'''simple docstring'''
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class lowerCamelCase							(	__lowercase					):




  '''simple docstring'''



  lowerCAmelCase__										=	(EulerDiscreteScheduler,)
  lowerCAmelCase__										=	10






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							**UpperCAmelCase__			:       Any  )				->Union[str, Any]:
        UpperCAmelCase_            =  {
            '''num_train_timesteps''': 1100,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**__A  )
        return config






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Any:
        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__A  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,							[0.0002, 0.002, 0.02]  ):
              self.check_over_configs(beta_start=__A  ,							beta_end=__A  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=__A  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->str:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__A  )






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  self.scheduler_classes[0]
        UpperCAmelCase_            =  self.get_scheduler_config()
        UpperCAmelCase_            =  scheduler_class(**__A  )

        scheduler.set_timesteps(self.num_inference_steps  )

        UpperCAmelCase_            =  torch.manual_seed(0  )

        UpperCAmelCase_            =  self.dummy_model()
        UpperCAmelCase_            =  self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase_            =  sample.to(__A  )

        for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_            =  scheduler.scale_model_input(__A  ,							__A  )

              UpperCAmelCase_            =  model(__A  ,							__A  )

              UpperCAmelCase_            =  scheduler.step(__A  ,							__A  ,							__A  ,							generator=__A  )
              UpperCAmelCase_            =  output.prev_sample

        UpperCAmelCase_            =  torch.sum(torch.abs(__A  )  )
        UpperCAmelCase_            =  torch.mean(torch.abs(__A  )  )

        assert abs(result_sum.item() - 10.0807  ) < 1e-2
        assert abs(result_mean.item() - 0.0131  ) < 1e-3






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  self.scheduler_classes[0]
        UpperCAmelCase_            =  self.get_scheduler_config(prediction_type='''v_prediction'''  )
        UpperCAmelCase_            =  scheduler_class(**__A  )

        scheduler.set_timesteps(self.num_inference_steps  )

        UpperCAmelCase_            =  torch.manual_seed(0  )

        UpperCAmelCase_            =  self.dummy_model()
        UpperCAmelCase_            =  self.dummy_sample_deter * scheduler.init_noise_sigma
        UpperCAmelCase_            =  sample.to(__A  )

        for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_            =  scheduler.scale_model_input(__A  ,							__A  )

              UpperCAmelCase_            =  model(__A  ,							__A  )

              UpperCAmelCase_            =  scheduler.step(__A  ,							__A  ,							__A  ,							generator=__A  )
              UpperCAmelCase_            =  output.prev_sample

        UpperCAmelCase_            =  torch.sum(torch.abs(__A  )  )
        UpperCAmelCase_            =  torch.mean(torch.abs(__A  )  )

        assert abs(result_sum.item() - 0.0002  ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06  ) < 1e-3






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        UpperCAmelCase_            =  self.scheduler_classes[0]
        UpperCAmelCase_            =  self.get_scheduler_config()
        UpperCAmelCase_            =  scheduler_class(**__A  )

        scheduler.set_timesteps(self.num_inference_steps  ,							device=__A  )

        UpperCAmelCase_            =  torch.manual_seed(0  )

        UpperCAmelCase_            =  self.dummy_model()
        UpperCAmelCase_            =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        UpperCAmelCase_            =  sample.to(__A  )

        for t in scheduler.timesteps:
              UpperCAmelCase_            =  scheduler.scale_model_input(__A  ,							__A  )

              UpperCAmelCase_            =  model(__A  ,							__A  )

              UpperCAmelCase_            =  scheduler.step(__A  ,							__A  ,							__A  ,							generator=__A  )
              UpperCAmelCase_            =  output.prev_sample

        UpperCAmelCase_            =  torch.sum(torch.abs(__A  )  )
        UpperCAmelCase_            =  torch.mean(torch.abs(__A  )  )

        assert abs(result_sum.item() - 10.0807  ) < 1e-2
        assert abs(result_mean.item() - 0.0131  ) < 1e-3






  def  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_            =  self.scheduler_classes[0]
        UpperCAmelCase_            =  self.get_scheduler_config()
        UpperCAmelCase_            =  scheduler_class(**__A  ,							use_karras_sigmas=__A  )

        scheduler.set_timesteps(self.num_inference_steps  ,							device=__A  )

        UpperCAmelCase_            =  torch.manual_seed(0  )

        UpperCAmelCase_            =  self.dummy_model()
        UpperCAmelCase_            =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        UpperCAmelCase_            =  sample.to(__A  )

        for t in scheduler.timesteps:
              UpperCAmelCase_            =  scheduler.scale_model_input(__A  ,							__A  )

              UpperCAmelCase_            =  model(__A  ,							__A  )

              UpperCAmelCase_            =  scheduler.step(__A  ,							__A  ,							__A  ,							generator=__A  )
              UpperCAmelCase_            =  output.prev_sample

        UpperCAmelCase_            =  torch.sum(torch.abs(__A  )  )
        UpperCAmelCase_            =  torch.mean(torch.abs(__A  )  )

        assert abs(result_sum.item() - 124.52_2994_9951_1719  ) < 1e-2
        assert abs(result_mean.item() - 0.1_6213_9326_3339_9963  ) < 1e-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}\''''
      )





