'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  @property
  def  lowerCAmelCase__ (      self			:       Any  )				->Tuple:
        torch.manual_seed(0  )
        UpperCAmelCase_            =  UNetaDModel(
            block_out_channels=(32, 64)  ,							layers_per_block=2  ,							sample_size=32  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,							)
        return model






  def  lowerCAmelCase__ (      self			:       Any  )				->List[Any]:
        UpperCAmelCase_            =  self.dummy_uncond_unet
        UpperCAmelCase_            =  ScoreSdeVeScheduler()

        UpperCAmelCase_            =  ScoreSdeVePipeline(unet=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__  )
        sde_ve.to(UpperCAmelCase__  )
        sde_ve.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sde_ve(num_inference_steps=2  ,							output_type='''numpy'''  ,							generator=UpperCAmelCase__  ).images

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sde_ve(num_inference_steps=2  ,							output_type='''numpy'''  ,							generator=UpperCAmelCase__  ,							return_dict=UpperCAmelCase__  )[
            0
        ]

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]
        UpperCAmelCase_            =  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        UpperCAmelCase_            =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]  )

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







@slow
@require_torch
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Dict  )				->Any:
        UpperCAmelCase_            =  '''google/ncsnpp-church-256'''
        UpperCAmelCase_            =  UNetaDModel.from_pretrained(UpperCAmelCase__  )

        UpperCAmelCase_            =  ScoreSdeVeScheduler.from_pretrained(UpperCAmelCase__  )

        UpperCAmelCase_            =  ScoreSdeVePipeline(unet=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__  )
        sde_ve.to(UpperCAmelCase__  )
        sde_ve.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sde_ve(num_inference_steps=10  ,							output_type='''numpy'''  ,							generator=UpperCAmelCase__  ).images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)

        UpperCAmelCase_            =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]  )

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






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


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

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


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

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            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:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      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(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

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


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





