"""simple docstring"""




def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							return len_board * len_board_column * row + column






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     ):






							"""simple docstring"""





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

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

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

														A__						  =				get_point_key(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase					)
														if key in visited_points_set:
																					continue

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

														visited_points_set.remove(__lowerCamelCase					)

							return False






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





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

							A__						  =				len(__lowerCamelCase					)
							if not isinstance(__lowerCamelCase							,     __lowerCamelCase					) or len(__lowerCamelCase					) == 0:
														raise ValueError(__lowerCamelCase					)

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

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

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

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

							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a					:				Optional[int]       =      False

@nightly
@require_torch_gpu
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






