'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	StableDiffusionSAGPipeline
  lowerCAmelCase__										=	TEXT_TO_IMAGE_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase__										=	TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[Any]:
        torch.manual_seed(0  )
        UpperCAmelCase_            =  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,							layers_per_block=2  ,							sample_size=32  ,							in_channels=4  ,							out_channels=4  ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,							cross_attention_dim=32  ,							)
        UpperCAmelCase_            =  DDIMScheduler(
            beta_start=0.0_0085  ,							beta_end=0.012  ,							beta_schedule='''scaled_linear'''  ,							clip_sample=UpperCAmelCase__  ,							set_alpha_to_one=UpperCAmelCase__  ,							)
        torch.manual_seed(0  )
        UpperCAmelCase_            =  AutoencoderKL(
            block_out_channels=[32, 64]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,							latent_channels=4  ,							)
        torch.manual_seed(0  )
        UpperCAmelCase_            =  CLIPTextConfig(
            bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=32  ,							intermediate_size=37  ,							layer_norm_eps=1e-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1000  ,							)
        UpperCAmelCase_            =  CLIPTextModel(UpperCAmelCase__  )
        UpperCAmelCase_            =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

        UpperCAmelCase_            =  {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple=0  )				->int:
        if str(UpperCAmelCase__  ).startswith('''mps'''  ):
              UpperCAmelCase_            =  torch.manual_seed(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  torch.Generator(device=UpperCAmelCase__  ).manual_seed(UpperCAmelCase__  )
        UpperCAmelCase_            =  {
            '''prompt''': '''.''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 1.0,
            '''sag_scale''': 1.0,
            '''output_type''': '''numpy''',
        }
        return inputs






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







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




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

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

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Any:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

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

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sag_pipe.to(UpperCAmelCase__  )
        sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        UpperCAmelCase_            =  '''.'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sag_pipe(
            [prompt]  ,							width=768  ,							height=512  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							sag_scale=1.0  ,							num_inference_steps=20  ,							output_type='''np'''  ,							)

        UpperCAmelCase_            =  output.images

        assert image.shape == (1, 512, 768, 3)






'''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}\''''
      )





