File size: 11,547 Bytes
a02da1b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
# coding=utf-8
# Copyright 2024 Microsoft and 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.

"""

Processor class for Phi3-V.

"""
import re
from typing import List, Optional, Union

import torch

import transformers
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy
from transformers.utils import TensorType
from .image_processing_phi3_v import Phi3VImageProcessor 
transformers.Phi3VImageProcessor = Phi3VImageProcessor 

class Phi3VProcessor(ProcessorMixin):
    r"""

    Constructs a Phi3-V processor which wraps a Phi3-V image processor and a LLaMa tokenizer into a single processor.



    [`Phi3VProcessor`] offers all the functionalities of [`Phi3VImageProcessor`] and [`LlamaTokenizerFast`]. See the

    [`~Phi3VProcessor.__call__`] and [`~Phi3VProcessor.decode`] for more information.



    Args:

        image_processor ([`Phi3VImageProcessor`], *optional*):

            The image processor is a required input.

        tokenizer ([`LlamaTokenizerFast`], *optional*):

            The tokenizer is a required input.

    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "Phi3VImageProcessor"
    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
    special_image_token = "<|image|>"

    def __init__(self, image_processor, tokenizer):
        self.image_processor = image_processor
        self.tokenizer = tokenizer
        self.num_img_tokens = image_processor.num_img_tokens
        self.img_tokens = [f"<|image_{i+1}|>" for i in range(1000000)]

    def __call__(

        self,

        text: Union[TextInput, List[TextInput]],

        images: ImageInput = None,

        padding: Union[bool, str, PaddingStrategy] = False,

        truncation: Union[bool, str, TruncationStrategy] = None,

        max_length=None,

        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,

    ) -> BatchFeature:
        """

        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`

        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode

        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to

        Phi3ImageProcessor's [`~Phi3ImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring

        of the above two methods for more information.



        Args:

            text (`str`, `List[str]`, `List[List[str]]`):

                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings

                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set

                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).

            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):

                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch

                tensor. Both channels-first and channels-last formats are supported.

            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):

                Select a strategy to pad the returned sequences (according to the model's padding side and padding

                index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single

                  sequence if provided).

                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum

                  acceptable input length for the model if that argument is not provided.

                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different

                  lengths).

            max_length (`int`, *optional*):

                Maximum length of the returned list and optionally padding length (see above).

            truncation (`bool`, *optional*):

                Activates truncation to cut input sequences longer than `max_length` to `max_length`.

            return_tensors (`str` or [`~utils.TensorType`], *optional*):

                If set, will return tensors of a particular framework. Acceptable values are:



                - `'tf'`: Return TensorFlow `tf.constant` objects.

                - `'pt'`: Return PyTorch `torch.Tensor` objects.

                - `'np'`: Return NumPy `np.ndarray` objects.

                - `'jax'`: Return JAX `jnp.ndarray` objects.



        Returns:

            [`BatchFeature`]: A [`BatchFeature`] with the following fields:



            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.

            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when

              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not

              `None`).

            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.

        """
        if images is not None:
            image_inputs = self.image_processor(images, return_tensors=return_tensors)
        else:
            image_inputs = {}
        inputs = self._convert_images_texts_to_inputs(image_inputs, text, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors)
        return inputs

    def calc_num_image_tokens(self, images: ImageInput):
        """ Calculate the number of image tokens for each image.

        Args:

            images (`ImageInput`):

                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If

                passing in images with pixel values between 0 and 1, set `do_rescale=False`.

        """
        return self.image_processor.calc_num_image_tokens(images)
        
    def calc_num_image_tokens_from_image_size(self, width, height):
        """ Calculate the number of image token for an image with given width and height.

        Args:

            width (`int`):

                Width of the image.

            height (`int`):

                Height of the image.

        """
        return self.image_processor.calc_num_image_tokens_from_image_size(width, height)
    
    
    @property 
    def special_image_token_id(self):
        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)

    def get_special_image_token_id(self):
        return self.tokenizer.convert_tokens_to_ids(self.special_image_token)
    
    def _convert_images_texts_to_inputs(self, images, texts, padding=False, truncation=None, max_length=None, return_tensors=None):

        if not len(images):
            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length)
            return BatchFeature(data={**model_inputs})

        pattern = r"<\|image_\d+\|>"
        prompt_chunks = [self.tokenizer(chunk).input_ids for chunk in re.split(pattern, texts)] 

        if 'num_img_tokens' in images:
            num_img_tokens = images['num_img_tokens']
        else:
            assert 'num_crops' in images, 'num_crops must be provided in images if num_img_tokens is not provided'
            num_crops = images['num_crops']
            num_img_tokens = [_num_crops * self.num_img_tokens for _num_crops in num_crops] 

        images, image_sizes = images['pixel_values'], images['image_sizes']

        # image_tags needs to start from 1 to n
        image_tags = re.findall(pattern, texts) 
        # image_ids = [int(s.split("|")[1].split("_")[-1]) * -1 for s in image_tags]
        # image_ids_pad = [[iid]*num_img_tokens[i] for i, iid in enumerate(image_ids)]
        image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]
        unique_image_ids = sorted(list(set(image_ids)))
        # image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be [1, 4, 5]
        # check the condition
        assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
        # total images must be the same as the number of image tags
        assert len(unique_image_ids) == len(images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(images)} images"

        image_ids_pad = [[-iid]*num_img_tokens[iid-1] for iid in image_ids]

        def insert_separator(X, sep_list):
            if len(X) > len(sep_list):
                sep_list.append([])
            return [ele for sublist in zip(X, sep_list) for ele in sublist]
        input_ids = []
        offset = 0                
        for x in insert_separator(prompt_chunks, image_ids_pad):
            input_ids.extend(x[offset:])

        input_ids = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
        attention_mask = (input_ids > -1000000).to(torch.long)

        return BatchFeature(data={"input_ids": input_ids,
                                  "attention_mask": attention_mask,
                                  "pixel_values": images, 
                                  "image_sizes": image_sizes})


    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
    def batch_decode(self, *args, **kwargs):
        """

        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

        refer to the docstring of this method for more information.

        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
    def decode(self, *args, **kwargs):
        """

        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to

        the docstring of this method for more information.

        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))