Path: voice.clone/Fastspeech2/TTS/tts/utils/text/cleaners.py
import re
from anyascii import anyascii
from TTS.tts.utils.text.chinese_mandarin.numbers import replace_numbers_to_characters_in_text
from .english.abbreviations import abbreviations_en
from .english.number_norm import normalize_numbers as en_normalize_numbers
from .english.time_norm import expand_time_english
from .french.abbreviations import abbreviations_fr
"""Set of default text cleaners"""
# TODO: pick the cleaner for languages dynamically
# Regular expression matching whitespace:
_whitespace_re = re.compile(r"\s+")
def expand_abbreviations(text, lang="en"):
if lang == "en":
_abbreviations = abbreviations_en
elif lang == "fr":
_abbreviations = abbreviations_fr
for regex, replacement in _abbreviations:
text = re.sub(regex, replacement, text)
return text
def lowercase(text):
return text.lower()
def collapse_whitespace(text):
return re.sub(_whitespace_re, " ", text).strip()
def convert_to_ascii(text):
return anyascii(text)
def remove_aux_symbols(text):
text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text)
return text
def replace_symbols(text, lang="en"):
text = text.replace(";", ",")
text = text.replace("-", " ")
text = text.replace(":", ",")
if lang == "en":
text = text.replace("&", " and ")
elif lang == "fr":
text = text.replace("&", " et ")
elif lang == "pt":
text = text.replace("&", " e ")
return text
def basic_cleaners(text):
"""Basic pipeline that lowercases and collapses whitespace without transliteration."""
text = lowercase(text)
text = collapse_whitespace(text)
return text
def transliteration_cleaners(text):
"""Pipeline for non-English text that transliterates to ASCII."""
# text = convert_to_ascii(text)
text = lowercase(text)
text = collapse_whitespace(text)
return text
def basic_german_cleaners(text):
"""Pipeline for German text"""
text = lowercase(text)
text = collapse_whitespace(text)
return text
# TODO: elaborate it
def basic_turkish_cleaners(text):
"""Pipeline for Turkish text"""
text = text.replace("I", "ı")
text = lowercase(text)
text = collapse_whitespace(text)
return text
def english_cleaners(text):
"""Pipeline for English text, including number and abbreviation expansion."""
# text = convert_to_ascii(text)
text = lowercase(text)
| text = expand_time_english(text) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: nillion-oss/tinysig
# Path: src/tinysig/utils.py
def add(values: list[int], size: int) -> int:
"""
Calculate the sum of a list of integers modulo 'size'.
Args:
values (list[int]): A list of integers to be summed.
size (int): The modulo value.
Returns:
int: The sum of the integers in 'values' modulo 'size'.
Examples:
>>> add([2, 4, 6], 5)
2
>>> add([3, 7, 10], 4)
0
"""
result = 0
for v in values:
result = (result + v) % size
return result
# Path: src/tinysig/utils.py
def generate_additive_shares(secret: int, n: int, size: int) -> list[int]:
"""
Generates additive secret shares for a given secret value, using modular arithmetic.
Args:
secret (int): The secret value to be shared.
n (int): The number of shares to generate.
size (int): The modulus value for modular arithmetic.
Returns:
List[int]: A list of additive secret shares.
Example:
>>> random.seed(0)
>>> generate_additive_shares(26, 3, 2**5)
[8, 24, 26]
"""
shares = [rand(size) for _ in range(n-1)]
last_sh = (secret - add(shares, size)) % size
shares = [last_sh] + shares
return shares
# Path: src/tinysig/network.py
from dataclasses import dataclass, field
from typing import Dict, List, Union
from .utils import add, generate_additive_shares
@dataclass
class Node:
""" Represents a node in the network."""
id: int
"""Identifier for the node."""
shares_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding shares."""
open_db: Dict[str, int] = field(default_factory=dict)
"""Database for holding open values."""
he_public_keys: Dict[int, int] = field(default_factory=dict)
"""Dictionary for holding homomorphic encryption public keys."""
def get_share(self, label: str) -> None:
"""Retrieve a share from the 'shares_db'."""
return self.shares_db[label]
def get_open(self, label: str) -> None:
"""Retrieve an open value from the 'open_db'."""
return self.open_db[label]
def set_share(self, value, label: str) -> None:
"""Set a share in the 'shares_db'."""
self.shares_db[label] = value
def set_open(self, value, label: str) -> None:
"""Set an open value in the 'open_db'."""
self.open_db[label] = value
def delete_share(self, label: str) -> None:
"""Delete a share from the 'shares_db'."""
self.shares_db.pop(label)
def delete_open(self, label: str) -> None:
"""Delete an open value from the 'open_db'."""
self.open_db.pop(label)
@dataclass
class Client(Node):
"""Represents a client node in the network, inheriting from the 'Node' class."""
he_private_key: int = field(default=0)
class Network:
"""Represents a network of nodes and clients.
Manages the interactions and cryptographic operations within the network,
including sharing secrets, broadcasting values, and reconstructing shared values.
"""
nodes: List[Node]
"""List of nodes in the network."""
clients: List[Client]
"""List of clients in the network."""
q: int
"""Prime field."""
h: int
"""Multiplicative field generator."""
def __init__(self, N, q, h=2, C=1):
"""
Initialize the network with 'N' nodes, prime field 'q', field generator 'h', and 'C' clients.
Parameters:
N (int): Number of nodes in the network.
q (int): Prime field.
h (int): Multiplicative field generator (default is 2).
C (int): Number of clients in the network (default is 1).
"""
self.nodes = [Node(i+1) for i in range(N)]
self.clients = [Client(i+1) for i in range(C)]
self.N = N
self.q = q
self.h = h
def print(self):
"""Print a readable representation of the network, including nodes and clients with their databases."""
print(f"Network(N={len(self.nodes)}, q={self.q},")
print(" nodes=[")
for node in self.nodes:
print(f" Node(id={node.id},")
print(" shares_db={")
for key, value in node.shares_db.items():
print(f" {key}: {value},")
print(" },")
print(" public_keys={")
for key, value in node.he_public_keys.items():
print(f" {key}: {value},")
print(" },")
print(" open_db={")
for key, value in node.open_db.items():
print(f" {key}: {value},")
print(" }")
print(" )")
print(" ]\n)")
print(" clients=[")
for client in self.clients:
print(f" Client(id={client.id},")
print(" shares_db={")
for key, value in client.shares_db.items():
print(f" {key}: {value},")
print(" },")
print(" public_keys={")
for key, value in client.he_public_keys.items():
print(f" {key}: {value},")
print(" },")
print(f" private_keys={client.he_private_key},")
print(" open_db={")
for key, value in client.open_db.items():
print(f" {key}: {value},")
print(" }")
print(" )")
print(" ]\n)")
def reconstruct_local(self, type_share: str, get_label: str, save_label: str, party: Union[Client, Node]) -> None:
"""Locally reconstruct exponent share ('exp') or base ('base') shared value."""
type_label = "_sh_exp" if type_share == "exp" else "_sh_base"
p = (self.q - 1) if type_share == "exp" else self.q
shares = [party.get_share(get_label+type_label+"_node_"+str(node.id)) for node in self.nodes]
| reconstructed = add(shares, p) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: naver-ai/scob
# Path: lightning_modules/data_modules/transforms/common.py
TRANSFORM_NAME_TO_CLASS = {
"RandomRotate": RandomRotate,
"CraftRandomCrop": CraftRandomCrop,
"Resize": Resize,
"ResizeOD": ResizeOD,
"PhotometricDistort": PhotometricDistort,
"MoCo_PhotometricDistort": MoCo_PhotometricDistort,
"ResizeTwoPic": ResizeTwoPic,
"ResizeMultiview": ResizeMultiview,
"KeepAspectRatioBilinearResize": KeepAspectRatioBilinearResize,
"RandomCrop": RandomCrop,
"MultiScaleResize": MultiScaleResize,
"KeepAspectRatioBilinearResizeOD": KeepAspectRatioBilinearResizeOD,
"Otor_OriginDistort": Otor_OriginDistort,
}
# Path: lightning_modules/data_modules/transforms/common.py
class W_Compose:
"""
Modified pytorch compose
pytorch.org/vision/0.10/transforms.html#torchvision.transforms.Compose
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, quads=None):
second_img = None
multiview_img = None
for transform in self.transforms:
if (
isinstance(transform, ResizeTwoPic)
and transform.second_size is not None
):
img, second_img, quads = transform(img, quads)
elif isinstance(transform, ResizeMultiview):
img, multiview_img, quads = transform(img, quads)
elif isinstance(transform, MoCo_PhotometricDistort):
img, _ = transform(img, quads)
multiview_img, quads = transform(multiview_img, quads)
else:
img, quads = transform(img, quads)
if second_img is None and multiview_img is None:
return img, quads
elif multiview_img is not None:
return img, multiview_img, quads
else:
return img, second_img, quads
def __repr__(self):
format_string = self.__class__.__name__ + "("
for transform in self.transforms:
format_string += f"\n {transform}"
format_string += "\n)"
return format_string
# Path: utils/dataset_utils.py
def get_image_normalize_mean_and_std(image_normalize):
if image_normalize is None:
mean_and_std = None
elif image_normalize == "imagenet_default":
mean_and_std = (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
elif image_normalize == "imagenet_inception":
# In BEiT, "--imagenet_default_mean_and_std: enable this for ImageNet-1k pre-training,
# i.e., (0.485, 0.456, 0.406) for mean and (0.229, 0.224, 0.225) for std.
# We use (0.5, 0.5, 0.5) for mean and (0.5, 0.5, 0.5) for std by default
# on other pre-training data."
mean_and_std = (IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD)
else:
raise ValueError(f"Unknown image_normalize={image_normalize}")
return mean_and_std
# Path: lightning_modules/data_modules/transforms/transformer_decoder.py
from typing import List, Tuple, Union
from lightning_modules.data_modules.transforms.common import (
TRANSFORM_NAME_TO_CLASS,
W_Compose,
)
from utils.dataset_utils import get_image_normalize_mean_and_std
import torch
import torchvision.transforms as transforms
class TransformerDecoderTransformForFineTuning:
"""
- BEiT:
https://github.com/microsoft/unilm/blob/master/beit/datasets.py#L27
- TrOCR:
https://github.com/microsoft/unilm/blob/53995b4876464146365693396aaaa09e88a4494e/trocr/data_aug.py#L120
"""
def __init__(
self,
size: Union[Tuple, List],
transforms_list=None,
image_normalize="imagenet_default",
):
self.common_transform = self.__get_common_transform(size, transforms_list)
self.patch_transform = self.__get_patch_transform(image_normalize)
def __call__(self, img, quads):
for_patches, quads = self.common_transform(img, quads)
for_patches = self.patch_transform(for_patches)
return for_patches, quads
@staticmethod
def __get_common_transform(size, transforms_list):
tranforms = []
for transform_obj in transforms_list:
transform_class = TRANSFORM_NAME_TO_CLASS[transform_obj.name]
if transform_obj.params is not None:
params = dict(transform_obj.params)
else:
params = {}
if transform_obj.name in [
"Resize",
"ResizeOD",
"KeepAspectRatioBilinearResize",
"ResizeMultiview",
"MultiScaleResize",
"KeepAspectRatioBilinearResizeOD",
]:
params["size"] = size
elif transform_obj.name == "ResizeTwoPic":
params["size"] = size
tranforms.append(transform_class(**params))
return W_Compose(tranforms)
@staticmethod
def __get_patch_transform(image_normalize):
patch_trans = [transforms.ToTensor()]
| mean_and_std = get_image_normalize_mean_and_std(image_normalize) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: speckai/speck
# Path: src/python/speck/chat/entities.py
NOT_GIVEN = None
class Message(BaseModel):
class SafeDict(dict):
class Prompt(str):
class Response(BaseModel):
class MessageChunk(BaseModel):
class Stream:
class LogConfig(BaseModel):
class Config:
class ChatConfig:
class OpenAIChatConfig(ChatConfig):
class IChatClient(ABC):
def __missing__(self, key):
def to_dict(self):
def __init__(
self,
messages: PromptTypes,
variables: Union[dict[str, str], None] = None,
**kwargs,
):
def create(
cls, messages: PromptTypes, variables: dict[str, str] = None
) -> "Prompt":
def _read(cls, lines: str) -> "Prompt":
def add_message():
def read(cls, path: str, name: Union[str, None] = None) -> "Prompt":
def read_all(cls, path: str) -> dict[str, "Prompt"]:
def _file(self):
def write(cls, prompt: Union["Prompt", dict[str, "Prompt"]], path: str):
def __new__(
cls,
messages: PromptTypes,
**kwargs,
):
def from_openai(cls, messages: list[dict[str, str]]):
def to_list(self):
def to_dict(self):
def _apply_variables(
messages: list[Message], variables: dict[str, str]
) -> list[Message]:
def _check_duplicate_keys(self, other_variables: dict[str, str]) -> dict[str, str]:
def _remove_duplicate_keys_from_messages(
self, other_variables: dict[str, str]
) -> list[Message]:
def format(self, *args, **kwargs):
def __add__(self, other):
def __str__(self):
def __init__(
self,
content: str,
closed: bool = False,
prompt_tokens: Union[int, None] = None,
completion_tokens: Union[int, None] = None,
raw: Union[dict, None] = None,
**kwargs,
):
def create(cls, response: ResponseTypes) -> "Response":
def __str__(self):
def encode(self, encoding: str = "utf-8"):
def __init__(
self,
client: "Speck",
iterator: Iterator[Any],
kwargs: dict,
log_config: "LogConfig",
processor: Callable[[Any], MessageChunk],
):
def _log(self):
def _process(self, item) -> MessageChunk:
def __next__(self) -> MessageChunk:
def __iter__(self) -> Iterator[MessageChunk]:
def close(self):
def __init__(
self,
*,
provider: str = None,
model: OpenAIModel,
stream: bool = False,
_log: bool = True,
temperature: Union[Optional[float], NotGiven] = NOT_GIVEN,
max_tokens: Union[Optional[int], NotGiven] = NOT_GIVEN,
top_p: Union[Optional[float], NotGiven] = NOT_GIVEN,
frequency_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN,
presence_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN,
**config_kwargs,
):
def to_dict(self):
def _convert_optional(self, value):
def create(cls, config: ChatConfigTypes, kwargs: dict = None) -> "ChatConfig":
def get(self, key: str, default: Any = None) -> Any:
def convert(self, provider: str = "speck") -> "ChatConfig":
def log_chat(
self,
*,
log_config: LogConfig,
prompt: Prompt,
response: Response,
provider: str = "speck",
):
def encode(self, encoding: str = "utf-8"):
def __str__(self):
def __init__(
self,
model: OpenAIModel,
stream: bool = False,
_log: bool = True,
temperature: Union[Optional[float], NotGiven] = NOT_GIVEN,
max_tokens: Union[Optional[int], NotGiven] = NOT_GIVEN,
top_p: Union[Optional[float], NotGiven] = NOT_GIVEN,
frequency_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN,
presence_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN,
**config_kwargs,
):
def convert(self, provider: str = "speck") -> ChatConfig:
def debug_chat(
self, prompt: "Prompt", config: "ChatConfig"
) -> ("Prompt", "ChatConfig"):
def chat(
self,
prompt: PromptTypes,
config: Union[ChatConfig, NotGiven] = NOT_GIVEN,
**config_kwargs,
) -> Union[Response, Stream]:
async def achat(
self,
prompt: PromptTypes,
config: Union[ChatConfig, NotGiven] = NOT_GIVEN,
**config_kwargs,
) -> Union[Response, Stream]:
# Path: src/python/speck/connections/providers.py
class Providers(Enum):
Anthropic = "Anthropic"
AzureOpenAI = "AzureOpenAI"
OpenAI = "OpenAI"
CustomProvider = "CustomProvider"
Replicate = "Replicate"
# Path: src/python/speck/connections/connector.py
from abc import ABC
from ..chat.entities import ChatLogger, LogConfig, Prompt, Response
from .providers import Providers
class IConnector(ABC):
_client: "Speck"
def __init__(self, client: "Speck", provider: Providers):
self._client = client
self.provider = provider
# @abstractmethod
# def process_message(self, messages: Messages, model: str) -> str:
# pass
def _get_log_kwargs(self, prompt: Prompt, response: Response, **kwargs):
return {
"provider": self.provider,
"model": kwargs.get("model"),
"temperature": kwargs.get("temperature"),
"stream": kwargs.get("stream", False),
"prompt": prompt,
"config": kwargs,
"response": response,
}
def log(
self, *, log_config: LogConfig, prompt: Prompt, response: Response, **kwargs
):
# Todo: refactor to use config.log_chat !!!
| ChatLogger.log( |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: chaiNNer-org/spandrel
# Path: src/spandrel/architectures/KBNet/arch/kb_utils.py
class KBAFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, att, selfk, selfg, selfb, selfw):
B, nset, H, W = att.shape
KK = selfk**2
selfc = x.shape[1]
att = att.reshape(B, nset, H * W).transpose(-2, -1)
ctx.selfk, ctx.selfg, ctx.selfc, ctx.KK, ctx.nset = (
selfk,
selfg,
selfc,
KK,
nset,
)
ctx.x, ctx.att, ctx.selfb, ctx.selfw = x, att, selfb, selfw
bias = att @ selfb
attk = att @ selfw
uf = torch.nn.functional.unfold(x, kernel_size=selfk, padding=selfk // 2)
# for unfold att / less memory cost
uf = uf.reshape(B, selfg, selfc // selfg * KK, H * W).permute(0, 3, 1, 2)
attk = attk.reshape(B, H * W, selfg, selfc // selfg, selfc // selfg * KK)
x = attk @ uf.unsqueeze(-1) #
del attk, uf
x = x.squeeze(-1).reshape(B, H * W, selfc) + bias
x = x.transpose(-1, -2).reshape(B, selfc, H, W)
return x
@staticmethod
def backward(ctx, grad_output):
x, att, selfb, selfw = ctx.x, ctx.att, ctx.selfb, ctx.selfw
selfk, selfg, selfc, KK, nset = (
ctx.selfk,
ctx.selfg,
ctx.selfc,
ctx.KK,
ctx.nset,
)
B, selfc, H, W = grad_output.size()
dbias = grad_output.reshape(B, selfc, H * W).transpose(-1, -2)
dselfb = att.transpose(-2, -1) @ dbias
datt = dbias @ selfb.transpose(-2, -1)
attk = att @ selfw
uf = F.unfold(x, kernel_size=selfk, padding=selfk // 2)
# for unfold att / less memory cost
uf = uf.reshape(B, selfg, selfc // selfg * KK, H * W).permute(0, 3, 1, 2)
attk = attk.reshape(B, H * W, selfg, selfc // selfg, selfc // selfg * KK)
dx = dbias.view(B, H * W, selfg, selfc // selfg, 1)
dattk = dx @ uf.view(B, H * W, selfg, 1, selfc // selfg * KK)
duf = attk.transpose(-2, -1) @ dx
del attk, uf
dattk = dattk.view(B, H * W, -1)
datt += dattk @ selfw.transpose(-2, -1)
dselfw = att.transpose(-2, -1) @ dattk
duf = duf.permute(0, 2, 3, 4, 1).view(B, -1, H * W)
dx = F.fold(duf, output_size=(H, W), kernel_size=selfk, padding=selfk // 2)
datt = datt.transpose(-1, -2).view(B, nset, H, W)
return dx, datt, None, None, dselfb, dselfw
# Path: src/spandrel/architectures/KBNet/arch/kb_utils.py
class LayerNorm2d(nn.Module):
def __init__(self, channels, eps=1e-6, requires_grad=True):
super().__init__()
self.register_parameter(
"weight", nn.Parameter(torch.ones(channels), requires_grad=requires_grad)
)
self.register_parameter(
"bias", nn.Parameter(torch.zeros(channels), requires_grad=requires_grad)
)
self.eps = eps
def forward(self, x):
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
# Path: src/spandrel/architectures/KBNet/arch/kb_utils.py
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
# Path: src/spandrel/architectures/KBNet/arch/kbnet_s.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from .kb_utils import KBAFunction, LayerNorm2d, SimpleGate
# type: ignore
class KBBlock_s(nn.Module):
def __init__(
self, c, DW_Expand=2, FFN_Expand=2, nset=32, k=3, gc=4, lightweight=False
):
super().__init__()
self.k, self.c = k, c
self.nset = nset
dw_ch = int(c * DW_Expand)
ffn_ch = int(FFN_Expand * c)
self.g = c // gc
self.w = nn.Parameter(torch.zeros(1, nset, c * c // self.g * self.k**2))
self.b = nn.Parameter(torch.zeros(1, nset, c))
self.init_p(self.w, self.b)
| self.norm1 = LayerNorm2d(c) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: robocorp/llmstatemachine
# Path: src/llmstatemachine/function.py
def create_definition(func: Callable, goal: str) -> FunctionDefinition:
source = inspect.getsource(func)
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4-1106-preview",
messages=[
{
"role": "system",
"content": f"""Extract function metadata from the following function definition:
```
{source}
```
Focus on details that are meaningful for the following assignment:
```
{goal}
```
Extract the function metadata.
""",
}
],
functions=[
{
"description": "FunctionDefinition is a tool for metadata extraction",
"name": "FunctionDefinition",
"parameters": {
"type": "object",
"properties": {
"thinking": {
"type": "string",
"description": (
"Logical thinking about function metadata extraction and draft of the answer."
),
},
"function_name": {
"type": "string",
"description": "Name of the function.",
},
"function_description": {
"type": "string",
"description": "Short well thought description of what the function is used for.",
},
"argument_description": {
"type": "string",
"description": "Short well thought description of what the function argument is used for.",
},
},
"required": [
"thinking",
"function_name",
"function_description",
"argument_description",
],
},
}
],
function_call={"name": "FunctionDefinition"},
)
msg = response.choices[0].message
assert msg.function_call
print(msg.function_call)
args: FunctionDefinition = json.loads(msg.function_call.arguments)
if not is_valid_function_definition(args):
raise ValueError("Invalid data format for FunctionDefinition")
return args
# Path: src/llmstatemachine/function.py
class FunctionDefinition(TypedDict):
function_name: str
function_description: str
argument_description: str
# Path: src/llmstatemachine/workflow_agent.py
import json
from typing import Dict, Callable, Any, Tuple, List
from openai.types.chat.chat_completion_message import FunctionCall
from .function import create_definition, FunctionDefinition
from openai import OpenAI
from openai.types.chat import (
ChatCompletionMessageParam,
ChatCompletionMessage,
completion_create_params,
)
TransitionFunction = Callable[[...], str]
FUNCTION_NAME = "ActionSelector"
MODEL = "gpt-4-1106-preview" # "gpt-4"
_CURRENT_STEPPING_AGENT = None
class WorkflowAgent:
def __init__(
self, goal: str, transitions: Dict[str, Dict[str, TransitionFunction]]
):
if "INIT" not in transitions:
raise Exception("Must define INIT state")
self._transitions: Dict[str, Dict[str, TransitionFunction]] = transitions
self._current_state = "INIT"
self.next_state = None
self._messages: List[ChatCompletionMessageParam] = []
self._messages.append({"role": "system", "content": goal})
self._client = OpenAI()
| self._func_defs: Dict[TransitionFunction, FunctionDefinition] = dict() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: GoldenThrust/Virtual-Bank
# Path: api/debit_cards/models.py
class DebitCard(models.Model):
account = models.ForeignKey(Account, on_delete=models.CASCADE)
card_number = models.BigIntegerField()
cvv = models.CharField(max_length=4)
expiration_date = models.DateTimeField()
created_date = models.DateTimeField(auto_now_add=True)
def __str__(self):
return f"{self.card_number} - User: {self.account.user.first_name} {self.account.user.last_name}"
# Path: api/debit_cards/models.py
class DebitCardTransaction(models.Model):
transaction = models.OneToOneField(Transaction, on_delete=models.CASCADE, related_name='debit_card')
transaction_partner_account = models.ForeignKey(Account, on_delete=models.CASCADE, related_name='transaction_partner_debit_card')
def __str__(self):
return f"Transfer ID: {self.pk} - Receiver: {self.transaction.account.user.first_name} {self.transaction.account.user.last_name} - Transaction_partner: {self.transaction_partner_account.user.first_name} {self.transaction_partner_account.user.last_name} - Amount: {self.transaction.amount}"
# Path: api/debit_cards/utils.py
def generate_valid_credit_card_number():
'''
Generate a random credit card number using the Luhn algorithm.
Returns:
- str: A valid 16-digit credit card number with a starting digit '5'.
'''
card_number = '5' + ''.join(str(random.randint(0, 9)) for _ in range(13))
checksum = luhn_checksum(card_number)
while checksum != 0:
card_number = '5' + ''.join(str(random.randint(0, 9)) for _ in range(13))
checksum = luhn_checksum(card_number)
return card_number
# Path: api/debit_cards/utils.py
def generate_cvv(card_number, expiration_date):
'''
Generate a simulated CVV based on the provided card number and expiration date.
Args:
- card_number (str): The card number to generate CVV from.
- expiration_date (datetime.datetime): The expiration date of the card.
Returns:
- str: The simulated CVV code.
'''
formatted_date = expiration_date.strftime('%d%m')
card_number_int = int(card_number)
masked_card_number = card_number_int >> 5
combined_data = int(f'{formatted_date}{masked_card_number}')
masked_combined_data = combined_data & card_number_int
hashed = hashlib.sha256(str(masked_combined_data).encode()).hexdigest()
cvv = []
index = 0
for char in hashed[::-5]:
index += 1
try:
int_value = int(char)
if len(cvv) < 3:
cvv.append(char)
except ValueError:
pass
return ''.join(cvv)
# Path: api/debit_cards/serializers.py
from rest_framework import serializers
from .models import DebitCard, DebitCardTransaction
from .utils import generate_valid_credit_card_number, generate_cvv
from accounts.serializers import AccountSerializer
from transactions.serializers import TransactionSerializer
class DebitCardSerializer(serializers.ModelSerializer):
card_number = serializers.CharField(read_only=True)
cvv = serializers.CharField(read_only=True)
created_date = serializers.DateTimeField(read_only=True)
account = AccountSerializer()
expiration_date = serializers.SerializerMethodField()
class Meta:
| model = DebitCard |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Mj23978/OpenServer
# Path: openserver/core/config/config.py
def get_config(self, key: str, default: Optional[str] = None) -> str | None:
return self.model_dump().get(key, default)
# Path: openserver/core/vector_store/base.py
class VectorStore(ABC):
client: Vector
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Run more documents through the embeddings and add to the vectorstore.
"""
texts: list[str] = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(texts=texts, metadatas=metadatas, **kwargs)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
return self.client.add_texts(texts=texts, metadatas=metadatas, kwargs=kwargs)
def similarity_search(self, query: str, top_k: int, metadata: Optional[dict] = None, **kwargs: Any) -> List[Document]:
return self.client.similarity_search(query=query, top_k=top_k, metadata=metadata, kwargs=kwargs)
def similarity_search_with_relevance_scores(self, query: str, top_k: int, score_threshold: float, **kwargs: Any) -> List[Tuple[Document, float]]:
return self.client.similarity_search_with_relevance_scores(query=query, top_k=top_k, score_threshold=score_threshold, kwargs=kwargs)
def delete_embeddings_from_vector_db(self, ids: List[str]) -> bool | None:
return self.client.delete(ids=ids)
# Path: openserver/core/vector_store/embedding/base.py
class BaseEmbedding(ABC):
client: Embeddings
@abstractmethod
def get_embeddings(self, text: List[str]) -> List[List[float]]:
pass
@abstractmethod
def get_embedding(self, text: str) -> List[float]:
pass
# Path: openserver/core/vector_store/qdrant.py
from mimetypes import common_types
from typing import Dict, Optional, Union
from qdrant_client import QdrantClient
from qdrant_client.conversions import common_types
from langchain.vectorstores.qdrant import Qdrant
from ..config.config import get_config
from .base import VectorStore
from .embedding.base import BaseEmbedding
from __future__ import annotations
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, common_types.Filter]
def create_qdrant_client(api_key: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = None
) -> QdrantClient:
if api_key is None:
qdrant_host_name = get_config("QDRANT_HOST_NAME") or "localhost"
qdrant_port = int(get_config("QDRANT_PORT", default="6333"))
qdrant_client = QdrantClient(host=qdrant_host_name, port=qdrant_port)
else:
qdrant_client = QdrantClient(api_key=api_key, url=url, port=port)
return qdrant_client
class QdrantVectorStore(VectorStore):
def __init__(
self,
client: QdrantClient,
collection_name: str,
| embedding_model: BaseEmbedding, |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: TCLResearchEurope/torch-dag
# Path: torch_dag_algorithms/pruning/orbits_search_stage.py
class OrbitsDiscoveryStage(enum.Enum):
EXTENDED_ORBIT_DISCOVERY = 'extended_orbits_discovery'
FINAL_ORBIT_DISCOVERY = 'final_orbits_discovery'
CLASSIC_ATTENTION_DISCOVERY = 'classic_attention_discovery'
# Path: torch_dag/core/dag_module.py
class InnerVertex(Vertex):
def __init__(
self,
name: str,
module: torch.nn.Module,
predecessors: List[Vertex],
):
super().__init__(name=name)
self._module = module
self._predecessors = list(predecessors)
self.dag_module: "DagModule" = None
self.orbit = None
@property
def successors(self) -> List['InnerVertex']:
if self.dag_module is None:
logger.error(f'Trying to get successors of an InnerVertex that has not been assigned to any DagModule.')
return [vertex for vertex in self.dag_module.inner_vertices if self in vertex.predecessors]
@property
def predecessors(self) -> List[Vertex]:
return self._predecessors
@property
def predecessor_indices(self) -> List[Vertex]:
return [self.dag_module.vertices.index(pd) for pd in self.predecessors]
@predecessors.setter
def predecessors(self, new_predecessors: List[Vertex]):
if not isinstance(new_predecessors, list):
logger.error(f'Predecessors is expected to be a list. Got {type(new_predecessors)} except.')
self._predecessors = new_predecessors
@property
def module(self) -> torch.nn.Module:
return self._module
@module.setter
def module(self, module: torch.nn.Module):
self._module = module
# TODO: Remove after validation
self.dag_module.update_inner_modules()
def config_dict(self, atomic_modules: List[torch.nn.Module]):
is_atomic = not isinstance(self.module, DagModule)
result = {
'name': self.name,
'predecessor_indices': self.predecessor_indices,
'is_atomic': is_atomic,
'type': 'inner',
'orbit': self.orbit,
}
if not is_atomic:
result['module_dict'] = self.module.config_dict(atomic_modules)
else:
result['module_index'] = atomic_modules.index(self.module)
return result
# Path: torch_dag_algorithms/pruning/orbit.py
from typing import List
from typing import Set
from typing import Tuple
from torch_dag_algorithms.pruning.orbits_search_stage import OrbitsDiscoveryStage
from torch_dag.core.dag_module import InnerVertex
class Orbit:
def __init__(self, color: int):
"""Basic orbit object that can represent either extended or final orbit. If orbit has `allow_for_further_processing` set to True then it can be processed by Orbitalizer by it's general mechanism. If set to False orbit won't be processed in any way and will be passed to orbitalization algorithm in unchanged state.
`_found_by` - indicates what stage lead to orbit being found. It's used in testing handling custom known patterns that are handled by hand. It also holds information that can be usefull durning debugging.
Args:
color (int): orbit color. has to be unique
allow_for_further_processing (bool, optional): If False orbit won't be process in any way. Defaults to True.
"""
self.color = color
self.vertices_in_scope: Set[InnerVertex] = set()
self.sources: List[InnerVertex] = []
self.sinks: List[InnerVertex] = []
self.end_path: List[Tuple[InnerVertex, InnerVertex]] = []
self.kmapps = None
self._discovery_stage = None
@property
| def discovery_stage(self) -> OrbitsDiscoveryStage: |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: repeating/Binance-P2P-alerts-Telegram-bot
# Path: bot/binance_api.py
async def get_offers(asset: str, fiat: str, trade_type: str, payment_method: str,
rows: int = 5, page: int = 1, trans_amount: str = None) -> List[dict]:
"""
Fetch the best offers from Binance P2P.
:param asset: Cryptocurrency asset, e.g., 'USDT', 'BTC'.
:param fiat: Fiat currency, e.g., 'USD', 'EUR'.
:param trade_type: Trade type, either 'Buy' or 'Sell'.
:param rows: Number of offers to retrieve, default is 5.
:param page: Page number for pagination, default is 1.
:param trans_amount: Transaction amount for filtering offers.
:param payment_method: payment type, default is "Wise".
:return: List of offers from Binance P2P.
"""
data = {
"asset": asset,
"fiat": fiat,
"merchantCheck": 'true', # Assuming this should always be true for more reliable offers.
"page": page,
"payTypes": [payment_method],
"publisherType": None, # Assuming we don't filter by publisher type.
"rows": rows,
"tradeType": trade_type,
"transAmount": trans_amount
}
headers = {
"Content-Type": "application/json"
}
async with aiohttp.ClientSession() as session:
async with session.post(BINANCE_P2P_API_URL, json=data, headers=headers) as response:
if response.status == 200:
response_json = await response.json()
offers_data = response_json.get('data', [])
offers = [{
'price': to_float(adv.get('price')),
'min_amount': to_float(adv.get('minSingleTransAmount')),
'max_amount': to_float(adv.get('maxSingleTransAmount'))
} for item in offers_data for adv in [item.get('adv', {})]]
return offers
else:
raise Exception(f"Error fetching offers from Binance P2P: {response.status} - {await response.text()}")
# Path: bot/binance_api.py
def get_link(fiat: str, asset: str, payment_method: str, order_type: str):
"""
Get the link to the offers from Binance P2P.
:param asset: Cryptocurrency asset, e.g., 'USDT', 'BTC'.
:param fiat: Fiat currency, e.g., 'USD', 'EUR'.
:param payment_method: payment type, default is "Wise".
:param order_type: Order type, either 'Buy' or 'Sell'.
:return: str, link to the offers from Binance P2P.
"""
url = f"https://p2p.binance.com/en/trade/{order_type}/{payment_method}/{asset}?fiat={fiat}"
return url
# Path: bot/utils.py
def send_telegram_message(user_id, message):
"""
Send a message to a user from a Telegram bot.
Parameters:
user_id (str): Unique identifier for the target user or username of the target channel.
message (str): Text of the message to be sent.
