Delete processing_phi3_v.py

processing_phi3_v.py

@@ -1,217 +0,0 @@
-# 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))