
"""simple docstring"""




from __future__ import annotations

from typing import TypedDict





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







def        _lowerCamelCase ( __a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter s type must be str.'''    )

		return [s[i:] + s[:i] for i in range(len(__a    )    )]



def        _lowerCamelCase ( __a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter s type must be str.'''    )
		if not s:
				raise ValueError('''The parameter s must not be empty.'''    )

		SCREAMING_SNAKE_CASE_						 = all_rotations(__a    )
		rotations.sort()  # sort the list of rotations in alphabetically order
		# make a string composed of the last char of each rotation
		SCREAMING_SNAKE_CASE_						 = {
		    '''bwt_string''': ''''''.join([word[-1] for word in rotations]    ),
		    '''idx_original_string''': rotations.index(__a    ),
		}
		return response



def        _lowerCamelCase ( __a,							__a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter bwt_string type must be str.'''    )
		if not bwt_string:
				raise ValueError('''The parameter bwt_string must not be empty.'''    )
		try:
				SCREAMING_SNAKE_CASE_						 = int(__a    )
		except ValueError:
				raise TypeError(
				    '''The parameter idx_original_string type must be int or passive'''
				    ''' of cast to int.'''    )
		if idx_original_string < 0:
				raise ValueError('''The parameter idx_original_string must not be lower than 0.'''    )
		if idx_original_string >= len(__a    ):
				raise ValueError(
				    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''    )

		SCREAMING_SNAKE_CASE_						 = [''''''] * len(__a    )
		for _ in range(len(__a    )    ):
				for i in range(len(__a    )    ):
						SCREAMING_SNAKE_CASE_						 = bwt_string[i] + ordered_rotations[i]
				ordered_rotations.sort()
		return ordered_rotations[idx_original_string]


if __name__ == "__main__":
						lowerCAmelCase__							=				'Provide a string that I will generate its BWT transform: '
						lowerCAmelCase__							=				input(entry_msg).strip()
						lowerCAmelCase__							=				bwt_transform(s)
						print(
						    f'''Burrows Wheeler transform for string \'{s}\' results '''
						    f'''in \'{result['bwt_string']}\''''
						)
						lowerCAmelCase__							=				reverse_bwt(result['bwt_string'], result['idx_original_string'])
						print(
						    f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
						    f'''we get original string \'{original_string}\''''
						)

"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCAmelCase__							=				False





class  snake_case     (							unittest.TestCase   ):
							pass







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt='''first prompt'''       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='''numpy'''       ,       ).images

									with tempfile.TemporaryDirectory() as tmpdirname:
											pipe.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = generator.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt='''first prompt'''       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''cyberpunk 2077'''
									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.dual_guided(
									    prompt=SCREAMING_SNAKE_CASE_       ,       image=SCREAMING_SNAKE_CASE_       ,       text_to_image_strength=0.75       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='''numpy'''       ,       ).images

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

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]    )

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

									SCREAMING_SNAKE_CASE_						 = '''A painting of a squirrel eating a burger '''
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.text_to_image(
									    prompt=SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='''numpy'''    ).images

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

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]    )

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

									SCREAMING_SNAKE_CASE_						 = pipe.image_variation(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       output_type='''numpy'''    ).images

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

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]    )

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