






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor





class        a__      (	unittest.TestCase		):






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							1
				__A    =							3
				__A    =							8
				__A    =							8

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

				return sample






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Any:
				if isinstance(image[0]							, PIL.Image.Image						):
						return np.stack([np.array(lowercase__						) for i in image]							, axis=0						)
				elif isinstance(lowercase__							, torch.Tensor						):
						return image.cpu().numpy().transpose(0							, 2							, 3							, 1						)
				return image






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							self.to_np(lowercase__						)

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(
						    image_processor.preprocess(lowercase__						)							, output_type=lowercase__							, )
						__A    =							self.to_np(lowercase__						)
						__A    =							(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 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)
				__A    =							self.dummy_sample.cpu().numpy().transpose(0							, 2							, 3							, 1						)

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

						__A    =							self.to_np(lowercase__						)
						__A    =							(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 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

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

				for output_type in ["pt", "np", "pil"]:
						__A    =							image_processor.postprocess(image_processor.preprocess(lowercase__						)							, output_type=lowercase__						)
						for i, o in zip(lowercase__							, lowercase__						):
								__A    =							np.array(lowercase__						)
								__A    =							self.to_np(lowercase__						) if output_type == "pil" else (self.to_np(lowercase__						) * 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 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

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

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

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

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )
				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, 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 					_lowerCamelCase					(      self						)				->				str:
				__A    =							VaeImageProcessor(do_resize=lowercase__							, do_normalize=lowercase__						)

				__A    =							self.dummy_sample
				__A    =							list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							self.to_np(self.dummy_sample						)
				list(lowercase__						)

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, output_type="np"							, )

				__A    =							image_processor.postprocess(
				    image_processor.preprocess(lowercase__						)							, 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:
 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:

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

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

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

  __A						:							Any      =						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:
 __A						:							Optional[Any]      =						(
     'The board should be a non empty matrix of single chars strings.'
 )

 __A						:							Dict      =						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.'		)

 __A						:							List[Any]      =						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()





