# coding=utf-8
# Copyright 2021 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,
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"""Feature extractor class for Perceiver."""

from typing import Optional, Union

import numpy as np
from PIL import Image

from transformers.image_utils import PILImageResampling

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ImageFeatureExtractionMixin,
    ImageInput,
    is_torch_tensor,
)
from ...utils import TensorType, logging


logger = logging.get_logger(__name__)


class PerceiverFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
    r"""
    Constructs a Perceiver feature extractor.

    This feature extractor inherits from [`ImageFeatureExtractionMixin`] which contains most of the main methods. Users
    should refer to this superclass for more information regarding those methods.

    Args:
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
            image is padded with 0's and then center cropped.
        crop_size (`int`, *optional*, defaults to 256):
            Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the input to a certain `size`.
        size (`int` or `Tuple(int)`, *optional*, defaults to 224):
            Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an
            integer is provided, then the input will be resized to (size, size). Only has an effect if `do_resize` is
            set to `True`.
        resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`):
            An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
            `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
            `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
            to `True`.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether or not to normalize the input with `image_mean` and `image_std`.
        image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`):
            The sequence of means for each channel, to be used when normalizing images.
        image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`):
            The sequence of standard deviations for each channel, to be used when normalizing images.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_center_crop=True,
        crop_size=256,
        do_resize=True,
        size=224,
        resample=PILImageResampling.BICUBIC,
        do_normalize=True,
        image_mean=None,
        image_std=None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD

    def center_crop(self, image):
        """
        Crops `image` to *self.crop_size* using a center crop. Note that if the image is too small to be cropped to the
        size given, it will be padded (so the returned result has the size asked).

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to resize.
        """

        if isinstance(image, Image.Image):
            image = self.to_numpy_array(image)

        image_height, image_width = image.shape[-2:]

        padded_center_crop_size = (
            (self.size / (self.crop_size)) * np.minimum(image_height, image_width).astype(np.float32)
        ).astype(np.int32)

        offset_height = ((image_height - padded_center_crop_size) + 1) // 2
        offset_width = ((image_width - padded_center_crop_size) + 1) // 2
        crop_window = [offset_height, offset_width, padded_center_crop_size, padded_center_crop_size]

        image = image[
            :, crop_window[0] : crop_window[0] + crop_window[2], crop_window[1] : crop_window[1] + crop_window[3]
        ]

        return image

    def __call__(
        self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several image(s).

        <Tip warning={true}>

        NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
        PIL images.

        </Tip>

        Args:
            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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.

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

            - **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
              width).
        """
        # Input type checking for clearer error
        valid_images = False

        # Check that images has a valid type
        if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
            valid_images = True
        elif isinstance(images, (list, tuple)):
            if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
                valid_images = True

        if not valid_images:
            raise ValueError(
                "Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example),"
                "`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
            )

        is_batched = bool(
            isinstance(images, (list, tuple))
            and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
        )

        if not is_batched:
            images = [images]

        # transformations (center cropping + resizing + normalization)
        if self.do_center_crop and self.crop_size is not None:
            images = [self.center_crop(image) for image in images]
        if self.do_resize and self.size is not None and self.resample is not None:
            images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images]
        if self.do_normalize:
            images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]

        # return as BatchFeature
        data = {"pixel_values": images}
        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)

        return encoded_inputs
