from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
			import torch

_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
			from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






			def    UpperCAmelCase__(						cls  ):



						"""simple docstring"""





						return cls






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_ = None							,	lowerCamelCase_ = None							,	lowerCamelCase_ = None							,	lowerCamelCase_ = None							,	):



			"""simple docstring"""





			_snake_case              =      device
			_snake_case              =      shape[0]

			_snake_case              =      layout or torch.strided
			_snake_case              =      device or torch.device("cpu"  )

			if generator is not None:
						_snake_case              =      generator.device.type if not isinstance(lowerCamelCase_							,	lowerCamelCase_  ) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									_snake_case              =      "cpu"
									if device != "mps":
												logger.info(
												    f'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
												    f' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
												    f' slighly speed up this function by passing a generator that was created on the {device} device.'  )
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'  )

			if isinstance(lowerCamelCase_							,	lowerCamelCase_  ):
						_snake_case              =      (1,) + shape[1:]
						_snake_case              =      [
						    torch.randn(lowerCamelCase_							,	generator=generator[i]							,	device=lowerCamelCase_							,	dtype=lowerCamelCase_							,	layout=lowerCamelCase_  )
						    for i in range(lowerCamelCase_  )
						]
						_snake_case              =      torch.cat(lowerCamelCase_							,	dim=0  ).to(lowerCamelCase_  )
			else:
						_snake_case              =      torch.randn(lowerCamelCase_							,	generator=lowerCamelCase_							,	device=lowerCamelCase_							,	dtype=lowerCamelCase_							,	layout=lowerCamelCase_  ).to(lowerCamelCase_  )

			return latents







def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			if is_torch_version("<"							,	"2.0.0"  ) or not hasattr(lowerCamelCase_							,	"_dynamo"  ):
						return False
			return isinstance(lowerCamelCase_							,	torch._dynamo.eval_frame.OptimizedModule  )


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()