Returns:
dict: Response from the Telegram API.
"""
# Telegram API endpoint for sending messages
send_message_url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage"
# Parameters for the API request
params = {
'chat_id': user_id,
'text': message,
'parse_mode': 'HTML'
}
# Making the request to the Telegram API
response = requests.post(send_message_url, params=params)
# Returning the response as a Python dictionary
return response.json()
# Path: bot/alerts/alert.py
from datetime import datetime, timedelta
from bot.binance_api import get_offers, get_link
from bot.utils import send_telegram_message
class Alert:
def __init__(self, alert_id, user_id, asset, fiat, trade_type, threshold_price, payment_method):
self.alert_id = alert_id
self.user_id = user_id
self.asset = asset
self.fiat = fiat
self.trade_type = trade_type
self.threshold_price = threshold_price
self.payment_method = payment_method
self.active = True
self.last_triggered = None # Track when the alert was last triggered
self.trigger_interval = 15 # in minutes
| self.link = get_link(self.fiat, self.asset, self.payment_method, self.trade_type) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: timlrx/simple-ai-agents
# Path: simple_ai_agents/models.py
class ChatMessage(BaseModel):
role: str
content: str
name: Optional[str] = None
function_call: Optional[str] = None
received_at: datetime.datetime = Field(default_factory=now_tz)
finish_reason: Optional[str] = None
prompt_length: Optional[int] = None
completion_length: Optional[int] = None
total_length: Optional[int] = None
def __str__(self) -> str:
return str(self.model_dump_json(exclude_none=True))
# Path: simple_ai_agents/models.py
class ChatSession(BaseModel):
id: Union[str, UUID] = Field(default_factory=uuid4)
created_at: datetime.datetime = Field(default_factory=now_tz)
system: str
params: Dict[str, Any] = {}
messages: List[ChatMessage] = []
input_fields: Set[str] = set()
recent_messages: Optional[int] = None
save_messages: Optional[bool] = True
total_prompt_length: int = 0
total_completion_length: int = 0
total_length: int = 0
title: Optional[str] = None
def __str__(self) -> str:
sess_start_str = self.created_at.strftime("%Y-%m-%d %H:%M:%S")
if self.messages:
last_message_str = self.messages[-1].received_at.strftime(
"%Y-%m-%d %H:%M:%S"
)
else:
last_message_str = "N/A"
return f"""Chat session started at {sess_start_str}:
- {len(self.messages):,} Messages
- Last message sent at {last_message_str}"""
def add_messages(
self,
user_message: ChatMessage,
assistant_message: ChatMessage,
save_messages: Optional[bool] = None,
) -> None:
# if save_messages is explicitly defined, always use that choice
# instead of the default
to_save = isinstance(save_messages, bool)
if to_save:
if save_messages:
self.messages.append(user_message)
self.messages.append(assistant_message)
elif self.save_messages:
self.messages.append(user_message)
self.messages.append(assistant_message)
# Path: simple_ai_agents/models.py
class LLMOptions(TypedDict, total=False):
model: str
functions: List
function_call: str
temperature: float
top_p: float
n: int
stream: bool
stop: str
max_tokens: float
presence_penalty: float
frequency_penalty: float
logit_bias: dict
user: str
deployment_id: str
request_timeout: int
api_base: str
api_version: str
api_key: str
model_list: list
# Path: simple_ai_agents/chat_session.py
from json import JSONDecodeError
from typing import Any, AsyncGenerator, Generator, Optional, Type, TypeVar
from instructor.function_calls import Mode
from instructor.patch import handle_response_model, process_response
from litellm import ModelResponse, acompletion, completion
from pydantic import BaseModel, ValidationError
from simple_ai_agents.models import ChatMessage, ChatSession, LLMOptions
import litellm
litellm.telemetry = False
litellm.add_function_to_prompt = True # add function to prompt for non openai models
litellm.drop_params = True # drop params if unsupported by provider
litellm.suppress_debug_info = True
T = TypeVar("T", bound=BaseModel)
| class ChatLLMSession(ChatSession): |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: DIAGNijmegen/HoVer-UNet
# Path: models/HoVerNet/utils.py
def remove_small_objects(pred, min_size=64, connectivity=1):
"""Remove connected components smaller than the specified size.
This function is taken from skimage.morphology.remove_small_objects, but the warning
is removed when a single label is provided.
Args:
pred: input labelled array
min_size: minimum size of instance in output array
connectivity: The connectivity defining the neighborhood of a pixel.
Returns:
out: output array with instances removed under min_size
"""
out = pred
if min_size == 0: # shortcut for efficiency
return out
if out.dtype == bool:
selem = ndimage.generate_binary_structure(pred.ndim, connectivity)
ccs = np.zeros_like(pred, dtype=np.int32)
ndimage.label(pred, selem, output=ccs)
else:
ccs = out
try:
component_sizes = np.bincount(ccs.ravel())
except ValueError:
raise ValueError(
"Negative value labels are not supported. Try "
"relabeling the input with `scipy.ndimage.label` or "
"`skimage.morphology.label`."
)
too_small = component_sizes < min_size
too_small_mask = too_small[ccs]
out[too_small_mask] = 0
return out
# Path: models/HoVerNet/utils.py
def get_bounding_box(img):
"""Get bounding box coordinate information."""
rows = np.any(img, axis=1)
cols = np.any(img, axis=0)
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
# due to python indexing, need to add 1 to max
# else accessing will be 1px in the box, not out
rmax += 1
cmax += 1
return [rmin, rmax, cmin, cmax]
# Path: models/HoVerNet/post_proc.py
import warnings
import cv2
import numpy as np
from scipy.ndimage import measurements
from scipy.ndimage.morphology import (
binary_fill_holes,
)
from skimage.segmentation import watershed
from models.HoVerNet.utils import remove_small_objects, get_bounding_box
def noop(*args, **kargs):
pass
warnings.warn = noop
####
def __proc_np_hv(pred):
"""Process Nuclei Prediction with XY Coordinate Map.
Args:
pred: prediction output, assuming
channel 0 contain probability map of nuclei
channel 1 containing the regressed X-map
channel 2 containing the regressed Y-map
"""
pred = np.array(pred, dtype=np.float32)
blb_raw = pred[..., 0]
h_dir_raw = pred[..., 1]
v_dir_raw = pred[..., 2]
# processing
blb = np.array(blb_raw >= 0.5, dtype=np.int32)
blb = measurements.label(blb)[0]
blb = remove_small_objects(blb, min_size=10)
blb[blb > 0] = 1 # background is 0 already
h_dir = cv2.normalize(
h_dir_raw, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F
)
v_dir = cv2.normalize(
v_dir_raw, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F
)
sobelh = cv2.Sobel(h_dir, cv2.CV_64F, 1, 0, ksize=21)
sobelv = cv2.Sobel(v_dir, cv2.CV_64F, 0, 1, ksize=21)
sobelh = 1 - (
cv2.normalize(
sobelh, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F
)
)
sobelv = 1 - (
cv2.normalize(
sobelv, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F
)
)
overall = np.maximum(sobelh, sobelv)
overall = overall - (1 - blb)
overall[overall < 0] = 0
dist = (1.0 - overall) * blb
## nuclei values form mountains so inverse to get basins
dist = -cv2.GaussianBlur(dist, (3, 3), 0)
overall = np.array(overall >= 0.4, dtype=np.int32)
marker = blb - overall
marker[marker < 0] = 0
marker = binary_fill_holes(marker).astype("uint8")
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
marker = cv2.morphologyEx(marker, cv2.MORPH_OPEN, kernel)
marker = measurements.label(marker)[0]
marker = remove_small_objects(marker, min_size=10)
proced_pred = watershed(dist, markers=marker, mask=blb)
return proced_pred
####
def process(pred_map, nr_types=None, return_centroids=False):
"""Post processing script for image tiles.
Args:
pred_map: commbined output of tp, np and hv branches, in the same order
nr_types: number of types considered at output of nc branch
overlaid_img: img to overlay the predicted instances upon, `None` means no
type_colour (dict) : `None` to use random, else overlay instances of a type to colour in the dict
output_dtype: data type of output
Returns:
pred_inst: pixel-wise nuclear instance segmentation prediction
pred_type_out: pixel-wise nuclear type prediction
"""
if nr_types is not None:
pred_type = pred_map[..., :1]
pred_inst = pred_map[..., 1:]
pred_type = pred_type.astype(np.int32)
else:
pred_inst = pred_map
pred_inst = np.squeeze(pred_inst)
pred_inst = __proc_np_hv(pred_inst)
inst_info_dict = None
if return_centroids or nr_types is not None:
inst_id_list = np.unique(pred_inst)[1:] # exlcude background
inst_info_dict = {}
for inst_id in inst_id_list:
inst_map = pred_inst == inst_id
# TODO: chane format of bbox output
| rmin, rmax, cmin, cmax = get_bounding_box(inst_map) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: fofr/cog-sdxl-lcm-multi-controlnet-lora
# Path: controlnet_preprocess.py
class ControlNetPreprocessor:
ANNOTATOR_NAMES = [
"none",
"edge_canny",
"depth_leres",
"depth_midas",
"soft_edge_pidi",
"soft_edge_hed",
"lineart",
"lineart_anime",
"openpose",
# "straight_edge_mlsd",
# "face_detector",
# "content_shuffle",
# "normal_bae",
# "segementation_sam",
]
def __init__(self, predictor):
WeightsDownloader.download_if_not_exists(
CONTROLNET_PREPROCESSOR_URL, CONTROLNET_PREPROCESSOR_MODEL_CACHE
)
self.annotators = {
"edge_canny": CannyDetector(),
"depth_leres": self.initialize_detector(LeresDetector),
"depth_midas": self.initialize_detector(MidasDetector),
"soft_edge_pidi": self.initialize_detector(PidiNetDetector),
"soft_edge_hed": self.initialize_detector(HEDdetector),
"lineart": self.initialize_detector(LineartDetector),
"lineart_anime": self.initialize_detector(LineartAnimeDetector),
"openpose": self.initialize_detector(OpenposeDetector),
# "straight_edge_mlsd": self.initialize_detector(MLSDdetector),
# "face_detector": MediapipeFaceDetector(),
# "content_shuffle": ContentShuffleDetector(),
# "normal_bae": self.initialize_detector(NormalBaeDetector),
# "segementation_sam": self.initialize_detector(
# SamDetector,
# model_name="ybelkada/segment-anything",
# subfolder="checkpoints",
# ),
}
torch.device("cuda")
@staticmethod
def get_annotator_names():
return ControlNetPreprocessor.ANNOTATOR_NAMES
def initialize_detector(
self, detector_class, model_name="lllyasviel/Annotators", **kwargs
):
print(f"Initializing {detector_class.__name__}")
return detector_class.from_pretrained(
model_name,
cache_dir=CONTROLNET_PREPROCESSOR_MODEL_CACHE,
**kwargs,
)
def annotators_list(self):
return list(self.annotators.keys())
def process_image(self, image, annotator):
print(f"Processing image with {annotator}")
return self.annotators[annotator](image)
# Path: weights_downloader.py
class WeightsDownloader:
@staticmethod
def download_if_not_exists(url, dest):
if not os.path.exists(dest):
WeightsDownloader.download(url, dest)
@staticmethod
def download(url, dest):
start = time.time()
print("downloading url: ", url)
print("downloading to: ", dest)
subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
print("downloading took: ", time.time() - start)
# Path: controlnet.py
import torch
from diffusers import ControlNetModel
from controlnet_preprocess import ControlNetPreprocessor
from weights_downloader import WeightsDownloader
CONTROLNET_MODEL_CACHE = "./controlnet-cache"
CONTROLNET_URL = "https://weights.replicate.delivery/default/controlnet/sdxl-cn-canny-depth-softe-pose-qr.tar"
class ControlNet:
CONTROLNET_MODELS = [
"none",
"edge_canny",
"illusion",
"depth_leres",
"depth_midas",
"soft_edge_pidi",
"soft_edge_hed",
"lineart",
"lineart_anime",
"openpose",
# Preprocessors without an XL model yet
# "straight_edge_mlsd",
# "face_detector",
# "content_shuffle",
# "normal_bae",
# "segementation_sam",
]
def __init__(self, predictor):
| WeightsDownloader.download_if_not_exists(CONTROLNET_URL, CONTROLNET_MODEL_CACHE) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: joyn-gg/discord.http
# Path: discord_http/emoji.py
class PartialEmoji:
def __init__(self, emoji: str):
self._original_name: str = emoji
self.id: Optional[int] = None
self.animated: bool = False
self.discord_emoji: bool = False
is_custom: Optional[re.Match] = utils.re_emoji.search(emoji)
if is_custom:
_animated, _name, _id = is_custom.groups()
self.discord_emoji = True
self.animated = bool(_animated)
self.name: str = _name
self.id = int(_id)
else:
self.name: str = emoji
def __repr__(self) -> str:
if self.discord_emoji:
return f"<PartialEmoji name='{self.name}' id={self.id} animated={self.animated}>"
return f"<PartialEmoji name='{self.name}'>"
def __str__(self) -> str:
return self._original_name
def __int__(self) -> Optional[int]:
if self.discord_emoji:
return self.id
return None
@property
def url(self) -> Optional[str]:
""" `str`: Returns the URL of the emoji if it's a Discord emoji """
if self.discord_emoji:
return f"{Asset.BASE}/emojis/{self.id}.{'gif' if self.animated else 'png'}"
return None
def to_dict(self) -> dict:
""" `dict`: Returns a dict representation of the emoji """
if self.discord_emoji:
# Include animated if it's a Discord emoji
return {"id": self.id, "name": self.name, "animated": self.animated}
return {"name": self.name, "id": None}
def to_reaction(self) -> str:
""" `str`: Returns a string representation of the emoji """
if self.discord_emoji:
return f"{self.name}:{self.id}"
return self.name
# Path: discord_http/enums.py
class ButtonStyles(Enum):
primary = 1
secondary = 2
success = 3
danger = 4
link = 5
blurple = 1
grey = 2
gray = 2
green = 3
red = 4
url = 5
# Path: discord_http/enums.py
class ComponentType(Enum):
action_row = 1
button = 2
string_select = 3
text_input = 4
user_select = 5
role_select = 6
mentionable_select = 7
channel_select = 8
# Path: discord_http/enums.py
class TextStyles(Enum):
short = 1
paragraph = 2
# Path: discord_http/enums.py
class ChannelType(Enum):
guild_text = 0
dm = 1
guild_voice = 2
group_dm = 3
guild_category = 4
guild_news = 5
guild_store = 6
guild_news_thread = 10
guild_public_thread = 11
guild_private_thread = 12
guild_stage_voice = 13
guild_directory = 14
guild_forum = 15
# Path: discord_http/view.py
import asyncio
import inspect
import logging
import secrets
import time
from typing import Union, Optional, TYPE_CHECKING, Callable
from .emoji import PartialEmoji
from .enums import ButtonStyles, ComponentType, TextStyles, ChannelType
from . import Snowflake
from .channel import BaseChannel
from .context import Context
from .message import Message
from .response import BaseResponse
if TYPE_CHECKING:
_log = logging.getLogger(__name__)
__all__ = (
"Button",
"ChannelSelect",
"Item",
"Link",
"MentionableSelect",
"Modal",
"RoleSelect",
"Select",
"UserSelect",
"View",
)
def _garbage_id() -> str:
""" `str`: Returns a random ID to satisfy Discord API """
return secrets.token_hex(16)
class Item:
def __init__(self, *, type: int, row: Optional[int] = None):
self.row: Optional[int] = row
self.type: int = type
def __repr__(self) -> str:
return f"<Item type={self.type} row={self.row}>"
def to_dict(self) -> dict:
""" `dict`: Returns a dict representation of the item """
raise NotImplementedError("to_dict not implemented")
class Button(Item):
def __init__(
self,
*,
label: Optional[str] = None,
style: Union[ButtonStyles, str, int] = ButtonStyles.primary,
disabled: bool = False,
row: Optional[int] = None,
custom_id: Optional[str] = None,
emoji: Optional[Union[str, dict]] = None,
url: Optional[str] = None
):
| super().__init__(type=int(ComponentType.button), row=row) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: catid/aiwebcam2
# Path: utils.py
class ColoredFormatter(logging.Formatter):
def format(self, record):
def setup_colored_logging(level=logging.INFO):
# Path: service_asr.py
class ASRServiceRunner:
def __init__(self):
self.lock = asyncio.Lock()
self.command_queue = Queue()
self.response_queue = Queue()
self.service_process = Process(
target=run_loop,
args=(self.command_queue, self.response_queue))
self.service_process.start()
def close(self):
logger.info("Stopping ASR service...")
self.command_queue.put(('stop',))
self.service_process.join()
self.command_queue.close()
self.response_queue.close()
logger.info("ASR service stopped.")
async def Transcribe(self, pcm_data_array, channels, sample_rate):
async with self.lock:
self.command_queue.put(('transcribe', pcm_data_array, channels, sample_rate))
return await asyncio.get_running_loop().run_in_executor(None, self.response_queue.get)
# Path: service_llm.py
class LLMServiceRunner:
def __init__(self):
self.lock = asyncio.Lock()
self.command_queue = Queue()
self.response_queue = Queue()
self.service_process = Process(
target=run_loop,
args=(self.command_queue, self.response_queue))
self.service_process.start()
def close(self):
logger.info("Stopping LLM service...")
self.command_queue.put(('stop',))
self.service_process.join()
self.command_queue.close()
self.response_queue.close()
logger.info("LLM service stopped.")
async def VisionCompletionBegin(self, prompt_messages):
async with self.lock:
self.command_queue.put(('vision_completion', prompt_messages))
async def TextCompletionBegin(self, prompt_messages):
async with self.lock:
self.command_queue.put(('text_completion', prompt_messages))
# Returns None on final one
async def CompletionPoll(self):
return await asyncio.get_running_loop().run_in_executor(None, self.response_queue.get)
# Path: service_tts.py
class TTSServiceRunner:
def __init__(self):
self.lock = asyncio.Lock()
self.command_queue = multiprocessing.Queue()
self.response_queue = multiprocessing.Queue()
self.service_process = multiprocessing.Process(
target=run_loop,
args=(self.command_queue, self.response_queue))
self.service_process.start()
self.silence_duration = 0.02
self.next_pts = 0
def close(self):
logger.info("Stopping background TTS worker...")
self.command_queue.put(('stop',))
self.service_process.join()
logger.info("Closing command_queue...")
self.command_queue.close()
logger.info("Closing response_queue...")
self.response_queue.close()
logger.info("Stopped background TTS worker.")
def generate_silence_packet(self, duration_seconds):
chunk = bytes.fromhex('f8 ff fe')
packet = av.packet.Packet(chunk)
packet.pts = self.next_pts
packet.dts = self.next_pts
packet.time_base = time_base_fraction
pts_count = round(duration_seconds * time_base)
self.next_pts += pts_count
#logger.info(f"silence pts_count = {pts_count}")
return packet
# Grab either the next TTS Opus packet to play back,
# or a silence packet if no data is available.
def poll_packet(self):
try:
duration, pts_count, chunk = self.response_queue.get_nowait()
packet = av.packet.Packet(chunk)
packet.pts = self.next_pts
packet.dts = self.next_pts
packet.time_base = time_base_fraction
self.next_pts += pts_count
return packet, duration
except:
pass # Ignore Empty exception
return self.generate_silence_packet(self.silence_duration), self.silence_duration
async def Speak(self, text):
async with self.lock:
self.command_queue.put(('speak', text))
# Path: app.py
from utils import logger
from service_asr import ASRServiceRunner
from service_llm import LLMServiceRunner
from service_tts import TTSServiceRunner
from aiortc import RTCIceCandidate, RTCSessionDescription, RTCPeerConnection
from aiortc.mediastreams import AudioStreamTrack, VideoStreamTrack, MediaStreamError, MediaStreamTrack
from queue import Queue
from fractions import Fraction
from PIL import Image
from zoneinfo import ZoneInfo
import socketio
import asyncio
import re
import io
import base64
import numpy as np
import time, datetime
import aiohttp.web
import asyncio
import ssl
import argparse
# Logging
sio = socketio.AsyncServer(cors_allowed_origins='*')
# Background services
asr_runner = ASRServiceRunner()
llm_runner = LLMServiceRunner()
# WebRTC peer listening for a single browser to connect
# We run each WebRTC peer in a separate process to avoid stalls in playback
# WebRTC Connection
class VideoReceiver(VideoStreamTrack):
kind = "video"
def __init__(self, track):
super().__init__() # Initialize the MediaStreamTrack
self.track = track
self.recording = False
self.recorded_frame = None
def startRecording(self):
self.recording = True
self.recorded_frame = None
def endRecording(self):
self.recording = False
image = self.recorded_frame
self.recorded_frame = None
return image
async def recv(self):
frame = await self.track.recv()
# Process the frame (e.g., save to a file, play audio, etc.)
if self.recording:
if not self.recorded_frame:
self.recorded_frame = frame
return frame
class CustomAudioStream(MediaStreamTrack):
kind = "audio"
def __init__(self):
super().__init__() # don't forget this!
self.tts = TTSServiceRunner()
self.stream_time = None
async def close(self):
super().stop()
self.tts.close()
async def recv(self):
packet, duration = self.tts.poll_packet()
#logger.info(f"opus duration={duration} pts={packet.pts}")
if self.stream_time is None:
self.stream_time = time.time()
wait = self.stream_time - time.time()
await asyncio.sleep(wait)
self.stream_time += duration
return packet
class WebRTCConnection:
def __init__(self, sid):
self.sid = sid
self.pc = RTCPeerConnection()
self.video_track = None
self.processing_audio = False
self.recording = False
self.opus_track = CustomAudioStream()
@self.pc.on("connectionstatechange")
async def on_connectionstatechange():
| logger.info(f"self.pc.connectionState = {self.pc.connectionState}") |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: chziakas/backbone-learn
# Path: backbone_learn/backbone/backbone_decision_tree.py
class BackboneDecisionTree(BackboneSupervised):
"""
Specific implementation of the Backbone method for sparse regression.
This class combines Pearson correlation for feature screening, L0BnB for exact solving, and Lasso for heuristic solving to construct a sparse regression model.
Inherits from:
BackboneBase (ABC): The abstract base class for backbone algorithms.
"""
def set_solvers(
self,
alpha=0.5,
depth=3,
time_limit=1000,
_lambda=0.5,
num_threads=None,
obj_mode="acc",
n_bins=2,
is_data_fit=False,
):
"""
Initializes the sparse regression method with specified components.
Args:
alpha (float): Proportion of features to retain after screening. Defaults to 0.5.
depth (int, optional): Depth of BendersOCT tree. Defaults to 3.
time_limit (int): Time limit for the optimization process.
_lambda (float): Regularization parameter.
num_threads (int or None): Number of threads for parallel processing.
obj_mode (str): Objective mode, e.g., 'acc' for accuracy.
n_bins (int): Number of bins for KBinsDiscretizer. Defaults to 2.
is_data_fit (bool): Whether data are in the format required for OCT
"""
self.screen_selector = PearsonCorrelationSelector(alpha)
self.exact_solver = BendersOCTDecisionTree(
depth=depth,
time_limit=time_limit,
_lambda=_lambda,
num_threads=num_threads,
obj_mode=obj_mode,
n_bins=n_bins,
is_data_fit=is_data_fit,
)
self.heuristic_solver = CARTDecisionTree()
# Path: backbone_learn/heuristic_solvers/cart_decision_tree.py
class CARTDecisionTree(HeuristicSolverBase):
"""
Implements a Classification And Regression Tree (CART) Decision Tree with cross-validation using AUC.
This solver is a heuristic approach for fitting a decision tree model and identifying significant features.
Attributes:
_model (DecisionTreeClassifier): An instance of the sklearn DecisionTreeClassifier.
_auc_score (float): The maximum AUC score obtained during cross-validation.
"""
def __init__(self, **kwargs):
"""
Initializes the CARTDecisionTree with a DecisionTreeClassifier model.
"""
self._model = DecisionTreeClassifier()
self._auc_score = None
@property
def auc_score(self) -> float:
"""
Returns the maximum AUC score obtained from cross-validation.
Returns:
float: The maximum AUC score.
"""
return self._auc_score
def fit(self, X: np.ndarray, y: np.ndarray, cv_folds: int = 5, random_state: int = 0) -> None:
"""
Fits a CART Decision Tree model to the data using hyperparameter tuning with cross-validation and evaluates it using AUC.
Args:
X (np.ndarray): The input features as a NumPy array.
y (np.ndarray): The target labels as a NumPy array.
cv_folds (int): The number of folds to use for cross-validation.
"""
self._model.set_params(random_state=random_state)
# Define the parameter grid for hyperparameter tuning
param_grid = {"max_depth": [None, 5, 10, 20], "min_samples_leaf": [1, 2, 4]}
# Initialize GridSearchCV with the model and parameter grid
grid_search = GridSearchCV(
self._model, param_grid, cv=cv_folds, scoring="roc_auc", verbose=1
)
# Perform the grid search on the provided data
grid_search.fit(X, y)
# Update the model with the best found parameters
self._model = grid_search.best_estimator_
# Store the best AUC score
self._auc_score = grid_search.best_score_
def get_relevant_variables(self, threshold: float) -> np.ndarray:
"""
Identifies features with importance greater than a specified threshold.
Args:
threshold (float): The threshold for determining feature relevance.
Returns:
np.ndarray: An array of indices of relevant features.
"""
significant_indices = np.where(self._model.feature_importances_ > threshold)[0]
return significant_indices
def predict(self, X: np.ndarray) -> np.ndarray:
"""
Predicts the target labels for the given data.
Args:
X (np.ndarray): The input features as a NumPy array.
Returns:
np.ndarray: The predicted target labels.
"""
return self._model.predict(X)
# Path: experiments/benchmark_decision_tree.py
import time
from itertools import product
from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder
from utils import save_results
from backbone_learn.backbone.backbone_decision_tree import BackboneDecisionTree
from backbone_learn.heuristic_solvers.cart_decision_tree import CARTDecisionTree
# Define parameter ranges for Backbone parameters
alpha_range = [0.1, 0.5]
beta_range = [0.5, 0.9]
num_subproblems_range = [5, 10]
num_iterations_range = [1]
# Define parameter ranges for FlowOCT parameters
depth_range = [2]
_lambda_range = [0.5]
# Define dataset parameters
n_informative = 4
n_bins = 5
n_features_range = [20]
n_samples = 500
n_classes = 2
random_state = 17
time_limit = 3600
log_filename = "decision_tree_results.json"
results = []
# Experiment loop
for n_features in n_features_range:
# Generate synthetic classification data
X, y = make_classification(
n_samples=n_samples,
n_informative=n_informative,
n_features=n_features,
n_classes=n_classes,
random_state=random_state,
)
# Convert features to binary
est_X = KBinsDiscretizer(
n_bins=n_bins, encode="ordinal", strategy="quantile", random_state=random_state
)
est_X.fit(X)
X_bin = est_X.transform(X)
enc = OneHotEncoder(handle_unknown="error", drop="if_binary")
X_cat_enc = enc.fit_transform(X_bin).toarray()
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
X_cat_enc, y, test_size=0.2, random_state=random_state
)
for depth in depth_range:
# CARTDecisionTree model iteration for heuristic_model
| heuristic_model = CARTDecisionTree(max_depth=depth) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: openclimatefix/Open-Source-Quartz-Solar-Forecast
# Path: quartz_solar_forecast/eval/pv.py
def get_pv_truth(testset: pd.DataFrame):
print('Loading PV data')
# download from hugginface or load from cache
cache_dir = "data/pv"
metadata_file = f"{cache_dir}/pv.netcdf"
if not os.path.exists(metadata_file):
print('Loading from HF)')
os.makedirs(cache_dir, exist_ok=True)
fs.get("datasets/openclimatefix/uk_pv/pv.netcdf", metadata_file)
# Load in the dataset
pv_ds = xr.open_dataset(metadata_file, engine="h5netcdf")
combined_data = []
for index, row in testset.iterrows():
print(f'Processing {index} of {len(testset)}')
pv_id = str(row["pv_id"])
base_datetime = pd.to_datetime(row["timestamp"])
# Calculate future timestamps up to the max horizon
for i in range(0, 49): # 48 hours in steps of 1 hour
future_datetime = base_datetime + pd.DateOffset(hours=i)
horizon = i # horizon in hours
try:
# Attempt to select data for the future datetime
selected_data = pv_ds[pv_id].sel(datetime=future_datetime)
value = selected_data.values.item()
value = value / 1000 # to convert from w to kw
except KeyError:
# If data is not found for the future datetime, set value as NaN
value = np.nan
# Add the data to the DataFrame
combined_data.append(pd.DataFrame(
{"pv_id": pv_id, "timestamp": future_datetime, "value": value, "horizon_hour": horizon}, index=[i])
)
combined_data = pd.concat(combined_data)
return combined_data
# Path: quartz_solar_forecast/eval/pv.py
def get_pv_metadata(testset: pd.DataFrame):
# download from hugginface or load from cache
cache_dir = "data/pv"
metadata_file = f"{cache_dir}/metadata.csv"
if not os.path.exists(metadata_file):
os.makedirs(cache_dir, exist_ok=True)
fs.get("datasets/openclimatefix/uk_pv/metadata.csv", metadata_file)
# Load in the dataset
metadata_df = pd.read_csv(metadata_file)
# join metadata with testset
metadata_df = metadata_df.rename(columns={"ss_id": "pv_id"})
combined_data = testset.merge(metadata_df, on="pv_id", how="left")
# only keep the columns we need
combined_data = combined_data[
["pv_id", "timestamp", "latitude_rounded", "longitude_rounded", "kwp"]
]
# rename latitude_rounded to latitude and longitude_rounded to longitude
combined_data = combined_data.rename(
columns={
"latitude_rounded": "latitude",
"longitude_rounded": "longitude",
"kwp": "capacity",
}
)
# format datetime
combined_data["timestamp"] = pd.to_datetime(combined_data["timestamp"])
return combined_data
# Path: tests/eval/test_pv.py
from quartz_solar_forecast.eval.pv import get_pv_truth, get_pv_metadata
import pandas as pd
def test_get_pv_metadata():
test_set_df = pd.DataFrame(
[
{
"timestamp": pd.Timestamp("2021-01-26 01:15:00"),
"pv_id": 8215,
}
]
)
| metadata_df = get_pv_metadata(test_set_df) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: newcastleuniversity/DISPEL
# Path: dispel/providers/generic/tasks/sbt_utt/const.py
MIN_MOTION_DUR = 1
# Path: dispel/signal/core.py
def signal_duration(data: Union[pd.Series, pd.DataFrame]) -> float:
"""Get signal duration from time-based indices.
Parameters
----------
data
The signal of which we want to compute the duration based on its index. The
index has to be either a TimedeltaIndex or DatetimeIndex.
Returns
-------
float
The duration of the signal (in seconds) from the index.
"""
assert isinstance(data.index, (pd.TimedeltaIndex, pd.DatetimeIndex))
return (data.index.max() - data.index.min()).total_seconds()
# Path: dispel/signal/geometric.py
def extract_ellipse_axes(comps: pd.DataFrame) -> Tuple[float, float]:
"""Extract length of the axes of an ellipse covering 95-percentile of data.
Parameters
----------
comps
A pd.DataFrame with a 2-dimensional timeseries
Returns
-------
Union[int, int]
Tuple containing:
major_axis : float
The length of the major axis of an ellipse
minor_axis : float
The length of the minor axis of an ellipse
"""
# Extract PCA components of the 2-dimensional planar timeseries
pca = PCA(n_components=2)
pca = pca.fit(comps)
# Transform distribution to canonical cartesian axes
data_transformed = pca.transform(comps)
data_transformed_df = pd.DataFrame(data_transformed, columns=["ap", "ml"])
# Compute the min and max boundaries of 95% of data covered by the ellipse
ml_min = np.quantile(data_transformed_df.ml, 0.05)
ml_max = np.quantile(data_transformed_df.ml, 0.95)
ap_min = np.quantile(data_transformed_df.ap, 0.05)
ap_max = np.quantile(data_transformed_df.ap, 0.95)
# Compute the range of each axes (i.e., ml and ap)
rang_ml = abs(ml_max - ml_min)
rang_ap = abs(ap_max - ap_min)
# Select the minor and major axes
major_axis = max([rang_ml, rang_ap])
minor_axis = min([rang_ml, rang_ap])
return major_axis, minor_axis
# Path: dispel/signal/vectorial.py
def mean_norm_planar(comp1: pd.Series, comp2: pd.Series) -> float:
"""Compute the mean norm of a 2-dimensional timeseries.
The mean norm of a 2-dimensional timeseries is referred to as the Average
Acceleration Amplitude eq. A2 of Martinez(2012)
https://doi.org/10.1080/10255842.2011.565753
Parameters
----------
comp1
The first component of the signal
comp2
The second component of the signal
Returns
-------
float
The average value of the norm of a 2 dimensional timeseries
"""
return resultant_norm_planar(comp1, comp2).mean()
# Path: dispel/signal/vectorial.py
def resultant_norm_planar(comp1: pd.Series, comp2: pd.Series) -> pd.Series:
"""Compute the norm of the resultant of a 2-dimensional vector on a plane.
The norm of the resultant of 2-components represents the magnitude of a
2-dimensional vector.
Parameters
----------
comp1
The first component of the signal
comp2
The second component of the signal
Returns
-------
pd.Series
A series comprising the norm values of the resultant of 2-dimensional
vectorial timeseries
"""
return np.sqrt(comp1**2 + comp2**2)
# Path: dispel/signal/vectorial.py
def rms_planar(comp1: pd.Series, comp2: pd.Series) -> float:
"""Compute the RMS of a 2-dimensional timeseries.
The Root-Mean-Square of a 2-dimensional timeseries as presented in eq. A4 of
Martinez(2012) https://doi.org/10.1080/10255842.2011.565753
Parameters
----------
comp1
The first component of the signal
comp2
The second component of the signal
Returns
-------
float
The RMS value of a 2-dimensional timeseries
"""
return np.sqrt(np.mean(comp1**2 + comp2**2))
# Path: dispel/providers/generic/tasks/sbt_utt/sbt_func.py
import numpy as np
import pandas as pd
from dispel.providers.generic.tasks.sbt_utt.const import MIN_MOTION_DUR
from dispel.signal.core import signal_duration
from dispel.signal.geometric import extract_ellipse_axes
from dispel.signal.vectorial import mean_norm_planar, resultant_norm_planar, rms_planar
"""Functionality implemented in SBT.steps module."""
def label_bouts(data: pd.Series) -> pd.Series:
"""Label each valid and invalid chunk as a bout.
