# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Image processor class for MobileViT."""

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    flip_channel_order,
    get_resize_output_image_size,
    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, is_torch_tensor, is_vision_available, logging


if is_vision_available():
    import PIL

if is_torch_available():
    import torch


logger = logging.get_logger(__name__)


class MobileViTImageProcessor(BaseImageProcessor):
    r"""
    Constructs a MobileViT image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
            `do_resize` parameter in the `preprocess` method.
        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
            Controls the size of the output image after resizing. Can be overridden by the `size` parameter in the
            `preprocess` method.
        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter
            in the `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
            parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
            `preprocess` method.
        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. Can be overridden by the `do_center_crop` parameter in
            the `preprocess` method.
        crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`):
            Desired output size `(size["height"], size["width"])` when applying center-cropping. Can be overridden by
            the `crop_size` parameter in the `preprocess` method.
        do_flip_channel_order (`bool`, *optional*, defaults to `True`):
            Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order`
            parameter in the `preprocess` method.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_center_crop: bool = True,
        crop_size: Dict[str, int] = None,
        do_flip_channel_order: bool = True,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"shortest_edge": 224}
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256}
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_flip_channel_order = do_flip_channel_order

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PIL.Image.BILINEAR,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Controls the size of the output image. The shortest edge of the image will be resized to
                `size["shortest_edge"]` while maintaining the aspect ratio.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                Resampling filter to use when resiizing the image.
            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.
        """
        size = get_size_dict(size, default_to_square=False)
        if "shortest_edge" not in size:
            raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
        output_size = get_resize_output_image_size(image, size=size["shortest_edge"], default_to_square=False)
        return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)

    def flip_channel_order(
        self, image: np.ndarray, data_format: Optional[Union[str, ChannelDimension]] = None
    ) -> np.ndarray:
        """
        Flip the color channels from RGB to BGR or vice versa.

        Args:
            image (`np.ndarray`):
                The image, represented as a numpy array.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
        """
        return flip_channel_order(image, data_format=data_format)

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_flip_channel_order: bool = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        **kwargs,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess.
            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_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image by rescale factor.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
                Whether to center crop the image.
            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
                Size of the center crop if `do_center_crop` is set to `True`.
            do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`):
                Whether to flip the channel order of the image.
            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.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        resample = resample if resample is not None else self.resample
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
        do_flip_channel_order = (
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        size = size if size is not None else self.size
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else self.crop_size
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        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."
            )

        if do_resize and size is None:
            raise ValueError("Size must be specified if do_resize is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True.")

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

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

        if do_center_crop:
            images = [self.center_crop(image=image, size=crop_size) for image in images]

        if do_rescale:
            images = [self.rescale(image=image, scale=rescale_factor) for image in images]

        # the pretrained checkpoints assume images are BGR, not RGB
        if do_flip_channel_order:
            images = [self.flip_channel_order(image=image) for image in images]

        images = [to_channel_dimension_format(image, data_format) for image in images]

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

    def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
        """
        Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports
        PyTorch.

        Args:
            outputs ([`MobileViTForSemanticSegmentation`]):
                Raw outputs of the model.
            target_sizes (`List[Tuple]`, *optional*):
                A list of length `batch_size`, where each item is a `Tuple[int, int]` corresponding to the requested
                final size (height, width) of each prediction. If left to None, predictions will not be resized.

        Returns:
            `List[torch.Tensor]`:
                A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
                corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
                `torch.Tensor` correspond to a semantic class id.
        """
        # TODO: add support for other frameworks
        logits = outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
            if len(logits) != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

            if is_torch_tensor(target_sizes):
                target_sizes = target_sizes.numpy()

            semantic_segmentation = []

            for idx in range(len(logits)):
                resized_logits = torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
                )
                semantic_map = resized_logits[0].argmax(dim=0)
                semantic_segmentation.append(semantic_map)
        else:
            semantic_segmentation = logits.argmax(dim=1)
            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation
