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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE:						Dict    	=       logging.get_logger(__name__)


if is_vision_available():
			import PIL

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      ['''pixel_values''']



		def __init__(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				Dict[str, int] = None  ,					__SCREAMING_SNAKE_CASE    :				PILImageResampling = PILImageResampling.BICUBIC  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				Dict[str, int] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				Union[int, float] = 1 / 255  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[float, List[float]]] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[float, List[float]]] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					**__SCREAMING_SNAKE_CASE    :				int  ,					):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE   )
					_snake_case              =      size if size is not None else {"shortest_edge": 224}
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE  ,					default_to_square=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      crop_size if crop_size is not None else {"height": 224, "width": 224}
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      do_resize
					_snake_case              =      size
					_snake_case              =      resample
					_snake_case              =      do_center_crop
					_snake_case              =      crop_size
					_snake_case              =      do_rescale
					_snake_case              =      rescale_factor
					_snake_case              =      do_normalize
					_snake_case              =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					_snake_case              =      image_std if image_std is not None else OPENAI_CLIP_STD
					_snake_case              =      do_convert_rgb



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				np.ndarray  ,					__SCREAMING_SNAKE_CASE    :				Dict[str, int]  ,					__SCREAMING_SNAKE_CASE    :				PILImageResampling = PILImageResampling.BICUBIC  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[str, ChannelDimension]] = None  ,					**__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					):






					"""simple docstring"""
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE  ,					default_to_square=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      get_resize_output_image_size(
					    __SCREAMING_SNAKE_CASE  ,					size=(size["height"], size["width"])  ,					default_to_square=__SCREAMING_SNAKE_CASE   )
					return resize(__SCREAMING_SNAKE_CASE  ,					size=__SCREAMING_SNAKE_CASE  ,					resample=__SCREAMING_SNAKE_CASE  ,					data_format=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				np.ndarray  ,					__SCREAMING_SNAKE_CASE    :				Dict[str, int]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[str, ChannelDimension]] = None  ,					**__SCREAMING_SNAKE_CASE    :				List[Any]  ,					):






					"""simple docstring"""
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE   )
					return center_crop(__SCREAMING_SNAKE_CASE  ,					size=(size["height"], size["width"])  ,					data_format=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				np.ndarray  ,					__SCREAMING_SNAKE_CASE    :				Union[int, float]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[str, ChannelDimension]] = None  ,					**__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE  ,					scale=__SCREAMING_SNAKE_CASE  ,					data_format=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				np.ndarray  ,					__SCREAMING_SNAKE_CASE    :				Union[float, List[float]]  ,					__SCREAMING_SNAKE_CASE    :				Union[float, List[float]]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[str, ChannelDimension]] = None  ,					**__SCREAMING_SNAKE_CASE    :				List[Any]  ,					):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE  ,					mean=__SCREAMING_SNAKE_CASE  ,					std=__SCREAMING_SNAKE_CASE  ,					data_format=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )







		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				ImageInput  ,					__SCREAMING_SNAKE_CASE    :				bool = None  ,					__SCREAMING_SNAKE_CASE    :				Dict[str, int] = None  ,					__SCREAMING_SNAKE_CASE    :				PILImageResampling = None  ,					__SCREAMING_SNAKE_CASE    :				bool = None  ,					__SCREAMING_SNAKE_CASE    :				int = None  ,					__SCREAMING_SNAKE_CASE    :				bool = None  ,					__SCREAMING_SNAKE_CASE    :				float = None  ,					__SCREAMING_SNAKE_CASE    :				bool = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[float, List[float]]] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[float, List[float]]] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Union[str, TensorType]] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[ChannelDimension] = ChannelDimension.FIRST  ,					**__SCREAMING_SNAKE_CASE    :				int  ,					):






					"""simple docstring"""
					_snake_case              =      do_resize if do_resize is not None else self.do_resize
					_snake_case              =      size if size is not None else self.size
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE  ,					default_to_square=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      resample if resample is not None else self.resample
					_snake_case              =      do_center_crop if do_center_crop is not None else self.do_center_crop
					_snake_case              =      crop_size if crop_size is not None else self.crop_size
					_snake_case              =      get_size_dict(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      do_rescale if do_rescale is not None else self.do_rescale
					_snake_case              =      rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case              =      do_normalize if do_normalize is not None else self.do_normalize
					_snake_case              =      image_mean if image_mean is not None else self.image_mean
					_snake_case              =      image_std if image_std is not None else self.image_std
					_snake_case              =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					_snake_case              =      make_list_of_images(__SCREAMING_SNAKE_CASE   )

					if not valid_images(__SCREAMING_SNAKE_CASE   ):
								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_center_crop and crop_size is None:
								raise ValueError("Crop size must be specified if do_center_crop is True."   )

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

					if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("Image mean and std must be specified if do_normalize is True."   )

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
								_snake_case              =      [convert_to_rgb(__SCREAMING_SNAKE_CASE   ) for image in images]

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

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

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

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

					if do_normalize:
								_snake_case              =      [self.normalize(image=__SCREAMING_SNAKE_CASE  ,					mean=__SCREAMING_SNAKE_CASE  ,					std=__SCREAMING_SNAKE_CASE   ) for image in images]

					_snake_case              =      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ) for image in images]

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


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

