Delete unecessary files

image_processing_idefics.py

@@ -1,168 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-"""Image processor class for Idefics."""
-
-from typing import Callable, Dict, List, Optional, Union
-
-from PIL import Image
-
-from ...image_processing_utils import BaseImageProcessor, BatchFeature
-from ...image_transforms import resize, to_channel_dimension_format
-from ...image_utils import (
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    make_list_of_images,
-    to_numpy_array,
-    valid_images,
-)
-from ...utils import TensorType, is_torch_available
-
-
-IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
-IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
-
-
-def convert_to_rgb(image):
-    # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
-    # for transparent images. The call to `alpha_composite` handles this case
-    if image.mode == "RGB":
-        return image
-
-    image_rgba = image.convert("RGBA")
-    background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
-    alpha_composite = Image.alpha_composite(background, image_rgba)
-    alpha_composite = alpha_composite.convert("RGB")
-    return alpha_composite
-
-
-class IdeficsImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a Idefics image processor.
-
-    Args:
-        image_size (`int`, *optional*, defaults to 224):
-            Resize to image size
-        image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
-            overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            Can be overridden by the `image_std` parameter in the `preprocess` method.
-        image_num_channels (`int`, *optional*, defaults to 3):
-            Number of image channels.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        image_size: int = 224,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        image_num_channels: Optional[int] = 3,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-
-        self.image_size = image_size
-        self.image_num_channels = image_num_channels
-        self.image_mean = image_mean
-        self.image_std = image_std
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        image_num_channels: Optional[int] = 3,
-        image_size: Optional[Dict[str, int]] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        transform: Callable = None,
-        **kwargs,
-    ) -> TensorType.PYTORCH:
-        """
-        Preprocess a batch of images.
-
-        Args:
-            images (`ImageInput`):
-                A list of images to preprocess.
-            image_size (`int`, *optional*, defaults to `self.image_size`):
-                Resize to image size
-            image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
-                Number of image channels.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
-                Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-                channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
-                be overridden by the `image_mean` parameter in the `preprocess` method.
-            image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
-                Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-                number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
-                method. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            transform (`Callable`, *optional*, defaults to `None`):
-                A custom transform function that accepts a single image can be passed for training. For example,
-                `torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
-                assumed - and then a preset of inference-specific transforms will be applied to the images
-
-        Returns:
-            a PyTorch tensor of the processed images
-
-        """
-        image_size = image_size if image_size is not None else self.image_size
-        image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-        size = (image_size, image_size)
-
-        if isinstance(images, list) and len(images) == 0:
-            return []
-
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        # For training a user needs to pass their own set of transforms as a Callable.
-        # For reference this is what was used in the original IDEFICS training:
-        # transform = transforms.Compose([
-        #     convert_to_rgb,
-        #     transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
-        #     transforms.ToTensor(),
-        #     transforms.Normalize(mean=image_mean, std=image_std),
-        # ])
-        if transform is not None:
-            if not is_torch_available():
-                raise ImportError("To pass in `transform` torch must be installed")
-            import torch
-
-            images = [transform(x) for x in images]
-            return torch.stack(images)
-
-        # for inference we do the exact transforms that were used to train IDEFICS
-        images = [convert_to_rgb(x) for x in images]
-        # further transforms expect numpy arrays
-        images = [to_numpy_array(x) for x in images]
-        images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
-        images = [self.rescale(image=image, scale=1 / 255) for image in images]
-        images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
-        images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
-        # TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
-        images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"]
-
-        return images

image_processing_img2html.py

@@ -1,168 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-"""Image processor class for Img2HTML."""
-
-from typing import Callable, Dict, List, Optional, Union
-
-from PIL import Image
-
-from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
-from transformers.image_transforms import resize, to_channel_dimension_format
-from transformers.image_utils import (
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    make_list_of_images,
-    to_numpy_array,
-    valid_images,
-)
-from transformers.utils import TensorType, is_torch_available
-
-
-IMG2HTML_STANDARD_MEAN = [0.5, 0.5, 0.5]
-IMG2HTML_STANDARD_STD = [0.5, 0.5, 0.5]
-
-
-def convert_to_rgb(image):
-    # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
-    # for transparent images. The call to `alpha_composite` handles this case
-    if image.mode == "RGB":
-        return image
-
-    image_rgba = image.convert("RGBA")
-    background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
-    alpha_composite = Image.alpha_composite(background, image_rgba)
-    alpha_composite = alpha_composite.convert("RGB")
-    return alpha_composite
-
-
-class Img2HTMLImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a Img2HTML image processor.
-
-    Args:
-        image_size (`int`, *optional*, defaults to 224):
-            Resize to image size
-        image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
-            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
-            overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
-            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            Can be overridden by the `image_std` parameter in the `preprocess` method.
-        image_num_channels (`int`, *optional*, defaults to 3):
-            Number of image channels.
-    """
-
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-        self,
-        image_size: int = 224,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        image_num_channels: Optional[int] = 3,
-        **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-
-        self.image_size = image_size
-        self.image_num_channels = image_num_channels
-        self.image_mean = image_mean
-        self.image_std = image_std
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        image_num_channels: Optional[int] = 3,
-        image_size: Optional[Dict[str, int]] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        transform: Callable = None,
-        **kwargs,
-    ) -> TensorType.PYTORCH:
-        """
-        Preprocess a batch of images.
-
-        Args:
-            images (`ImageInput`):
-                A list of images to preprocess.
-            image_size (`int`, *optional*, defaults to `self.image_size`):
-                Resize to image size
-            image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
-                Number of image channels.
-            image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
-                Mean to use if normalizing the image. This is a float or list of floats the length of the number of
-                channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
-                be overridden by the `image_mean` parameter in the `preprocess` method.
-            image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
-                Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
-                number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
-                method. Can be overridden by the `image_std` parameter in the `preprocess` method.
-            transform (`Callable`, *optional*, defaults to `None`):
-                A custom transform function that accepts a single image can be passed for training. For example,
-                `torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
-                assumed - and then a preset of inference-specific transforms will be applied to the images
-
-        Returns:
-            a PyTorch tensor of the processed images
-
-        """
-        image_size = image_size if image_size is not None else self.image_size
-        image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
-        image_mean = image_mean if image_mean is not None else self.image_mean
-        image_std = image_std if image_std is not None else self.image_std
-        size = (image_size, image_size)
-
-        if isinstance(images, list) and len(images) == 0:
-            return []
-
-        images = make_list_of_images(images)
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        # For training a user needs to pass their own set of transforms as a Callable.
-        # For reference this is what was used in the original IDEFICS training:
-        # transform = transforms.Compose([
-        #     convert_to_rgb,
-        #     transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BILINEAR),
-        #     transforms.ToTensor(),
-        #     transforms.Normalize(mean=image_mean, std=image_std),
-        # ])
-        if transform is not None:
-            if not is_torch_available():
-                raise ImportError("To pass in `transform` torch must be installed")
-            import torch
-
-            images = [transform(x) for x in images]
-            return torch.stack(images)
-
-        # for inference we do the exact transforms that were used to train IDEFICS
-        images = [convert_to_rgb(x) for x in images]
-        # further transforms expect numpy arrays
-        images = [to_numpy_array(x) for x in images]
-        images = [resize(x, size, resample=PILImageResampling.BILINEAR) for x in images]
-        images = [self.rescale(image=image, scale=1 / 255) for image in images]
-        images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
-        images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
-        # TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
-        images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"]
-
-        return images

processing_idefics.py

@@ -1,414 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# 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 IDEFICS.
-"""
-
-from typing import Callable, List, Optional, Union
-from urllib.parse import urlparse
-
-from ...feature_extraction_utils import BatchFeature
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
-from ...utils import TensorType, is_torch_available
-
-
-if is_torch_available():
-    import torch
-
-
-IMAGE_TOKEN = "<image>"
-
-
-# copied from m4.training.packing
-def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1):
-    # This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]]
-
-    # If any of images index are more than num_classes, set them to -1.
-    # Words after the max number of images allowed have been seen don't attend on anything
-    if num_classes != -1:
-        incremental_mask[incremental_mask >= num_classes] = -1
-
-    negatives = incremental_mask == -1
-    incremental_mask[negatives] = 0
-    attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
-    attn_mask[negatives, :] = 0
-    return attn_mask
-
-
-# copied from m4.training.packing
-def image_attention_mask_for_packed_input_ids(input_ids, tokenizer):
-    image_attention_mask = torch.full_like(input_ids, fill_value=-1)
-    next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
-    image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
-    eod_token_id = tokenizer.eos_token_id
-    for batch_idx in range(input_ids.size(0)):
-        count = -1
-        seen_eod = False
-        for idx, token_id in enumerate(input_ids[batch_idx]):
-            if token_id == image_token_id:
-                count += 1
-                image_attention_mask[batch_idx][idx] = count
-                seen_eod = False
-            else:
-                image_attention_mask[batch_idx][idx] = count
-
-            if seen_eod:
-                image_attention_mask[batch_idx][idx] = -1
-
-            if token_id == eod_token_id:
-                seen_eod = True
-
-    for batch_idx in range(input_ids.size(0)):
-        count = -1
-        seen_eod = False
-        for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
-            token_id = input_ids[batch_idx][idx]
-            if token_id == image_token_id:
-                count += 1
-                next_image_attention_mask[batch_idx][idx] = count
-                seen_eod = False
-            else:
-                next_image_attention_mask[batch_idx][idx] = count
-
-            if token_id == eod_token_id:
-                seen_eod = True
-
-            if seen_eod:
-                next_image_attention_mask[batch_idx][idx] = -1
-
-        non_negative_indices = next_image_attention_mask[batch_idx] != -1
-        next_image_attention_mask[batch_idx][non_negative_indices] -= count
-        next_image_attention_mask[batch_idx][non_negative_indices] *= -1
-
-    return image_attention_mask, next_image_attention_mask
-
-
-def is_url(string):
-    """Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
-    invalidated the url"""
-    if " " in string:
-        return False
-    result = urlparse(string)
-    return all([result.scheme, result.netloc])
-
-
-class IdeficsProcessor(ProcessorMixin):
-    r"""
-    Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
-
-    [`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
-    the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
-
-    Args:
-        image_processor (`IdeficsImageProcessor`):
-            An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
-        tokenizer (`LlamaTokenizerFast`):
-            An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
-        image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "IdeficsImageProcessor"
-    tokenizer_class = "LlamaTokenizerFast"
-
-    def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
-        if image_processor is None:
-            raise ValueError("You need to specify an `image_processor`.")
-        if tokenizer is None:
-            raise ValueError("You need to specify a `tokenizer`.")
-
-        super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-        self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
-
-        self.default_image_dims = (
-            self.image_processor.image_num_channels,
-            self.image_processor.image_size,
-            self.image_processor.image_size,
-        )
-
-        self.tokenizer_was_trained_with_end_of_utterance_token = (
-            True
-            if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
-            else False
-        )
-
-    def __call__(
-        self,
-        prompts: Union[List[TextInput], List[List[TextInput]]],
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        transform: Callable = None,
-        add_eos_token=False,
-        add_end_of_utterance_token=None,
-        debug=False,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
-    ) -> BatchEncoding:
-        """This method takes batched or non-batched prompts made of text and images and converts them into prompts that
-        the model was trained on and prepares the image pixel values for the model to process.
-
-        Args:
-            prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
-                either a single prompt or a batched list of prompts - see the detailed description immediately after
-                the end of the arguments doc section.
