Lightstar/venv/Lib/site-packages/transformers/models/imagegpt/image_processing_imagegpt.py

# 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
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"""Image processor class for ImageGPT."""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    infer_channel_dimension_format,
    is_scaled_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
    validate_preprocess_arguments,
)
from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging


if is_vision_available():
    import PIL


logger = logging.get_logger(__name__)


def squared_euclidean_distance(a, b):
    b = b.T
    a2 = np.sum(np.square(a), axis=1)
    b2 = np.sum(np.square(b), axis=0)
    ab = np.matmul(a, b)
    d = a2[:, None] - 2 * ab + b2[None, :]
    return d


def color_quantize(x, clusters):
    x = x.reshape(-1, 3)
    d = squared_euclidean_distance(x, clusters)
    return np.argmin(d, axis=1)


class ImageGPTImageProcessor(BaseImageProcessor):
    r"""
    Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution
    (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values"
    (color clusters).

    Args:
        clusters (`np.ndarray` or `List[List[int]]`, *optional*):
            The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overriden by `clusters`
            in `preprocess`.
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by
            `do_resize` in `preprocess`.
        size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
            Size of the image after resizing. Can be overridden by `size` in `preprocess`.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in
            `preprocess`.
        do_color_quantize (`bool`, *optional*, defaults to `True`):
            Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        # clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor
        clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_normalize: bool = True,
        do_color_quantize: bool = True,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 256, "width": 256}
        size = get_size_dict(size)
        self.clusters = np.array(clusters) if clusters is not None else None
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_normalize = do_normalize
        self.do_color_quantize = do_color_quantize

    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
        output_size = (size["height"], size["width"])
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def normalize(
        self,
        image: np.ndarray,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        """
        Normalizes an images' pixel values to between [-1, 1].

        Args:
            image (`np.ndarray`):
                Image to normalize.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
        """
        image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)
        image = image - 1
        return image

    @filter_out_non_signature_kwargs()
    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_normalize: bool = None,
        do_color_quantize: Optional[bool] = None,
        clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        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_normalize=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Size of the image after resizing.
            resample (`int`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
                has an effect if `do_resize` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image
            do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`):
                Whether to color quantize the image.
            clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`):
                Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if
                `do_color_quantize` is set to `True`.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                Only has an effect if `do_color_quantize` is set to `False`.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        size = get_size_dict(size)
        resample = resample if resample is not None else self.resample
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        clusters = clusters if clusters is not None else self.clusters
        clusters = np.array(clusters)

        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."
            )

        # Here, normalize() is using a constant factor to divide pixel values.
        # hence, the method does not need iamge_mean and image_std.
        validate_preprocess_arguments(
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        if do_color_quantize and clusters is None:
            raise ValueError("Clusters must be specified if do_color_quantize is True.")

        # All transformations expect numpy arrays.
        images = [to_numpy_array(image) for image in images]

        if is_scaled_image(images[0]) and do_normalize:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If you wish to do this, "
                "make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].",
            )

        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

        if do_resize:
            images = [
                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        if do_normalize:
            images = [self.normalize(image=image, input_data_format=input_data_format) for image in images]

        if do_color_quantize:
            images = [to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format) for image in images]
            # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
            images = np.array(images)
            images = color_quantize(images, clusters).reshape(images.shape[:-1])

            # flatten to (batch_size, height*width)
            batch_size = images.shape[0]
            images = images.reshape(batch_size, -1)

            # We need to convert back to a list of images to keep consistent behaviour across processors.
            images = list(images)
        else:
            images = [
                to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
                for image in images
            ]

        data = {"input_ids": images}
        return BatchFeature(data=data, tensor_type=return_tensors)