





"""simple docstring"""




from __future__ import annotations

from typing import TypedDict


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str
							lowerCamelCase   :					int






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->list[str]:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("The parameter s type must be str."					)

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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->BWTTransformDict:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("The parameter s type must be str."					)
	if not s:
		raise ValueError("The parameter s must not be empty."					)

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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		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:
		a      :		Tuple	=				int(_lowercase					)
	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(_lowercase					):
		raise ValueError(
		    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

	a      :		Any	=				[""] * len(_lowercase					)
	for _ in range(len(_lowercase					)					):
		for i in range(len(_lowercase					)					):
			a      :		Tuple	=				bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		a						: Dict     	=			'''Provide a string that I will generate its BWT transform: '''
		a						: Any     	=			input(entry_msg).strip()
		a						: str     	=			bwt_transform(s)
		print(
		    F'''Burrows Wheeler transform for string \'{s}\' results '''
		    F'''in \'{result["bwt_string"]}\''''
		)
		a						: int     	=			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}\''''
		)









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


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

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

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

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

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      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							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=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

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=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

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=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






