
'''simple docstring'''

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class    A     (	unittest.TestCase    ):





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							1
				lowerCAmelCase__								=							3
				lowerCAmelCase__								=							8
				lowerCAmelCase__								=							8

				lowerCAmelCase__								=							torch.rand((batch_size, num_channels, height, width)							)

				return sample





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				if isinstance(image[0]      ,							PIL.Image.Image							):
							return np.stack([np.array(__magic_name__							) for i in image]      ,							axis=0							)
				elif isinstance(__magic_name__      ,							torch.Tensor							):
							return image.cpu().numpy().transpose(0      ,							2      ,							3      ,							1							)
				return image





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""simple docstring"""

				lowerCAmelCase__								=							VaeImageProcessor(do_resize=__magic_name__      ,							do_normalize=__magic_name__							)

				lowerCAmelCase__								=							self.dummy_sample
				lowerCAmelCase__								=							self.to_np(__magic_name__							)

				for output_type in ["pt", "np", "pil"]:
							lowerCAmelCase__								=							image_processor.postprocess(
							    image_processor.preprocess(__magic_name__							)      ,							output_type=__magic_name__      ,							)
							lowerCAmelCase__								=							self.to_np(__magic_name__							)
							lowerCAmelCase__								=							(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), f"""decoded output does not match input for output_type {output_type}"""





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							VaeImageProcessor(do_resize=__magic_name__      ,							do_normalize=__magic_name__							)
				lowerCAmelCase__								=							self.dummy_sample.cpu().numpy().transpose(0      ,							2      ,							3      ,							1							)

				for output_type in ["pt", "np", "pil"]:
							lowerCAmelCase__								=							image_processor.postprocess(image_processor.preprocess(__magic_name__							)      ,							output_type=__magic_name__							)

							lowerCAmelCase__								=							self.to_np(__magic_name__							)
							lowerCAmelCase__								=							(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), f"""decoded output does not match input for output_type {output_type}"""





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__								=							VaeImageProcessor(do_resize=__magic_name__      ,							do_normalize=__magic_name__							)

				lowerCAmelCase__								=							self.dummy_sample.cpu().numpy().transpose(0      ,							2      ,							3      ,							1							)
				lowerCAmelCase__								=							image_processor.numpy_to_pil(__magic_name__							)

				for output_type in ["pt", "np", "pil"]:
							lowerCAmelCase__								=							image_processor.postprocess(image_processor.preprocess(__magic_name__							)      ,							output_type=__magic_name__							)
							for i, o in zip(__magic_name__      ,							__magic_name__							):
										lowerCAmelCase__								=							np.array(__magic_name__							)
										lowerCAmelCase__								=							self.to_np(__magic_name__							) if output_type == "pil" else (self.to_np(__magic_name__							) * 255).round()
										assert (
										    np.abs(in_np - out_np							).max() < 1E-6
										), f"""decoded output does not match input for output_type {output_type}"""





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							VaeImageProcessor(do_resize=__magic_name__      ,							do_normalize=__magic_name__							)

				lowerCAmelCase__								=							self.dummy_sample
				lowerCAmelCase__								=							input_pt_ad.squeeze(0							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)
				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				lowerCAmelCase__								=							self.to_np(self.dummy_sample							)
				lowerCAmelCase__								=							input_np_ad.squeeze(0							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)
				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
				assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6







	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__								=							VaeImageProcessor(do_resize=__magic_name__      ,							do_normalize=__magic_name__							)

				lowerCAmelCase__								=							self.dummy_sample
				lowerCAmelCase__								=							list(__magic_name__							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				lowerCAmelCase__								=							self.to_np(self.dummy_sample							)
				list(__magic_name__							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				lowerCAmelCase__								=							image_processor.postprocess(
				    image_processor.preprocess(__magic_name__							)      ,							output_type="np"      ,							)

				assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
				assert np.abs(out_np_ad - out_np_list							).max() < 1E-6







'''simple docstring'''

def   A     (      UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			return len_board * len_board_column * row + column







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     set[int]						,	)					->       bool:







			'''simple docstring'''



			if board[row][column] != word[word_index]:
						return False

			if word_index == len(UpperCamelCase_      ) - 1:
						return True

			lowerCAmelCase__								=							[(0, 1), (0, -1), (-1, 0), (1, 0)]
			lowerCAmelCase__								=							len(UpperCamelCase_      )
			lowerCAmelCase__								=							len(board[0]      )
			for direction in traverts_directions:
						lowerCAmelCase__								=							row + direction[0]
						lowerCAmelCase__								=							column + direction[1]
						if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
									continue

						lowerCAmelCase__								=							get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )
						if key in visited_points_set:
									continue

						visited_points_set.add(UpperCamelCase_      )
						if exits_word(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	word_index + 1						,	UpperCamelCase_      ):
									return True

						visited_points_set.remove(UpperCamelCase_      )

			return False







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str      )					->       bool:







			'''simple docstring'''



			lowerCAmelCase__								=							(
			    "The board should be a non empty matrix of single chars strings."
			)

			lowerCAmelCase__								=							len(UpperCamelCase_      )
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(UpperCamelCase_      )

			for row in board:
						if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
									raise ValueError(UpperCamelCase_      )

						for item in row:
									if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) != 1:
												raise ValueError(UpperCamelCase_      )

    # Validate word
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(
						    "The word parameter should be a string of length greater than 0."      )

			lowerCAmelCase__								=							len(board[0]      )
			for i in range(UpperCamelCase_      ):
						for j in range(UpperCamelCase_      ):
									if exits_word(
									    UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	0						,	{get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )}      ):
												return True

			return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()