Parameters
----------
data
A Series that contains one column including the flag continuous
signal
Returns
-------
Series
A labelled pd.Series where each valid/invalid bout is assigned an
increasing integer number
"""
# We increase a counter number everytime the flag changes (solution
# inspired in StakOverflow community
return data.astype(bool).diff().fillna(method="bfill").cumsum()
def reject_short_bouts(bout_mask: pd.Series, flag: pd.Series) -> pd.Series:
"""Reject bouts whose duration is less than MIN_MOTION_DUR seconds.
Parameters
----------
bout_mask
A Series containing a flag_signal and a bout_number.
flag
A Series containing a flag_signal and a bout_number.
Returns
-------
Series
A Series with a flag_signal where the valence has been inverted
in case its duration is below MIN_MOTION_DUR seconds.
"""
flag = flag.astype(bool)
for _, bout in bout_mask.groupby(bout_mask):
| if signal_duration(bout) < MIN_MOTION_DUR: |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: runDMCA/home-assistant-mazda
# Path: custom_components/mazda/pymazda/sensordata/android_builds.py
class AndroidBuilds: # noqa: D101
def __init__(self): # noqa: D107
self.builds = None
def get_builds(self): # noqa: D102
if self.builds is None:
self.builds = json.loads(ANDROID_BUILDS_JSON)
return self.builds
# Path: custom_components/mazda/pymazda/sensordata/sensor_data_util.py
def percent_encode(str): # noqa: D100, D103
if str is None:
return ""
result_str = ""
for char in str.encode():
if (
char >= 33
and char <= 0x7E
and char != 34
and char != 37
and char != 39
and char != 44
and char != 92
):
result_str += chr(char)
else:
result_str += "%"
result_str += format(char, "x").upper()
return result_str
# Path: custom_components/mazda/pymazda/sensordata/sensor_data_util.py
def sum_char_codes(str): # noqa: D103
sum = 0
for char in str.encode():
if char < 0x80:
sum += char
return sum
# Path: custom_components/mazda/pymazda/sensordata/system_info.py
import random # noqa: D100
import secrets
from .android_builds import AndroidBuilds
from .sensor_data_util import percent_encode, sum_char_codes
SCREEN_SIZES = [[1280, 720], [1920, 1080], [2560, 1440]]
ANDROID_VERSION_TO_SDK_VERSION = {
"11": 30,
"10": 29,
"9": 28,
"8.1.0": 27,
"8.0.0": 26,
"7.1": 25,
"7.0": 24,
}
class SystemInfo: # noqa: D101
def __init__(self): # noqa: D107
self.android_builds = AndroidBuilds()
def randomize(self): # noqa: D102
device_model, device = random.choice(
list(self.android_builds.get_builds().items())
)
codename = device["codename"]
build = random.choice(device["builds"])
build_version_incremental = random.randrange(1000000, 9999999)
self.screen_height, self.screen_width = random.choice(SCREEN_SIZES)
self.battery_charging = random.randrange(0, 10) <= 1
self.battery_level = random.randrange(10, 90)
self.orientation = 1
self.language = "en"
self.android_version = build["version"]
self.rotation_lock = "1" if random.randrange(0, 10) > 1 else "0"
self.build_model = device_model
self.build_bootloader = str(random.randrange(1000000, 9999999))
self.build_hardware = codename
self.package_name = "com.interrait.mymazda"
self.android_id = secrets.token_bytes(8).hex()
self.keyboard = 0
self.adb_enabled = False
self.build_version_codename = "REL"
self.build_version_incremental = build_version_incremental
self.build_version_sdk = ANDROID_VERSION_TO_SDK_VERSION.get(build["version"])
self.build_manufacturer = "Google"
self.build_product = codename
self.build_tags = "release-keys"
self.build_type = "user"
self.build_user = "android-build"
self.build_display = build["buildId"]
self.build_board = codename
self.build_brand = "google"
self.build_device = codename
self.build_fingerprint = f"google/{codename}/{codename}:{build['version']}/{build['buildId']}/{build_version_incremental}:user/release-keys"
self.build_host = f"abfarm-{random.randrange(10000, 99999)}"
self.build_id = build["buildId"]
def to_string(self): # noqa: D102
return ",".join(
[
"-1",
"uaend",
"-1",
str(self.screen_height),
str(self.screen_width),
("1" if self.battery_charging else "0"),
str(self.battery_level),
str(self.orientation),
percent_encode(self.language),
percent_encode(self.android_version),
self.rotation_lock,
percent_encode(self.build_model),
percent_encode(self.build_bootloader),
percent_encode(self.build_hardware),
"-1",
self.package_name,
"-1",
"-1",
self.android_id,
"-1",
str(self.keyboard),
"1" if self.adb_enabled else "0",
percent_encode(self.build_version_codename),
percent_encode(str(self.build_version_incremental)),
str(self.build_version_sdk),
percent_encode(self.build_manufacturer),
percent_encode(self.build_product),
percent_encode(self.build_tags),
percent_encode(self.build_type),
percent_encode(self.build_user),
percent_encode(self.build_display),
percent_encode(self.build_board),
percent_encode(self.build_brand),
percent_encode(self.build_device),
percent_encode(self.build_fingerprint),
percent_encode(self.build_host),
percent_encode(self.build_id),
]
)
def get_char_code_sum(self): # noqa: D102
| return sum_char_codes(self.to_string()) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: uysalserkan/url-shorter
# Path: models/urls.py
class URLS(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
long_url: str = Field(nullable=False)
generated_url: str = Field(nullable=True)
created_date: int = datetime.utcnow().timestamp()
expire_date: int = Field(nullable=False)
@classmethod
def generate_randoms(cls):
"""Docstring."""
characters = string.ascii_letters + string.digits
return ''.join(random.choice(characters) for _ in range(10))
# Path: controller/url_c.py
class URLController:
"""Universal URL Controller."""
@classmethod
def get(cls, url_id):
"""Get URL object with id field."""
try:
url_obj = DB_engine.get(statement=select(URLS).where(URLS.id == url_id), first=True)
return url_obj
except Exception as exc:
print("ERROR:", exc)
@classmethod
def delete(cls, url_id):
"""Delete a url with id field."""
try:
url_obj = cls.get(url_id=url_id)
status = DB_engine.delete(obj=url_obj, batch=False)
if not status:
raise Exception("Did not delete.")
except Exception as exc:
print("ERROR:", exc)
# Path: config.py
# Path: engines.py
class DatabaseEngine(Singlethon):
"""Database Engine."""
def __init__(self):
sql_file_path = os.path.join(settings.DATABASE.FOLDER_PATH, settings.DATABASE.NAME)
sqlite_url = f"sqlite:///{sql_file_path}"
self.engine = create_engine(sqlite_url, echo=False)
def get(self, statement, first: bool):
"""Get elements of the sql statement."""
with Session(self.engine) as sess:
results = sess.exec(
statement=statement
).all()
return results[0] if first else results
def add(self, obj, batch: bool = False):
"""Add object."""
if not batch:
with Session(self.engine) as sess:
sess.add(obj)
sess.commit()
sess.refresh(obj)
def delete(self, obj, batch: bool = False) -> bool:
"""Delete object."""
if not batch:
with Session(self.engine) as sess:
if not obj:
return False
sess.delete(obj)
sess.commit()
return True
# Path: engines.py
class MinIOEngine(Singlethon):
"""MinIO Engine"""
def __init__(self):
self.client = Minio(
endpoint=settings.BUCKET.MINIO_SERVER,
access_key=secrets.development.MINIO_USERNAME,
secret_key=secrets.development.MINIO_PASSWORD,
secure=False
)
self.bucket_name = settings.BUCKET.MINIO_BUCKET
def add(self, file: UploadFile, short_name: str):
try:
_ = self.put_object(
bucket_name=self.bucket_name,
object_name=short_name,
data=file.file,
length=file.size,
metadata={
'filename': file.filename,
'content_type': file.content_type,
'headers': file.headers,
'size': file.size
}
)
except Exception as exc:
print("Error:", exc)
def get(self, short_name: str):
try:
return self.client.get_object(
bucket_name=self.bucket_name,
object_name=short_name
)
except Exception as exc:
print("Error", exc)
def create_bucket(self):
try:
if not self.client.bucket_exists(self.bucket_name):
self.client.make_bucket(self.bucket_name)
except Exception as exc:
print(exc)
def delete(self, short_url):
try:
self.client.remove_object(
bucket_name=self.bucket_name,
object_name=short_url
)
except Exception as exc:
print(exc)
# Path: app.py
import multiprocessing
import time
from datetime import datetime, timedelta
from fastapi import FastAPI, Response, UploadFile, Request
from fastapi.responses import RedirectResponse, JSONResponse
from sqlmodel import select
from prometheus_fastapi_instrumentator import Instrumentator
from models.urls import URLS
from controller.url_c import URLController
from config import settings, secrets
from engines import DatabaseEngine, MinIOEngine
from validators import url_validation
"""URL Shorter API."""
app = FastAPI(
title="URL Shorter Service",
description="Short your long url links.",
)
Instrumentator().instrument(app).expose(app)
| DB_engine = DatabaseEngine() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: logicalroot/gpt-4v-demos
# Path: utils.py
def show_code(code):
"""Showing the code of the demo."""
show_code = st.sidebar.checkbox("Show code", False)
if show_code:
st.markdown("## Code")
for function in code:
# Showing the code of the demo.
sourcelines, _ = inspect.getsourcelines(function)
st.code(textwrap.dedent("".join(sourcelines[0:])))
# Path: parsers.py
def extract_json(string):
"""
This function extracts the first valid JSON object from a given string.
Parameters:
string (str): The string from which to extract the JSON object.
Returns:
obj: The first valid JSON object found in the string.
Raises:
ValueError: If no valid JSON object is found in the string.
"""
start_positions = [pos for pos, char in enumerate(string) if char == "{"]
end_positions = [pos for pos, char in enumerate(string) if char == "}"]
for start in start_positions:
for end in reversed(end_positions):
if start < end:
try:
obj = json.loads(string[start : end + 1])
return json.dumps(obj, indent=4, ensure_ascii=False)
except JSONDecodeError:
continue
return "{}"
# Path: pages/3_📋_Quality_Control.py
import streamlit as st
import base64
import requests
import json
import components
from utils import show_code
from parsers import extract_json
def submit(image, api_key, issue_attributes):
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
base64_image = base64.b64encode(image).decode("utf-8")
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "system",
"content": "You are an expert quality control inspector for leading manufacturers.",
},
{
"role": "user",
"content": [
{
"type": "text",
"text": (
"Inspect this image and write a report in the following format:\n\n"
"```json\n"
"{\n"
' "issues": [\n'
" {\n"
f"{issue_attributes}\n"
" }\n"
" ]\n"
"}\n"
"```\n\n"
"If you see any signs of quality deterioration of any kind, such as corrosion, "
"physical damage, decay, or contamination, add them as separate issues in the "
"`issues` array. If there are no issues, the `issues` array should be empty. "
"Your response should contain only valid JSON."
),
},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
},
],
},
],
"max_tokens": 1024,
"temperature": 0.1,
# Response format not yet supported by GPT-4V
# "response_format": {"type": "json_object"},
}
try:
response = requests.post(
"https://api.openai.com/v1/chat/completions", headers=headers, json=payload
)
response.raise_for_status()
| text = extract_json(response.json()["choices"][0]["message"]["content"]) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: intel/llm-on-ray
# Path: inference/api_openai_backend/query_client.py
class RouterQueryClient():
def __init__(self, serve_deployments):
self.serve_deployments = serve_deployments
async def query(self, model: str, prompt: Prompt, request_id: str):
response_stream = self.stream(
model,
prompt,
request_id,
)
responses = [resp async for resp in response_stream]
return ModelResponse.merge_stream(*responses)
async def stream(
self, model: str, prompt: Prompt, request_id: str
):
if model in self.serve_deployments:
deploy_handle = self.serve_deployments[model]
else:
raise HTTPException(404, f"Could not find model with id {model}")
prompt_content = prompt.prompt
request_config = prompt.parameters
temperature = request_config.get("temperature", 1.0)
top_p = request_config.get("top_p", 1.0)
max_new_tokens = request_config.get("max_tokens", None)
gen_config = {
"max_new_tokens": max_new_tokens,
"temperature": temperature,
"top_p": top_p,
}
if temperature != 1.0 or top_p != 1.0:
gen_config.update({"do_sample": True})
async for x in handle_request(
model=model,
prompt=prompt,
request_id=request_id,
async_iterator=deploy_handle.options(stream=True).stream_response.options(stream=True, use_new_handle_api=True).remote(prompt_content, gen_config)
):
yield x
async def model(self, model_id: str) -> ModelCard:
"""Get configurations for a supported model"""
return ModelCard(
id=model_id,
root=model_id,
)
async def models(self) -> Dict[str, ModelCard]:
"""Get configurations for supported models"""
metadatas = {}
for model_id in self.serve_deployments:
metadatas[model_id] = await self.model(model_id)
return metadatas
# Path: inference/api_openai_backend/router_app.py
TIMEOUT = float(os.environ.get("ROUTER_HTTP_TIMEOUT", 600))
def init() -> FastAPI:
async def _completions_wrapper(
completion_id: str,
body: CompletionRequest,
response: Response,
generator: AsyncGenerator[ModelResponse, None],
) -> AsyncGenerator[str, None]:
async def _chat_completions_wrapper(
completion_id: str,
body: ChatCompletionRequest,
response: Response,
generator: AsyncGenerator[ModelResponse, None],
) -> AsyncGenerator[str, None]:
def __init__(
self,
query_client: RouterQueryClient,
) -> None:
async def models(self) -> ModelList:
async def model_data(self, model: str) -> ModelCard:
async def completions(
self,
body: CompletionRequest,
response: FastAPIResponse,
):
async def chat(
self,
body: ChatCompletionRequest,
response: FastAPIResponse,
):
async def health_check(self) -> bool:
class Router:
# Path: inference/api_server_openai.py
import os
from ray import serve
from inference.api_openai_backend.query_client import RouterQueryClient
from inference.api_openai_backend.router_app import Router, router_app
#
# Copyright 2023 The LLM-on-Ray Authors.
#
# 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.
#
# ===========================================================================
#
# This file is adapted from
# https://github.com/ray-project/ray-llm/blob/b3560aa55dadf6978f0de0a6f8f91002a5d2bed1/aviary/backend/server/run.py
# Copyright 2023 Anyscale
#
# 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.
#
def router_application(deployments):
"""Create a Router Deployment.
Router Deployment will point to a Serve Deployment for each specified base model,
and have a client to query each one.
"""
merged_client = RouterQueryClient(deployments)
RouterDeployment = serve.deployment(
route_prefix="/",
autoscaling_config={
"min_replicas": int(os.environ.get("ROUTER_MIN_REPLICAS", 2)),
"initial_replicas": int(os.environ.get("ROUTER_INITIAL_REPLICAS", 2)),
"max_replicas": int(os.environ.get("ROUTER_MAX_REPLICAS", 16)),
"target_num_ongoing_requests_per_replica": int(
os.environ.get("ROUTER_TARGET_NUM_ONGOING_REQUESTS_PER_REPLICA", 200)
),
},
max_concurrent_queries=1000, # Maximum backlog for a single replica
| )(serve.ingress(router_app)(Router)) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: carlhampuswall/smartknob_ha
# Path: custom_components/smartknob/const.py
DATA_REGISTRY = f"{DOMAIN}_storage"
# Path: custom_components/smartknob/const.py
SAVE_DELAY = 10
# Path: custom_components/smartknob/const.py
STORAGE_KEY = f"{DOMAIN}.storage"
# Path: custom_components/smartknob/logger.py
_LOGGER = logging.getLogger(__name__)
# Path: custom_components/smartknob/store.py
from collections import OrderedDict
from collections.abc import MutableMapping
from typing import Dict, cast
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.storage import Store
from homeassistant.loader import bind_hass
from .const import DATA_REGISTRY, SAVE_DELAY, STORAGE_KEY
from .logger import _LOGGER
import attr
@attr.s(slots=True, frozen=True)
class AppEntry:
"""App storage entry."""
app_id = attr.ib(type=str, default=None)
app_slug_id = attr.ib(type=str, default=None)
entity_id = attr.ib(type=str, default=None)
friendly_name = attr.ib(type=str, default=None)
@attr.s(slots=True, frozen=True)
class SmartknobConfig:
"""Smartknob device configuration, storage entry."""
mac_address = attr.ib(type=str, default=None)
apps = attr.ib(type=list[AppEntry], default=None)
class SmartknobStorage:
"""Class to hold Smartknob storage."""
def __init__(self, hass: HomeAssistant) -> None:
"""Initialize the Smartknob storage."""
self.hass = hass
self.config: MutableMapping[
str, str
] = {} #! ADD SMARTKNOB DEVICE SPECIFIC CONFIG HERE
self.knobs: MutableMapping[str, SmartknobConfig] = {}
self._store = Store(hass, 1, STORAGE_KEY)
async def async_load(self) -> None:
"""Load the registry of Smartknob."""
data = await self._store.async_load()
knobs: "OrderedDict[str, AppEntry]" = OrderedDict()
if data is None:
return
if "knobs" in data:
for knob in data["knobs"]:
apps = [
AppEntry(
app_id=app["app_id"],
app_slug_id=app["app_slug_id"],
entity_id=app["entity_id"],
friendly_name=app["friendly_name"],
)
for (app) in knob["apps"]
]
knobs[knob["mac_address"]] = SmartknobConfig(
mac_address=knob["mac_address"], apps=apps
)
self.knobs = knobs
# TODO ADD CHECK IF NO APPS
# if not apps:
# await self.async_factory_default()
@callback
def async_schedule_save(self) -> None:
"""Schedule saving the registry of alarmo."""
self._store.async_delay_save(self._data_to_save, SAVE_DELAY)
async def async_save(self) -> None:
"""Save the registry of Smartknob."""
await self._store.async_save(self._data_to_save())
@callback
def _data_to_save(self) -> dict:
store_data = {"knobs": [attr.asdict(entry) for entry in self.knobs.values()]}
# EXAMPLE OF ADDING MORE DATA TO STORE
# store_data["apps"] = [attr.asdict(entry) for entry in self.areas.values()]
return store_data
async def async_delete(self):
"""Delete all registry data."""
| _LOGGER.warning("Removing Smartknob configuration data!") |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: chuzhumin98/LLM_Eval
# Path: PRE/data.py
class DataLoader:
'''
The loader to load for evaluated task, with given prompt template to generate a series of prompts feeding for each LLM
'''
def __init__(self, args):
self.path_data = args['path_data'] # the load path for the data
self.format = args['format'] # the data format, csv (need a title line) or json (each line is a single data item)
self.path_prompt = args['path_prompt'] if 'path_prompt' in args else None # the path of prompt template. In the prompt template, using {{key}} for the replacement of the key. For example, in the prompt "You need answer a question: {{question}}", the "question" field need to be included in the data
if not os.path.exists(self.path_data):
raise FileExistsError("Load task data failed: file not exist!")
assert self.format in ['csv', 'json']
def generate_reader(self):
if self.format == 'csv':
with open(self.path_data, encoding='utf-8') as f:
gen = csv.DictReader(f, skipinitialspace=True)
elif self.format == 'json':
gen = open(self.path_data, encoding='utf-8')
else:
raise Exception("Invalid data format")
return gen
def get_prompt(self):
if self.path_prompt is None:
raise Exception("Exception: missing argument path_prompt")
if not os.path.exists(self.path_prompt):
raise FileExistsError("Load task prompt template failed: file not exist!")
self.template_prompt = open(self.path_prompt, encoding='utf-8').read().strip()
gen = self.generate_reader()
for row in gen:
if self.format == 'json':
item = json.loads(row.strip())
else:
item = row
prompt = self.template_prompt
for key in item:
prompt = prompt.replace("{{" + key + "}}", item[key])
yield prompt # a generator to return each prompt
def get_task_items(self):
data_list = []
gen = self.generate_reader()
for row in gen:
if self.format == 'json':
item = json.loads(row.strip())
elif self.format == 'csv':
item = dict(row)
data_list.append(item)
return data_list
# Path: PRE/api.py
class Auto_API:
@staticmethod
def instantiate_api(api_type, args) -> LLM_API:
for at, _API in API_type2class_list:
if api_type == at:
return _API(args)
raise Exception(f"Invalid api_type: {api_type}")
# Path: PRE/utils.py
def parse_response(response, parse_type, nominal_list=None, nominal_ticks=None):
'''
parse_type: int, float or str
if parse_type = str, then required parameter nominal_list and nominal_ticks
nominal_list: a series of nominal types, its name
nomianl_ticks: the corresponding nominal number (int)
'''
assert parse_type in ['int', 'float', 'str']
if parse_type == 'int':
nums = re.findall(r"-?\d+", response)
if len(nums) == 0:
return None
return int(nums[0])
elif parse_type == 'float':
nums = re.findall(r"-?\d+\.?\d*", response)
if len(nums) == 0:
return None
return int(nums[0])
elif parse_type == 'str':
appear_pos, cur_idx = math.inf, -1
response = response.lower()
for idx, label in enumerate(nominal_list):
pos = response.find(label.lower())
if pos != -1: # really appear!
if pos < appear_pos:
appear_pos, cur_idx = pos, idx
if cur_idx == -1:
return None
else:
return nominal_ticks[cur_idx]
# Path: PRE/eval.py
import os
import yaml
import warnings
import json
import copy
import sys
import numpy as np
from PRE.data import DataLoader
from PRE.api import Auto_API
from PRE.utils import parse_response
'''
The implement of the peer review and result aggregation module
'''
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(base_dir)
class PEER_REVIEW:
'''
Conduct peer review, process for one prompt (pairwise or pointwise)
'''
def __init__(self, args) -> None:
self.parser_type = args['parser_type'] # int, float, str
self.task_name = args['task_name']
self.save_dir = args['save_dir']
if self.parser_type == 'str':
self.nominal_list = [nn.strip() for nn in args['nominal_list'].split(',')]
self.nominal_ticks = [int(nn.strip()) for nn in args['nominal_ticks'].split(',')]
else:
self.nominal_list, self.nominal_ticks = None, None
def peer_review_single_round(self, reviewers, prompts):
'''
used in gaming sampling strategy
reviewers: LLM config list
prompts: an array, each item is a dict with key "prompt"
return a dict to denote the results of each evaluate task under all the reviews, key: reviewer model name, value: the original response of this reviewer
'''
| apis_reviewer = [Auto_API.instantiate_api(config_api['api_type'], config_api) for config_api in reviewers] |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: tahaafarooq/werkzeug-hash-cracker
# Path: simplifiers/simplifier.py
class SimplifierSingle(object):
def __init__(self, hasho, wordlist):
self.hasho = hasho
self.wordlist = wordlist
def crack_single_hash(self):
with open(self.wordlist, "r", encoding="latin-1") as wordlist_file:
for word in wordlist_file:
words = word.strip().split()
for line in words:
check_hash = check_password_hash(self.hasho, line)
if check_hash:
print(f"Hash: {self.hasho} Has Password {line}")
exit(0)
else:
continue
# Path: simplifiers/simplifier.py
class SimplifierFile(object):
def __init__(self, hash_file, wordlist):
self.hash_file = hash_file
self.wordlist = wordlist
self.hashes = {}
self.hashes_cracked = {}
def interprete_hash_file(self):
with open(self.hash_file, "r", encoding="latin-1") as hashs:
for hasho in hashs:
words = hasho.strip().split()
for line in words:
self.hashes[line] = True
return "Saved The Hashes"
def crack_hash_file(self):
with open(self.hash_file, "r") as hasho:
hasho = hasho.read().split()
with open(self.wordlist, "r", encoding="latin-1") as wordlist_file:
raw_words = wordlist_file.read().split()
words = Queue()
for word in raw_words:
words.put(word)
while not words.empty():
for i in range(0, len(hasho)):
password = words.get()
if check_password_hash(hasho[i], password):
print(f"Hash: {hasho[i]} Has Password {password}")
break
else:
continue
break
exit(0)
def check_results(self):
if self.hashes_cracked is not None:
return self.hashes_cracked
# Path: cracker.py
import argparse
from simplifiers.simplifier import SimplifierSingle, SimplifierFile
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Werkzeug Security Hash Cracker :: @tahaafarooq")
parser.add_argument('--single', nargs=2, metavar=('hash', 'wordlist'), help='Crack a single hash string')
parser.add_argument('--file', nargs=2, metavar=('hashfile', 'wordlist'), help='Crack a file with multiple hashes')
parser.add_argument('--about', action='store_true', help='Print core information about the script and developer')
args = parser.parse_args()
if args.about:
about = """
Werkzeug Hash Cracker: Is a minimal script that cracks hashes which are generated from werkzeug.security library in python\n
About Developer: Tahaa Farooq is a cybersecurity professional with a passion in programming. Check his github for more information (https://github.com/tahaafarooq)"""
print(about)
elif args.single:
hash_string, wordlist_file = args.single
simple_crack = SimplifierSingle(hash_string, wordlist_file)
simple_crack.crack_single_hash()
elif args.file:
hash_file, wordlist_file = args.file
| simple_crack = SimplifierFile(hash_file, wordlist_file) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: victor0089/AirBnB_clone_v2
# Path: models/base_model.py
class BaseModel:
def __init__(self, *args, **kwargs):
def __str__(self):
def __repr__(self):
def save(self):
def to_dict(self):
def delete(self):
# Path: models/state.py
class State(BaseModel, Base):
"""This is the class for State
Attributes:
name: input name
"""
__tablename__ = "states"
name = Column(String(128), nullable=False)
cities = relationship("City", cascade='all, delete, delete-orphan',
backref="state")
@property
def cities(self):
var = models.storage.all()
lista = []
result = []
for key in var:
city = key.replace('.', ' ')
city = shlex.split(city)
if (city[0] == 'City'):
lista.append(var[key])
for elem in lista:
if (elem.state_id == self.id):
result.append(elem)
return (result)
# Path: models/city.py
class City(BaseModel, Base):
"""This is the class for City
Attributes:
state_id: The state id
name: input name
"""
__tablename__ = "cities"
name = Column(String(128), nullable=False)
state_id = Column(String(60), ForeignKey('states.id'), nullable=False)
places = relationship("Place", cascade='all, delete, delete-orphan',
backref="cities")
# Path: models/user.py
class User(BaseModel, Base):
"""This is the class for user
Attributes:
email: email address
password: password for you login
first_name: first name
last_name: last name
"""
__tablename__ = "users"
email = Column(String(128), nullable=False)
password = Column(String(128), nullable=False)
first_name = Column(String(128))
last_name = Column(String(128))
places = relationship("Place", cascade='all, delete, delete-orphan',
backref="user")
reviews = relationship("Review", cascade='all, delete, delete-orphan',
backref="user")
# Path: models/place.py
class Place(BaseModel, Base):
"""This is the class for Place
Attributes:
city_id: city id
user_id: user id
name: name input
description: string of description
number_rooms: number of room in int
number_bathrooms: number of bathrooms in int
max_guest: maximum guest in int
price_by_night:: pice for a staying in int
latitude: latitude in flaot
longitude: longitude in float
amenity_ids: list of Amenity ids
"""
__tablename__ = "places"
city_id = Column(String(60), ForeignKey("cities.id"), nullable=False)
user_id = Column(String(60), ForeignKey("users.id"), nullable=False)
name = Column(String(128), nullable=False)
description = Column(String(1024))
number_rooms = Column(Integer, nullable=False, default=0)
number_bathrooms = Column(Integer, nullable=False, default=0)
max_guest = Column(Integer, nullable=False, default=0)
price_by_night = Column(Integer, nullable=False, default=0)
latitude = Column(Float)
longitude = Column(Float)
amenity_ids = []
if getenv("HBNB_TYPE_STORAGE") == "db":
reviews = relationship("Review", cascade='all, delete, delete-orphan',
backref="place")
amenities = relationship("Amenity", secondary=place_amenity,
viewonly=False,
back_populates="place_amenities")
else:
@property
def reviews(self):
""" Returns list of reviews.id """
var = models.storage.all()
lista = []
result = []
for key in var:
review = key.replace('.', ' ')
review = shlex.split(review)
if (review[0] == 'Review'):
lista.append(var[key])
for elem in lista:
if (elem.place_id == self.id):
result.append(elem)
return (result)
@property
def amenities(self):
""" Returns list of amenity ids """
return self.amenity_ids
@amenities.setter
def amenities(self, obj=None):
""" Appends amenity ids to the attribute """
if type(obj) is Amenity and obj.id not in self.amenity_ids:
self.amenity_ids.append(obj.id)
# Path: models/review.py
class Review(BaseModel, Base):
"""This is the class for Review
Attributes:
place_id: place id
user_id: user id
text: review description
"""
__tablename__ = "reviews"
text = Column(String(1024), nullable=False)
place_id = Column(String(60), ForeignKey("places.id"), nullable=False)
user_id = Column(String(60), ForeignKey("users.id"), nullable=False)
# Path: models/amenity.py
class Amenity(BaseModel, Base):
"""This is the class for Amenity
Attributes:
name: input name
"""
__tablename__ = "amenities"
name = Column(String(128), nullable=False)
place_amenities = relationship("Place", secondary=place_amenity)
# Path: models/engine/db_storage.py
from os import getenv
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy import (create_engine)
from sqlalchemy.ext.declarative import declarative_base
from models.base_model import Base
from models.state import State
from models.city import City
from models.user import User
from models.place import Place
from models.review import Review
from models.amenity import Amenity
#!/usr/bin/python3
""" new class for sqlAlchemy """
class DBStorage:
""" create tables in environmental"""
__engine = None
__session = None
def __init__(self):
'''instantiate new dbstorage instance'''
HBNB_MYSQL_USER = getenv('HBNB_MYSQL_USER')
HBNB_MYSQL_PWD = getenv('HBNB_MYSQL_PWD')
HBNB_MYSQL_HOST = getenv('HBNB_MYSQL_HOST')
HBNB_MYSQL_DB = getenv('HBNB_MYSQL_DB')
HBNB_ENV = getenv('HBNB_ENV')
self.__engine = create_engine(
'mysql+mysqldb://{}:{}@{}/{}'.format(
HBNB_MYSQL_USER,
HBNB_MYSQL_PWD,
HBNB_MYSQL_HOST,
HBNB_MYSQL_DB
), pool_pre_ping=True)
if HBNB_ENV == 'test':
| Base.metadata.drop_all(self.__engine) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: believethehype/nostrdvm
# Path: nostr_dvm/tasks/textgeneration_llmlite.py
class TextGenerationLLMLite(DVMTaskInterface):
KIND: int = EventDefinitions.KIND_NIP90_GENERATE_TEXT
TASK: str = "text-to-text"
FIX_COST: float = 0
dependencies = [("nostr-dvm", "nostr-dvm"),
("litellm", "litellm==1.12.3")]
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
admin_config: AdminConfig = None, options=None):
dvm_config.SCRIPT = os.path.abspath(__file__)
super().__init__(name, dvm_config, nip89config, admin_config, options)
def is_input_supported(self, tags, client=None, dvm_config=None):
for tag in tags:
if tag.as_vec()[0] == 'i':
input_value = tag.as_vec()[1]
input_type = tag.as_vec()[2]
if input_type != "text":
return False
return True
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
prompt = ""
if self.options.get("default_model") and self.options.get("default_model") != "":
model = self.options['default_model']
else:
model = "gpt-3.5-turbo" # "gpt-4-1106-preview" # This will call chatgpt and requires an OpenAI API Key set in .env
if self.options.get("server") and self.options.get("server") != "":
server = self.options['server']
else:
server = "http://localhost:11434" # default ollama server. This will only be used for ollama models.
