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

import cv2
from torch import TensorType
from transformers import DonutImageProcessor
from transformers.image_processing_utils import BatchFeature
from transformers.image_transforms import pad, normalize
from transformers.image_utils import PILImageResampling, ImageInput, ChannelDimension, make_list_of_images, get_image_size
import numpy as np
from PIL import Image
import PIL

from surya.common.s3 import S3DownloaderMixin
from surya.settings import settings

class SuryaEncoderImageProcessor(S3DownloaderMixin, DonutImageProcessor):
    def __init__(self, *args, max_size=None, align_long_axis=False, **kwargs):
        super().__init__(*args, **kwargs)

        self.patch_size = kwargs.get("patch_size", (4, 4))
        self.max_size = max_size
        self.do_align_long_axis = align_long_axis

    @classmethod
    def numpy_resize(cls, image: np.ndarray, size, interpolation=cv2.INTER_LANCZOS4):
        max_width, max_height = size["width"], size["height"]

        resized_image = cv2.resize(image, (max_width, max_height), interpolation=interpolation)
        resized_image = resized_image.transpose(2, 0, 1)

        return resized_image

    def process_inner(self, images: List[np.ndarray]):
        assert images[0].shape[2] == 3 # RGB input images, channel dim last

        if self.do_align_long_axis:
            # Rotate if the bbox is wider than it is tall
            images = [SuryaEncoderImageProcessor.align_long_axis(image, size=self.max_size, input_data_format=ChannelDimension.LAST) for image in images]

            # Verify that the image is wider than it is tall
            for img in images:
                assert img.shape[1] >= img.shape[0]

        # This also applies the right channel dim format, to channel x height x width
        images = [SuryaEncoderImageProcessor.numpy_resize(img, self.max_size, self.resample) for img in images]
        assert images[0].shape[0] == 3 # RGB input images, channel dim first

        # Convert to float32 for rescale/normalize
        images = [img.astype(np.float32) for img in images]

        # Pads with 255 (whitespace)
        # Pad to max size to improve performance
        max_size = self.max_size
        images = [
            SuryaEncoderImageProcessor.pad_image(
                image=image,
                size=max_size,
                input_data_format=ChannelDimension.FIRST,
                pad_value=settings.RECOGNITION_PAD_VALUE
            )
            for image in images
        ]

        # Rescale and normalize
        for idx in range(len(images)):
            images[idx] = (images[idx].astype(np.float64) * self.rescale_factor).astype(np.float32)

        images = [
            SuryaEncoderImageProcessor.normalize(img, mean=self.image_mean, std=self.image_std, input_data_format=ChannelDimension.FIRST)
            for img in images
        ]

        return images

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_thumbnail: bool = None,
        do_align_long_axis: bool = None,
        do_pad: bool = None,
        random_padding: bool = False,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> PIL.Image.Image:
        images = make_list_of_images(images)

        # Convert to numpy for later processing steps
        images = [np.array(img) for img in images]
        images = self.process_inner(images)

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

    @classmethod
    def pad_image(
        cls,
        image: np.ndarray,
        size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        pad_value: float = 0.0,
    ) -> np.ndarray:
        output_height, output_width = size["height"], size["width"]
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)

        delta_width = output_width - input_width
        delta_height = output_height - input_height

        assert delta_width >= 0 and delta_height >= 0

        pad_top = delta_height // 2
        pad_left = delta_width // 2

        pad_bottom = delta_height - pad_top
        pad_right = delta_width - pad_left

        padding = ((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(image, padding, data_format=data_format, input_data_format=input_data_format, constant_values=pad_value)

    @classmethod
    def align_long_axis(
        cls,
        image: np.ndarray,
        size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        input_height, input_width = image.shape[:2]
        output_height, output_width = size["height"], size["width"]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
            image = np.rot90(image, 3)

        return image

    @classmethod
    def normalize(
        cls,
        image: np.ndarray,
        mean: Union[float, Iterable[float]],
        std: Union[float, Iterable[float]],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        return normalize(
            image, mean=mean, std=std, data_format=data_format, input_data_format=input_data_format, **kwargs
        )