-            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`.
-            transform (`Callable`, *optional*):
-                A custom transform function that accepts a single image can be passed for training. For example,
-                `torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
-                set of transforms will be applied to the images
-            add_eos_token (`bool`, *optional*, defaults to `False`):
-                Adds `eos_token` at the end of the final prompt if True`
-            add_end_of_utterance_token (`bool`, *optional*)
-                Whether to automatically add `<end_of_utterance>` after each prompt's text input (unless followed by an
-                image). If `None` the tokenizer will be checked instead and if this token is found in
-                `additional_special_tokens` then the value will be `True`.
-            debug (`bool`, *optional*, defaults to `False`):
-                `True` value will help debug prompt generation by dumping useful information
-            return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
-                The type of tensors to return. Can be one of:
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-
-        Returns:
-            a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
-            directly passed to `model.generate`
-
-        Detailed explanation:
-
-        Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
-
-        An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
-
-        When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
-        entry into the prompt.
-
-        Example:
-
-        ```python
-        checkpoint = "HuggingFaceM4/idefics-9b"
-        processor = AutoProcessor.from_pretrained(checkpoint)
-        url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
-        img = processor.image_processor.fetch_images([url])[0]
-
-        prompts = [
-            "User:",
-            img,
-            "Describe this image.\nAssistant: An image of two kittens in grass.\n",
-            "User:",
-            "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
-            "Describe this image.\nAssistant:",
-        ]
-
-        inputs = processor(prompts, return_tensors="pt")
-        generated_ids = model.generate(**inputs, max_length=100)
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        ```
-
-        In this example the `prompts` will be converted into:
-
-        ```
-        <s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
-        Assistant: An image of two kittens in grass.
-        User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
-        Assistant:'
-        ```
-
-        and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
-        `pixel_values` dict entry of the return value.
-
-        This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
-        first image is passed as object and the second one as a url.
-
-        To do training do:
-
-        ```python
-        image_transform = transforms.Compose(
-            [
-                transforms.RandomResizedCrop(
-                    (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
-                ),
-                transforms.ToTensor(),
-                transforms.Normalize(mean=self.image_mean, std=self.image_std),
-            ]
-        )
-        inputs = processor(prompts, transform=image_transform, return_tensors="pt")
-        ```
-
-        In order to help debug prompt generation enable `debug=True` which will show you what's happening.
-
-        """
-
-        # if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
-        if add_end_of_utterance_token is None:
-            add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
-
-        # turn non-batched prompts into batched
-        if not any(isinstance(i, list) for i in prompts):
-            prompts = [prompts]
-
-        fake_token = "<fake_token_around_image>"
-        image_token = "<image>"
-        end_of_utterance_token = "<end_of_utterance>"
-
-        def image_tokens(last_was_image):
-            if last_was_image:
-                return image_token + fake_token
-            else:
-                return fake_token + image_token + fake_token
-
-        all_prompts = []
-        all_images = []
-        for sample in prompts:
-            # the model was trained on samples starting with <s>
-            full_text = f"{self.tokenizer.bos_token}"
-
-            # an image can either be an image object in the item or the url, everything else is a verbatim prompt text
-            image_objects = []
-            last_was_image = False
-            last_was_text = False
-            for i, item in enumerate(sample):
-                if i > 0:
-                    last_was_text = True if not last_was_image else False
-
-                if isinstance(item, str):
-                    item = item.strip(" ")
-                    if is_url(item):
-                        image = self.image_processor.fetch_images(item)
-                        full_text += image_tokens(last_was_image)
-                        image_objects.append(image)
-                        last_was_image = True
-                    else:
-                        # we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
-                        if add_end_of_utterance_token and last_was_text:
-                            full_text += end_of_utterance_token
-                        full_text += item
-                        last_was_image = False
-                else:
-                    # must be an image obj
-                    full_text += image_tokens(last_was_image)
-                    image_objects.append(item)
-                    last_was_image = True
-
-            if add_eos_token:
-                full_text += self.tokenizer.eos_token
-
-            if debug is True:
-                print(f"{full_text=}")
-
-            image_objects = self.image_processor(image_objects, transform=transform)
-
-            all_prompts.append(full_text)
-            all_images.append(image_objects)
-
-        text_encoding = self.tokenizer(
-            text=all_prompts,
-            add_special_tokens=False,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-        )
-        all_texts = text_encoding["input_ids"]
-
-        max_seq_len = max(len(x) for x in all_texts)
-
-        # max_num_images has to be at least 1 even when there are no images
-        max_num_images = max(len(x) for x in all_images)
-        max_num_images = max(1, max_num_images)
-
-        at_least_one_image = sum(len(x) for x in all_images) > 0
-        output_input_ids = []
-        output_images = []
-        output_attention_masks = []
-        for text, images in zip(all_texts, all_images):
-            padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
-            unpadded_seq_len = len(text)
-            start = max_seq_len - unpadded_seq_len
-            padded_input_ids[start:] = text[:max_seq_len]
-
-            attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
-            attention_mask[start:] = 1
-
-            image_count = padded_input_ids.count(self.image_token_id)
-            local_max_num_images = min(image_count, max_num_images)
-
-            current_images = images[:local_max_num_images]
-
-            if len(current_images) > 0:
-                padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
-                padded_image_tensor[: current_images.size(0)] = current_images
-            else:
-                padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
-
-            output_images.append(padded_image_tensor)
-            output_input_ids.append(torch.tensor(padded_input_ids))
-
-            output_attention_masks.append(attention_mask)
-
-        output_input_ids = torch.stack(output_input_ids)
-        output_images = torch.stack(output_images)
-        output_attention_masks = torch.stack(output_attention_masks)
-
-        if at_least_one_image:
-            image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
-            image_attention_mask = incremental_to_binary_attention_mask(
-                image_attention_mask, num_classes=max_num_images
-            )
-        else:
-            # in full language mode we set the image mask to all-0s
-            image_attention_mask = torch.zeros(
-                output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
-            )
-
-        return BatchFeature(
-            data={
-                "input_ids": output_input_ids,
-                "attention_mask": output_attention_masks,
-                "pixel_values": output_images,
-                "image_attention_mask": image_attention_mask,
-            }
-        )
-
-    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)
-
-    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
-    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))

processing_img2html.py

@@ -1,345 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# 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 Img2HTML.
-"""
-
-from typing import Callable, List, Optional, Union
-from urllib.parse import urlparse
-
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.processing_utils import ProcessorMixin
-from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
-from transformers.utils import TensorType, is_torch_available
-
-from .image_processing_img2html import Img2HTMLImageProcessor
-
-if is_torch_available():
-    import torch
-
-
-IMAGE_TOKEN = "<image>"
-
-
-def is_url(string):
-    """Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
-    invalidated the url"""
-    if " " in string:
-        return False
-    result = urlparse(string)
-    return all([result.scheme, result.netloc])
-
-class Img2HTMLProcessor(ProcessorMixin):
-    r"""
-    Constructs a Img2HTML processor which wraps a LLama tokenizer and Img2HTML image processor into a single processor.
-
-    [`Img2HTMLProcessor`] offers all the functionalities of [`Img2HTMLImageProcessor`] and [`LlamaTokenizerFast`]. See
-    the docstring of [`~Img2HTMLProcessor.__call__`] and [`~Img2HTMLProcessor.decode`] for more information.
-
-    Args:
-        image_processor (`Img2HTMLImageProcessor`):
-            An instance of [`Img2HTMLImageProcessor`]. The image processor is a required input.