for tag in event.tags():
if tag.as_vec()[0] == 'i':
input_type = tag.as_vec()[2]
if input_type == "text":
prompt = tag.as_vec()[1]
options = {
"prompt": prompt,
"model": model,
"server": server
}
request_form['options'] = json.dumps(options)
return request_form
def process(self, request_form):
from litellm import completion
options = DVMTaskInterface.set_options(request_form)
try:
if options["model"].startswith("ollama"):
response = completion(
model=options["model"],
messages=[{"content": options["prompt"], "role": "user"}],
api_base=options["server"],
stream=False
)
print(response.choices[0].message.content)
return response.choices[0].message.content
else:
response = completion(
model=options["model"],
messages=[{"content": options["prompt"], "role": "user"}],
)
print(response.choices[0].message.content)
return response.choices[0].message.content
except Exception as e:
print("Error in Module: " + str(e))
raise Exception(e)
# Path: nostr_dvm/utils/admin_utils.py
class AdminConfig:
REBROADCAST_NIP89: bool = False
UPDATE_PROFILE: bool = False
DELETE_NIP89: bool = False
WHITELISTUSER: bool = False
UNWHITELISTUSER: bool = False
BLACKLISTUSER: bool = False
DELETEUSER: bool = False
LISTDATABASE: bool = False
ClEANDB: bool = False
USERNPUB: str = ""
LUD16: str = ""
EVENTID: str = ""
PRIVKEY: str = ""
# Path: nostr_dvm/utils/dvmconfig.py
def build_default_config(identifier):
dvm_config = DVMConfig()
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
dvm_config.IDENTIFIER = identifier
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
dvm_config.LNBITS_INVOICE_KEY = invoice_key
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
dvm_config.LN_ADDRESS = lnaddress
return dvm_config
# Path: nostr_dvm/utils/nip89_utils.py
class NIP89Config:
DTAG: str = ""
NAME: str = ""
KIND: int = None
PK: str = ""
CONTENT: str = ""
# Path: nostr_dvm/utils/nip89_utils.py
def check_and_set_d_tag(identifier, name, pk, imageurl):
if not os.getenv("NIP89_DTAG_" + identifier.upper()):
new_dtag = nip89_create_d_tag(name, Keys.from_sk_str(pk).public_key().to_hex(),
imageurl)
nip89_add_dtag_to_env_file("NIP89_DTAG_" + identifier.upper(), new_dtag)
print("Some new dtag:" + new_dtag)
return new_dtag
else:
return os.getenv("NIP89_DTAG_" + identifier.upper())
# Path: examples/ollama_dvm/main.py
import json
import dotenv
from pathlib import Path
from nostr_dvm.tasks.textgeneration_llmlite import TextGenerationLLMLite
from nostr_dvm.utils.admin_utils import AdminConfig
from nostr_dvm.utils.dvmconfig import build_default_config
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
def main():
identifier = "llama2"
name = "Ollama"
| dvm_config = build_default_config(identifier) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: zouXH-god/meme_web
# Path: meme_generator/config.py
class MemeConfig(BaseModel):
class ResourceConfig(BaseModel):
class GifConfig(BaseModel):
class TranslatorConfig(BaseModel):
class ServerConfig(BaseModel):
class LogConfig(BaseModel):
class Config(BaseModel, extra=Extra.ignore):
def load(cls) -> "Config":
def dump(self):
# Path: meme_generator/exception.py
class NoSuchMeme(MemeGeneratorException):
status_code: int = 531
def __init__(self, meme_key: str):
self.meme_key = meme_key
message = f'No such meme with key="{self.meme_key}"'
super().__init__(message)
# Path: meme_generator/log.py
class LoguruHandler(logging.Handler):
def emit(self, record: logging.LogRecord):
def setup_logger():
def default_filter(record: "Record"):
LOGGING_CONFIG = {
"version": 1,
"disable_existing_loggers": False,
"handlers": {
"default": {
"class": "meme_generator.log.LoguruHandler",
},
},
"loggers": {
"uvicorn.error": {"handlers": ["default"], "level": "INFO"},
"uvicorn.access": {
"handlers": ["default"],
"level": "INFO",
},
},
}
# Path: meme_generator/meme.py
class UserInfo(BaseModel):
class MemeArgsModel(BaseModel):
class MemeArgsParser(ArgumentParser):
class MemeArgsType:
class MemeParamsType:
class Meme:
def _print_message(self, message: str, file: Optional[IO[str]] = None):
def exit(self, status: int = 0, message: Optional[str] = None):
async def __call__(
self,
*,
images: Union[List[str], List[Path], List[bytes], List[BytesIO]] = [],
texts: List[str] = [],
args: Dict[str, Any] = {},
) -> BytesIO:
def parse_args(self, args: List[str] = []) -> Dict[str, Any]:
async def generate_preview(self, *, args: Dict[str, Any] = {}) -> BytesIO:
async def _generate_preview(images: List[BytesIO], texts: List[str]):
# Path: meme_generator/manager.py
import importlib
import importlib.util
import pkgutil
from pathlib import Path
from typing import Dict, List, Optional, Union
from .config import meme_config
from .exception import NoSuchMeme
from .log import logger
from .meme import Meme, MemeArgsType, MemeFunction, MemeParamsType
_memes: Dict[str, Meme] = {}
def path_to_module_name(path: Path) -> str:
rel_path = path.resolve().relative_to(Path.cwd().resolve())
if rel_path.stem == "__init__":
return ".".join(rel_path.parts[:-1])
else:
return ".".join(rel_path.parts[:-1] + (rel_path.stem,))
def load_meme(module_path: Union[str, Path]):
module_name = (
path_to_module_name(module_path)
if isinstance(module_path, Path)
else module_path
)
try:
importlib.import_module(module_name)
except Exception as e:
logger.opt(colors=True, exception=e).error(f"Failed to import {module_path}!")
def load_memes(dir_path: Union[str, Path]):
if isinstance(dir_path, Path):
dir_path = str(dir_path.resolve())
for module_info in pkgutil.iter_modules([dir_path]):
if module_info.name.startswith("_"):
continue
if not (
module_spec := module_info.module_finder.find_spec(module_info.name, None)
):
continue
if not (module_path := module_spec.origin):
continue
if not (module_loader := module_spec.loader):
continue
try:
module = importlib.util.module_from_spec(module_spec)
module_loader.exec_module(module)
except Exception as e:
logger.opt(colors=True, exception=e).error(
f"Failed to import {module_path}!"
)
def add_meme(
key: str,
| function: MemeFunction, |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: embrake/Aquilify
# Path: aquilify/types.py
T = typing.TypeVar("T")
# Path: aquilify/responses.py
class JsonResponse(BaseResponse):
def __init__(
self,
content: Union[Dict, Callable, None] = {},
status: Optional[int] = 200,
headers: Optional[Dict[str, Union[str, int]]] = None,
content_type: str = 'application/json',
encoding: Optional[str] = 'utf-8',
validate: Optional[bool] = False,
) -> None:
"""
Create a JSON response.
Args:
content (Union[Dict, Callable, None]): The response content (as a dictionary).
status (Optional[int]): The HTTP status code (default is 200).
headers (Optional[Dict[str, Union[str, int]]]): Additional headers for the response.
content_type (str): The content type for the response (default is 'application/json').
encoding (Optional[str]): The character encoding for JSON content (default is 'utf-8').
validate (Optional[bool]): Whether to validate the JSON data (default is False).
"""
if validate:
try:
json.dumps(content)
except ValueError:
raise ValueError("Invalid JSON content")
super().__init__(json.dumps(content, ensure_ascii=False), status, headers)
self.headers.setdefault('Content-Type', f'{content_type}; charset={encoding}')
# Path: aquilify/middlewares/dispatcher.py
import logging
from typing import Awaitable, Callable, Dict, Optional, Union
from ..types import ASGIApp, Receive, Scope, Send
from ..responses import JsonResponse
class Dispatcher:
"""
Dispatches incoming requests to different mounted ASGI apps based on the URL path.
Usage:
```python
# Create the main application
main_app = Aquilify()
# Create instances of the mounted apps
app1 = Aquilify()
app2 = Aquilify()
# Create the Dispatcher instance
dispatcher = Dispatcher(main_app, {})
# Map app1 to /app1 and app2 to /app2
dispatcher.map_url('/app1', app1)
dispatcher.map_url('/app2', app2)
# Define error handlers if necessary
async def error_handler1(scope, receive, send, exc):
# Custom error handling logic for app1
pass
async def error_handler2(scope, receive, send, exc):
# Custom error handling logic for app2
pass
dispatcher.map_url('/app1', app1, error_handler1)
dispatcher.map_url('/app2', app2, error_handler2)
# Run the dispatcher
@app.route("/")
async def homepage(request):
return JsonResponse({"message": "Hello, world!"})
@app.route("/app1")
async def app1_homepage(request):
return JSONResponse({"message": "App 1 homepage"})
@app.route("/app2")
async def app2_homepage(request):
return JSONResponse({"message": "App 2 homepage"})
```
"""
def __init__(self, main_app: ASGIApp, mounts: Dict[str, ASGIApp]) -> None:
"""
Initializes the Dispatcher instance.
Args:
main_app (ASGIApp): The main ASGI app to handle the requests.
mounts (Dict[str, ASGIApp]): A dictionary containing mounted apps.
Usage:
```python
main_app = Aquilify() # create a main app instance
app2 = Aquilify() #sub app for mounting in main_app
dispatcher = Dispatcher(main_app, {
'/app2': app2
})
Run:
$ netix --debug main:dispatcher
---------- or -----------
$ uvicorn main:dispatcher
"""
self.main_app: ASGIApp = main_app
self.mounts: Dict[str, ASGIApp] = mounts
self.error_handlers: Dict[str, Optional[Callable[..., Awaitable[None]]]] = {
mount_point: None for mount_point in mounts
}
self.logger = logging.getLogger(__name__)
def map_url(self, mount_point: str, app: ASGIApp,
error_handler: Optional[Callable[..., Awaitable[None]]] = None) -> None:
"""
Maps a URL mount point to a specified ASGI app.
Args:
mount_point (str): The URL mount point.
app (ASGIApp): The ASGI app to mount at the specified point.
error_handler (Optional[Callable[..., Awaitable[None]]]): Error handler for this mounted app.
"""
self.mounts[mount_point] = app
self.error_handlers[mount_point] = error_handler
def unmap_url(self, mount_point: str) -> None:
"""
Unmaps a URL mount point, removing the mounted app.
Args:
mount_point (str): The URL mount point to unmap.
"""
if mount_point in self.mounts:
del self.mounts[mount_point]
del self.error_handlers[mount_point]
async def conditional_mount(self, mount_point: str, app: ASGIApp,
condition: Union[Callable, Awaitable[bool]],
error_handler: Optional[Callable[..., Awaitable[None]]] = None) -> None:
"""
Mounts an ASGI app based on a specified condition.
Args:
mount_point (str): The URL mount point.
app (ASGIApp): The ASGI app to mount at the specified point.
condition (Union[Callable, Awaitable[bool]]): Condition to decide the mounting.
error_handler (Optional[Callable[..., Awaitable[None]]]): Error handler for this mounted app.
"""
if callable(condition):
condition = await condition()
if condition:
self.mounts[mount_point] = app
self.error_handlers[mount_point] = error_handler
| async def dispatch(self, scope: Scope, receive: Receive, send: Send) -> None: |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Viicos/django-autotyping
# Path: src/django_autotyping/_compat.py
def is_relative_to(path: Path, other: Path) -> bool:
# Path: src/django_autotyping/typing.py
class AutotypingSettingsDict(TypedDict, total=False):
class StubsGenerationSettingsDict(TypedDict, total=False):
class CodeGenerationSettingsDict(TypedDict, total=False):
IGNORE: list[RulesT]
STUBS_GENERATION: StubsGenerationSettingsDict
CODE_GENERATION: CodeGenerationSettingsDict
LOCAL_STUBS_DIR: Path | None
SOURCE_STUBS_DIR: Path | None
ALLOW_PLAIN_MODEL_REFERENCES: bool
ALLOW_NONE_SET_TYPE: bool
MODEL_FIELDS_OPTIONAL: bool
ALLOW_REVERSE_ARGS: bool
PROJECT_DIR: Path | None
DIFF: bool
TYPE_CHECKING_BLOCK: bool
ASSUME_CLASS_GETITEM: bool
# Path: src/django_autotyping/app_settings.py
from copy import deepcopy
from dataclasses import dataclass, field
from pathlib import Path
from django.conf import LazySettings
from ._compat import Self
from .typing import AutotypingSettingsDict, RulesT
from __future__ import annotations
@dataclass
class CodeGenerationSettings:
"""Configuration for adding type annotations to Django user code."""
PROJECT_DIR: Path | None = None
"""The directory of the project, where code modifications should be applied."""
DIFF: bool = False
"""Show changes to be applied instead of modifying existing files."""
TYPE_CHECKING_BLOCK: bool = True
"""Whether newly added imports should be in an `if TYPE_CHECKING` block (avoids circular imports)."""
ASSUME_CLASS_GETITEM: bool = False
"""Whether generic classes in stubs files but not at runtime should be assumed to have a
`__class_getitem__` method. This can be achieved by using `django-stubs-ext` or manually.
Affected rules: `DJA001`.
"""
@dataclass
class StubsGenerationSettings:
"""Configuration for dynamic stubs generation."""
LOCAL_STUBS_DIR: Path | None = None
"""The directory of the local type stubs. If not set, this setting must be set as a CLI argument."""
SOURCE_STUBS_DIR: Path | None = None
"""The directory of the source `django-stubs` to be used. Will default
to the first entry in site packages.
"""
ALLOW_PLAIN_MODEL_REFERENCES: bool = True
"""Whether string references in the form of `{model_name}` should be generated in overloads.
If set to `True`, both `{model_name}` and `{model_name}.{app_label}` are allowed
(unless the model name has a duplicate in a different app).
Affected rules: `DJAS001`.
"""
ALLOW_NONE_SET_TYPE: bool = False
"""Whether to allow having the `__set__` type variable set to `None`, even if the field is not nullable.
While Django allows setting most model instance fields to any value (before saving),
it is generally a bad practice to do so. However, it might be beneficial to allow `None`
to be set temporarly.
This also works for foreign fields, where unlike standard fields, the Django descriptor used
only allows model instances and `None` to be set.
Affected rules: `DJAS001`.
"""
MODEL_FIELDS_OPTIONAL: bool = True
"""Whether all model fields should be considered optional when creating model instances.
This affects the following signatures:
- [`Manager.create/acreate`][django.db.models.Manager]
- `__init__` methods of models
A lot can happen behind the scenes when instantiating models. Even if a field doesn't have
a default value provided, the database could have triggers implemented that would provide one.
This is why, by default, this configuration attribute defaults to `True`. If set to `False`,
`django-autotyping` will try its best to determine required fields, namely by checking if:
- the field can be [`null`][django.db.models.Field.null]
- the field has a default or a database default value set
- the field is a subclass of [`DateField`][django.db.models.DateField] and has
[`auto_now`][django.db.models.DateField.auto_now] or [`auto_now_add`][django.db.models.DateField.auto_now_add]
set to `True`.
Affected rules: `DJAS002`.
"""
ALLOW_REVERSE_ARGS: bool = False
"""Whether type checking should be added to the `args` argument of [`reverse`][django.urls.reverse].
By default, this is set to `False` to avoid having too many overloads being generated.
Moreover, only tuples can be type checked, and most people are using lists for this argument.
Instead, it is recommended to use the `kwargs` argument.
Affected rules: `DJAS011`.
"""
@dataclass
class AutotypingSettings:
"""A class holding the django-autotyping configuration."""
IGNORE: list[RulesT] = field(default_factory=list)
"""A list of ignored rules."""
STUBS_GENERATION: StubsGenerationSettings = field(default_factory=StubsGenerationSettings)
"""Stub related settings."""
CODE_GENERATION: CodeGenerationSettings = field(default_factory=CodeGenerationSettings)
"""Code generation related settings."""
@classmethod
| def from_django_settings(cls, settings: LazySettings) -> Self: |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: IBM/oper8
# Path: oper8/setup_vcs.py
DEFAULT_DEST = "oper8_vcs"
# Path: oper8/setup_vcs.py
DEFAULT_TAG_EXPR = r"[0-9]+\.[0-9]+\.[0-9]+"
# Path: oper8/setup_vcs.py
def setup_vcs(
source: str,
destination: Optional[str] = None,
branch_expr: Optional[List[str]] = None,
tag_expr: Optional[List[str]] = __UNSET__,
force: bool = False,
):
"""This utility will initialize an operator's VCS directory for use with
oper8's VCS versioning.
