



"""simple docstring"""

from __future__ import annotations

from typing import TypedDict




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42







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

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





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

							__a		:		int									=       all_rotations(_lowerCamelCase					)
							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(_lowerCamelCase					),
							}
							return response





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							if not isinstance(_lowerCamelCase  ,       _lowerCamelCase					):
														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		:		int									=       int(_lowerCamelCase					)
							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(_lowerCamelCase					):
														raise ValueError(
														    """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""					)

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


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

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