-        tokenizer (`LlamaTokenizerFast`):
-            An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
-        image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image)
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "Img2HTMLImageProcessor"
-    tokenizer_class = "LlamaTokenizerFast"
-
-    def __init__(self, image_processor, tokenizer=None, image_size=960, **kwargs):
-        if image_processor is None:
-            raise ValueError("You need to specify an `image_processor`.")
-        if tokenizer is None:
-            raise ValueError("You need to specify a `tokenizer`.")
-
-        super().__init__(image_processor, tokenizer)
-        self.current_processor = self.image_processor
-        self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
-
-        self.default_image_dims = (
-            self.image_processor.image_num_channels,
-            self.image_processor.image_size,
-            self.image_processor.image_size,
-        )
-
-    # @classmethod
-    # def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-    #     # Hack overriding things
-    #     from pathlib import Path
-    #     from transformers.utils import direct_transformers_import
-    #     # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
-    #     transformers_module = direct_transformers_import(Path(__file__).parent)
-
-    #     args = []
-    #     for attribute_name in cls.attributes:
-    #         class_name = getattr(cls, f"{attribute_name}_class")
-    #         if isinstance(class_name, tuple):
-    #             classes = tuple(getattr(transformers_module, n) if n is not None else None for n in class_name)
-    #             use_fast = kwargs.get("use_fast", True)
-    #             if use_fast and classes[1] is not None:
-    #                 attribute_class = classes[1]
-    #             else:
-    #                 attribute_class = classes[0]
-    #         else:
-    #             if class_name == "Img2HTMLImageProcessor":
-    #                 attribute_class = Img2HTMLImageProcessor
-    #             else:
-    #                 attribute_class = getattr(transformers_module, class_name)
-
-    #         args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
-    #     return args
-
-    def __call__(
-        self,
-        prompts: Union[List[TextInput], List[List[TextInput]]],
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        transform: Callable = None,
-        add_eos_token=False,
-        debug=False,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
-    ) -> BatchEncoding:
-        """This method takes batched or non-batched prompts made of text and images and converts them into prompts that
-        the model was trained on and prepares the image pixel values for the model to process.
-
-        Args:
-            prompts (`Union[List[TextInput], [List[List[TextInput]]]]`):
-                either a single prompt or a batched list of prompts - see the detailed description immediately after
-                the end of the arguments doc section.
-            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`.
-            transform (`Callable`, *optional*):
-                A custom transform function that accepts a single image can be passed for training. For example,
-                `torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific
-                set of transforms will be applied to the images
-            add_eos_token (`bool`, *optional*, defaults to `False`):
-                Adds `eos_token` at the end of the final prompt if True`
-            debug (`bool`, *optional*, defaults to `False`):
-                `True` value will help debug prompt generation by dumping useful information
-            return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
-                The type of tensors to return. Can be one of:
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-
-        Returns:
-            a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
-            directly passed to `model.generate`
-
-        Detailed explanation:
-
-        Each entry in `prompts` is either a text to be passed as is or an image that will be processed.
-
-        An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
-
-        When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
-        entry into the prompt.
-
-        Example:
-
-        ```python
-        checkpoint = "HuggingFaceM4/Img2HTML-9b"
-        processor = AutoProcessor.from_pretrained(checkpoint)
-        url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
-        img = processor.image_processor.fetch_images([url])[0]
-
-        prompts = [
-            "User:",
-            img,
-            "Describe this image.\nAssistant: An image of two kittens in grass.\n",
-            "User:",
-            "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
-            "Describe this image.\nAssistant:",
-        ]
-
-        inputs = processor(prompts, return_tensors="pt")
-        generated_ids = model.generate(**inputs, max_length=100)
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        ```
-
-        In this example the `prompts` will be converted into:
-
-        ```
-        <s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
-        Assistant: An image of two kittens in grass.
-        User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
-        Assistant:'
-        ```
-
-        and the two images will be massaged using [`Img2HTMLImageProcessor.__call__`] method and placed inside the
-        `pixel_values` dict entry of the return value.