Args:
source (str): The path to the source repository on disk
destination (Optional[str]): The path where the VCS repo should be
created
branch_expr (Optional[List[str]]): Regular expression(s) to use to
identify branches to retain in the VCS repo
tag_expr (Optional[List[str]]): Regular expression(s) to use to
identify tags to retain in the VCS repo
force (bool): Force overwrite existing destination
"""
initializer = VCSRepoInitializer(
source=source, destination=destination or DEFAULT_DEST, force=force
)
initializer.initialize_branches(
branch_expr=branch_expr,
tag_expr=tag_expr if tag_expr is not __UNSET__ else [DEFAULT_TAG_EXPR],
)
initializer.clean_up()
# Path: oper8/cmd/base.py
class CmdBase(abc.ABC):
__doc__ = __doc__
@abc.abstractmethod
def add_subparser(
self,
subparsers: argparse._SubParsersAction,
) -> argparse.ArgumentParser:
"""Add this command's argument parser subcommand
Args:
subparsers (argparse._SubParsersAction): The subparser section for
the central main parser
Returns:
subparser (argparse.ArgumentParser): The configured parser for this
command
"""
@abc.abstractmethod
def cmd(self, args: argparse.Namespace):
"""Execute the command with the parsed arguments
Args:
args (argparse.Namespace): The parsed command line arguments
"""
# Path: oper8/cmd/setup_vcs_cmd.py
import argparse
import alog
from ..setup_vcs import DEFAULT_DEST, DEFAULT_TAG_EXPR, setup_vcs
from .base import CmdBase
"""
CLI command for setting up a VCS version repo
"""
# Standard
# First Party
# Local
log = alog.use_channel("CMD-VCS")
class SetupVCSCmd(CmdBase):
__doc__ = __doc__
def add_subparser(
self,
subparsers: argparse._SubParsersAction,
) -> argparse.ArgumentParser:
"""Add the subparser for this command"""
parser = subparsers.add_parser(
"setup-vcs",
help="Initialize a clean git repo to use with VCS versioning",
)
command_args = parser.add_argument_group("Command Arguments")
command_args.add_argument(
"--source",
"-s",
required=True,
help="Source repo to seed the clean git history",
)
command_args.add_argument(
"--destination",
"-d",
| default=DEFAULT_DEST, |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ariebovenberg/whenever
# Path: tests/common.py
class AlwaysEqual:
def __eq__(self, other):
return True
# Path: tests/common.py
class AlwaysLarger:
def __lt__(self, other):
return False
def __le__(self, other):
return False
def __gt__(self, other):
return True
def __ge__(self, other):
return True
# Path: tests/common.py
class AlwaysSmaller:
def __lt__(self, other):
return True
def __le__(self, other):
return True
def __gt__(self, other):
return False
def __ge__(self, other):
return False
# Path: tests/common.py
class NeverEqual:
def __eq__(self, other):
return False
# Path: tests/common.py
@contextmanager
def local_ams_tz():
with patch.dict(os.environ, {"TZ": "Europe/Amsterdam"}):
tzset()
yield
# Path: tests/test_naive_datetime.py
import pickle
import weakref
import pytest
from datetime import datetime as py_datetime
from datetime import timedelta, timezone
from hypothesis import given
from hypothesis.strategies import text
from whenever import InvalidFormat, NaiveDateTime
from .common import (
AlwaysEqual,
AlwaysLarger,
AlwaysSmaller,
NeverEqual,
local_ams_tz,
)
def test_minimal():
d = NaiveDateTime(2020, 8, 15, 5, 12, 30, 450)
assert d.year == 2020
assert d.month == 8
assert d.day == 15
assert d.hour == 5
assert d.minute == 12
assert d.second == 30
assert d.microsecond == 450
assert (
NaiveDateTime(2020, 8, 15, 12)
== NaiveDateTime(2020, 8, 15, 12, 0)
== NaiveDateTime(2020, 8, 15, 12, 0, 0)
== NaiveDateTime(2020, 8, 15, 12, 0, 0, 0)
)
def test_immutable():
d = NaiveDateTime(2020, 8, 15)
with pytest.raises(AttributeError):
d.year = 2021 # type: ignore[misc]
class TestFromCanonicalStr:
def test_valid(self):
assert NaiveDateTime.from_canonical_str(
"2020-08-15T12:08:30"
) == NaiveDateTime(2020, 8, 15, 12, 8, 30)
def test_valid_three_fractions(self):
assert NaiveDateTime.from_canonical_str(
"2020-08-15T12:08:30.349"
) == NaiveDateTime(2020, 8, 15, 12, 8, 30, 349_000)
def test_valid_six_fractions(self):
assert NaiveDateTime.from_canonical_str(
"2020-08-15T12:08:30.349123"
) == NaiveDateTime(2020, 8, 15, 12, 8, 30, 349_123)
def test_single_space_instead_of_T(self):
assert NaiveDateTime.from_canonical_str(
"2020-08-15 12:08:30"
) == NaiveDateTime(2020, 8, 15, 12, 8, 30)
def test_unpadded(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str("2020-8-15T12:8:30")
def test_overly_precise_fraction(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str(
"2020-08-15T12:08:30.123456789123"
)
def test_trailing_z(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str("2020-08-15T12:08:30Z")
def test_no_seconds(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str("2020-08-15T12:08")
def test_empty(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str("")
def test_garbage(self):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str("garbage")
@given(text())
def test_fuzzing(self, s: str):
with pytest.raises(InvalidFormat):
NaiveDateTime.from_canonical_str(s)
def test_equality():
d = NaiveDateTime(2020, 8, 15)
different = NaiveDateTime(2020, 8, 16)
same = NaiveDateTime(2020, 8, 15)
assert d == same
assert d != different
assert not d == different
assert not d != same
assert hash(d) == hash(same)
assert hash(d) != hash(different)
| assert d == AlwaysEqual() |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: DataWizual/Raycasting
# Path: ray_casting.py
def ray_casting(sc, player_pos, player_angle):
ox, oy = player_pos
xm, ym = mapping(ox, oy)
cur_angle = player_angle - HALF_FOV
for ray in range(NUM_RAYS):
sin_a = math.sin(cur_angle)
cos_a = math.cos(cur_angle)
sin_a = sin_a if sin_a else 0.000001
cos_a = cos_a if cos_a else 0.000001
# verticals
x, dx = (xm + TILE, 1) if cos_a >= 0 else (xm, -1)
for i in range(0, WIDTH, TILE):
depth_v = (x - ox) / cos_a
y = oy + depth_v * sin_a
if mapping(x + dx, y) in world_map:
break
x += dx * TILE
# horizontals
y, dy = (ym + TILE, 1) if sin_a >= 0 else (ym, -1)
for i in range(0, HEIGHT, TILE):
depth_h = (y - oy) / sin_a
x = ox + depth_h * cos_a
if mapping(x, y + dy) in world_map:
break
y += dy * TILE
# projection
depth = depth_v if depth_v < depth_h else depth_h
depth *= math.cos(player_angle - cur_angle)
proj_height = PROJ_COEFF / depth
c = 255 / (1 + depth * depth * 0.00002)
color = (63 + c // 2, 63 + c // 2, 63 + c // 2)
pygame.draw.rect(sc, color, (ray * SCALE, HALF_HEIGHT - proj_height // 2, SCALE, proj_height))
cur_angle += DELTA_ANGLE
# Path: map.py
# Path: drawing.py
import pygame
from settings import *
from ray_casting import ray_casting
from map import mini_map
class Drawing:
def __init__(self, sc, sc_map):
self.sc = sc
self.sc_map = sc_map
self.font = pygame.font.SysFont('Arial', 36, bold=True)
def background(self):
pygame.draw.rect(self.sc, SKYBLUE, (0, 0, WIDTH, HALF_HEIGHT))
pygame.draw.rect(self.sc, DARKGREY, (0, HALF_HEIGHT, WIDTH, HALF_HEIGHT))
def world(self, player_pos, player_angle):
ray_casting(self.sc, player_pos, player_angle)
def fps(self, clock):
display_fps = str(int(clock.get_fps()))
render = self.font.render(display_fps, 0, CRED)
self.sc.blit(render, FPS_POS)
| def mini_map(self, player):
|
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: CV-Reimplementation/Ucolor-Reimplementation
# Path: config/config.py
class Config(object):
r"""
A collection of all the required configuration parameters. This class is a nested dict-like
structure, with nested keys accessible as attributes. It contains sensible default values for
all the parameters, which may be overriden by (first) through a YAML file and (second) through
a list of attributes and values.
Extended Summary
----------------
This class definition contains default values corresponding to ``joint_training`` phase, as it
is the final training phase and uses almost all the configuration parameters. Modification of
any parameter after instantiating this class is not possible, so you must override required
parameter values in either through ``config_yaml`` file or ``config_override`` list.
Parameters
----------
config_yaml: str
Path to a YAML file containing configuration parameters to override.
config_override: List[Any], optional (default= [])
A list of sequential attributes and values of parameters to override. This happens after
overriding from YAML file.
Examples
--------
Let a YAML file named "config.yaml" specify these parameters to override::
ALPHA: 1000.0
BETA: 0.5
>>> _C = Config("config.yaml", ["OPTIM.BATCH_SIZE", 2048, "BETA", 0.7])
>>> _C.ALPHA # default: 100.0
1000.0
>>> _C.BATCH_SIZE # default: 256
2048
>>> _C.BETA # default: 0.1
0.7
Attributes
----------
"""
def __init__(self, config_yaml: str, config_override: List[Any] = []):
self._C = CN()
self._C.GPU = [0]
self._C.VERBOSE = False
self._C.MODEL = CN()
self._C.MODEL.SESSION = 'LUT'
self._C.MODEL.INPUT = 'input'
self._C.MODEL.TARGET = 'target'
self._C.OPTIM = CN()
self._C.OPTIM.BATCH_SIZE = 1
self._C.OPTIM.SEED = 3407
self._C.OPTIM.NUM_EPOCHS = 100
self._C.OPTIM.NEPOCH_DECAY = [100]
self._C.OPTIM.LR_INITIAL = 0.0002
self._C.OPTIM.LR_MIN = 0.0002
self._C.OPTIM.BETA1 = 0.5
self._C.OPTIM.WANDB = False
self._C.TRAINING = CN()
self._C.TRAINING.VAL_AFTER_EVERY = 3
self._C.TRAINING.RESUME = False
self._C.TRAINING.TRAIN_DIR = '../dataset/Jung/train'
self._C.TRAINING.VAL_DIR = '../dataset/Jung/test'
self._C.TRAINING.SAVE_DIR = 'checkpoints'
self._C.TRAINING.PS_W = 512
self._C.TRAINING.PS_H = 512
self._C.TRAINING.ORI = False
self._C.TESTING = CN()
self._C.TESTING.WEIGHT = None
self._C.TESTING.SAVE_IMAGES = False
# Override parameter values from YAML file first, then from override list.
self._C.merge_from_file(config_yaml)
self._C.merge_from_list(config_override)
# Make an instantiated object of this class immutable.
self._C.freeze()
def dump(self, file_path: str):
r"""Save config at the specified file path.
Parameters
----------
file_path: str
(YAML) path to save config at.
"""
self._C.dump(stream=open(file_path, "w"))
def __getattr__(self, attr: str):
return self._C.__getattr__(attr)
def __repr__(self):
return self._C.__repr__()
# Path: data/data_RGB.py
def get_training_data(rgb_dir, inp, target, img_options):
assert os.path.exists(rgb_dir)
return DataLoaderTrain(rgb_dir, inp, target, img_options)
# Path: data/data_RGB.py
def get_validation_data(rgb_dir, inp, target, img_options):
assert os.path.exists(rgb_dir)
return DataLoaderVal(rgb_dir, inp, target, img_options)
# Path: train.py
import warnings
import torch.optim as optim
from accelerate import Accelerator
from pytorch_msssim import SSIM
from torch.utils.data import DataLoader
from torchmetrics.functional import peak_signal_noise_ratio, structural_similarity_index_measure
from tqdm import tqdm
from config import Config
from data import get_training_data, get_validation_data
from models import *
from utils import *
warnings.filterwarnings('ignore')
opt = Config('config.yml')
seed_everything(opt.OPTIM.SEED)
def train():
# Accelerate
accelerator = Accelerator(log_with='wandb') if opt.OPTIM.WANDB else Accelerator()
device = accelerator.device
config = {
"dataset": opt.TRAINING.TRAIN_DIR
}
accelerator.init_trackers("shadow", config=config)
if accelerator.is_local_main_process:
os.makedirs(opt.TRAINING.SAVE_DIR, exist_ok=True)
# Data Loader
train_dir = opt.TRAINING.TRAIN_DIR
val_dir = opt.TRAINING.VAL_DIR
train_dataset = get_training_data(train_dir, opt.MODEL.INPUT, opt.MODEL.TARGET, {'w': opt.TRAINING.PS_W, 'h': opt.TRAINING.PS_H})
train_loader = DataLoader(dataset=train_dataset, batch_size=opt.OPTIM.BATCH_SIZE, shuffle=True, num_workers=16,
drop_last=False, pin_memory=True)
| val_dataset = get_validation_data(val_dir, opt.MODEL.INPUT, opt.MODEL.TARGET, {'w': opt.TRAINING.PS_W, 'h': opt.TRAINING.PS_H, 'ori': opt.TRAINING.ORI}) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: ottuco/multi-api-mocker
# Path: multi_api_mocker/utils.py
def group_by_url(api_mocks: List[MockAPIResponse]) -> List[MockConfiguration]:
"""
Organizes a list of MockAPIResponse objects by their URL and method, grouping
them into lists of responses for each endpoint. This grouping is necessary for
requests-mock when multiple responses for the same endpoint are required, as it
allows requests-mock to cycle through the responses in order for each subsequent
call to the same URL.
Parameters:
api_mocks (List[MockConfiguration]): A list of MockAPIResponse objects
representing the expected responses
for different API calls.
Returns:
List[MockConfiguration]: A list of MockConfiguration objects where each object
contains the URL, method, and a list of responses to be
used by requests-mock to simulate API interactions.
"""
grouped_mocks = defaultdict(list)
for mock in api_mocks:
# Create an instance of ResponseKwargs
response_kwargs = ResponseKwargs(
text=mock.text if not mock.exc else None,
status_code=mock.status_code if not mock.exc else None,
json=mock.json if not mock.exc else None,
exc=mock.exc if mock.exc else None,
)
# Add the ResponseKwargs instance, not the dict
grouped_mocks[(mock.url, mock.method)].append(response_kwargs)
output = []
for (url, method), kwargs_list in grouped_mocks.items():
# Convert each ResponseKwargs instance to a dict
responses = [kwargs.to_dict() for kwargs in kwargs_list]
config = MockConfiguration(url=url, method=method.upper(), responses=responses)
output.append(config)
return output
# Path: multi_api_mocker/utils.py
class MockSet:
"""
A collection class that manages MockAPIResponse objects and integrates with the
requests_mock fixture. This class provides efficient access and iteration over
grouped API responses by their endpoint names, simplifying the process of setting
up and managing multiple mock responses in tests. It also stores and allows access
to the requests_mock adapter's _Matcher objects associated with each mock response,
enabling advanced interactions and assertions in tests.
Parameters:
api_responses (List[MockAPIResponse]): A list of MockAPIResponse objects, each
representing a specific API response.
requests_mock (Mocker): The requests_mock fixture instance used for registering
the mock API responses.
matchers (Dict[str, _Matcher]): A dictionary mapping endpoint names to their
respective requests_mock adapter _Matcher
objects.
Attributes:
_response_registry (Dict[str, MockAPIResponse]): A dictionary mapping endpoint
names to their respective
MockAPIResponse objects.
requests_mock (Optional[Mocker]): The requests_mock fixture instance.
matchers (Dict[str, _Matcher]): A dictionary of _Matcher objects, providing
detailed control and inspection capabilities
for the registered mock API responses.
Methods:
get_matcher(endpoint_name: str) -> _Matcher: Returns the _Matcher object
associated with the given
endpoint name.
"""
def __init__(
self,
api_responses: List[MockAPIResponse],
requests_mock: Mocker = None,
matchers: Dict[str, _Matcher] = None,
):
self._response_registry = {
response.endpoint_name: response for response in api_responses
}
self.requests_mock = requests_mock
self.matchers = matchers or {}
def __getitem__(self, endpoint_name: str) -> MockAPIResponse:
return self._response_registry[endpoint_name]
def __iter__(self):
return iter(self._response_registry.values())
def __len__(self):
return len(self._response_registry)
def __repr__(self):
endpoint_names = ", ".join(self._response_registry.keys())
return f"<{self.__class__.__name__} with endpoints: {endpoint_names}>"
def get_matcher(self, endpoint_name: str) -> _Matcher:
return self.matchers.get(endpoint_name)
# Path: multi_api_mocker/contrib/pytest_plugin.py
import pytest
from requests_mock import Mocker
from ..utils import group_by_url, MockSet
@pytest.fixture(scope="function")
def setup_api_mocks(requests_mock: Mocker, request) -> MockSet:
"""
A pytest fixture for configuring mock API responses in a test environment.
It takes subclasses of MockAPIResponse, each representing a unique API call
configuration. These subclasses facilitate the creation of simple or complex
response flows, simulating real-world API interactions.
Parameters:
requests_mock (Mocker): The pytest requests_mock fixture.
request: The pytest request object containing parametrized test data.
Returns:
MockSet: An instance of MockSet containing the organized MockAPIResponse
objects, ready for use in tests.
The fixture supports multiple test scenarios, allowing for thorough
testing of varying API response conditions. This is especially useful
for simulating sequences of API calls like Fork, Commit, and Push
in a version control system context.
Example Usage:
- Single API Call Test:
@pytest.mark.parametrize("setup_api_mocks", [([Fork()])], indirect=True)
- Multi-call Sequence Test:
@pytest.mark.parametrize(
"setup_api_mocks", [([Fork(), Commit(), Push()])], indirect=True
)
- Testing Multiple Scenarios:
@pytest.mark.parametrize(
"setup_api_mocks",
[([Fork(), Commit(), Push()]), ([Fork(), Commit(), ForcePush()])],
indirect=True
)
This fixture converts the list of MockAPIResponse subclasses into MockConfiguration
instances, registers them with requests_mock, and returns a MockSet object, which
allows querying each mock by its endpoint name.
"""
# Convert the incoming parameter to a list of MockConfiguration instances
| api_mocks_configurations = group_by_url(request.param) |
You will be given python files from a code repository, with the current file being shown last. Your task is to predict the next line of code in the current file.
NOTE: You should only predict the next line in the current file. Do not produce more than one line, and do not provide any explanation.
====REPOSITORY====
# Repo Name: Jisencc/yolov5_dual_weighting
# Path: utils/augmentations.py
def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
# Path: utils/general.py
def resample_segments(segments, n=1000):
# Up-sample an (n,2) segment
for i, s in enumerate(segments):
s = np.concatenate((s, s[0:1, :]), axis=0)
x = np.linspace(0, len(s) - 1, n)
xp = np.arange(len(s))
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
return segments
# Path: utils/general.py
def segment2box(segment, width=640, height=640):
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
x, y = segment.T # segment xy
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
x, y, = x[inside], y[inside]
return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
# Path: utils/segment/augmentations.py
import math
import random
import cv2
import numpy as np
from ..augmentations import box_candidates
from ..general import resample_segments, segment2box
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Image augmentation functions
"""
def mixup(im, labels, segments, im2, labels2, segments2):
# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
im = (im * r + im2 * (1 - r)).astype(np.uint8)
labels = np.concatenate((labels, labels2), 0)
segments = np.concatenate((segments, segments2), 0)
return im, labels, segments
def random_perspective(im,
targets=(),
segments=(),
degrees=10,
translate=.1,
scale=.1,
shear=10,
perspective=0.0,
border=(0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]
height = im.shape[0] + border[0] * 2 # shape(h,w,c)
width = im.shape[1] + border[1] * 2
# Center
C = np.eye(3)
C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
# Perspective
P = np.eye(3)
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
# Rotation and Scale
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1 + scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
# Translation
T = np.eye(3)
T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
# Combined rotation matrix
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
if perspective:
im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
else: # affine
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
# Visualize
# import matplotlib.pyplot as plt
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
# ax[0].imshow(im[:, :, ::-1]) # base
# ax[1].imshow(im2[:, :, ::-1]) # warped
# Transform label coordinates
n = len(targets)
new_segments = []
if n:
new = np.zeros((n, 4))
| segments = resample_segments(segments) # upsample |