-
-        This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
-        first image is passed as object and the second one as a url.
-
-        To do training do:
-
-        ```python
-        image_transform = transforms.Compose(
-            [
-                transforms.RandomResizedCrop(
-                    (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
-                ),
-                transforms.ToTensor(),
-                transforms.Normalize(mean=self.image_mean, std=self.image_std),
-            ]
-        )
-        inputs = processor(prompts, transform=image_transform, return_tensors="pt")
-        ```
-
-        In order to help debug prompt generation enable `debug=True` which will show you what's happening.
-
-        """
-        # turn non-batched prompts into batched
-        if not any(isinstance(i, list) for i in prompts):
-            prompts = [prompts]
-
-        fake_token = "<fake_token_around_image>"
-        image_token = "<image>"
-
-        def image_tokens(last_was_image):
-            if last_was_image:
-                return image_token + fake_token
-            else:
-                return fake_token + image_token + fake_token
-
-        all_prompts = []
-        all_images = []
-        for sample in prompts:
-            # the model was trained on samples starting with <s>
-            full_text = f"{self.tokenizer.bos_token}"
-
-            # an image can either be an image object in the item or the url, everything else is a verbatim prompt text
-            image_objects = []
-            last_was_image = False
-            last_was_text = False
-            for i, item in enumerate(sample):
-                if i > 0:
-                    last_was_text = True if not last_was_image else False
-
-                if isinstance(item, str):
-                    item = item.strip(" ")
-                    if is_url(item):
-                        image = self.image_processor.fetch_images(item)
-                        full_text += image_tokens(last_was_image)
-                        image_objects.append(image)
-                        last_was_image = True
-                    else:
-                        full_text += item
-                        last_was_image = False
-                else:
-                    # must be an image obj
-                    full_text += image_tokens(last_was_image)
-                    image_objects.append(item)
-                    last_was_image = True
-
-            if add_eos_token:
-                full_text += self.tokenizer.eos_token
-
-            if debug is True:
-                print(f"{full_text=}")
-
-            image_objects = self.image_processor(image_objects, transform=transform)
-
-            all_prompts.append(full_text)
-            all_images.append(image_objects)
-
-        text_encoding = self.tokenizer(
-            text=all_prompts,
-            add_special_tokens=False,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-        )
-        all_texts = text_encoding["input_ids"]
-
-        max_seq_len = max(len(x) for x in all_texts)
-
-        # max_num_images has to be at least 1 even when there are no images
-        max_num_images = max(len(x) for x in all_images)
-        max_num_images = max(1, max_num_images)
-
-        output_input_ids = []
-        output_images = []
-        output_attention_masks = []
-        for text, images in zip(all_texts, all_images):
-            padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len
-            unpadded_seq_len = len(text)
-            start = max_seq_len - unpadded_seq_len
-            padded_input_ids[start:] = text[:max_seq_len]
-
-            attention_mask = torch.zeros((max_seq_len,), dtype=torch.long)
-            attention_mask[start:] = 1
-
-            image_count = padded_input_ids.count(self.image_token_id)
-            local_max_num_images = min(image_count, max_num_images)
-
-            current_images = images[:local_max_num_images]
-
-            if len(current_images) > 0:
-                padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
-                padded_image_tensor[: current_images.size(0)] = current_images
-            else:
-                padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
-
-            output_images.append(padded_image_tensor)
-            output_input_ids.append(torch.tensor(padded_input_ids))
-
-            output_attention_masks.append(attention_mask)
-
-        output_input_ids = torch.stack(output_input_ids)
-        output_images = torch.stack(output_images)
-        output_attention_masks = torch.stack(output_attention_masks)
-
-
-        return BatchFeature(
-            data={
-                "input_ids": output_input_ids,
-                "attention_mask": output_attention_masks,
-                "pixel_values": output_images,
-            }
-        )
-
-    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)
-
-    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
-    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))