




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       42
 lowercase_							       =       42
 lowercase_							       =       None





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       2



 @register_to_config
 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						float = 0.02							,							_lowerCAmelCase    :						float = 100							,							_lowerCAmelCase    :						float = 1.007							,							_lowerCAmelCase    :						float = 80							,							_lowerCAmelCase    :						float = 0.05							,							_lowerCAmelCase    :						float = 50							,							):
       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE_      		=     sigma_max

       # setable values
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None  # sigma(t_i)



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						Optional[int] = None		):
       return sample



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, torch.device] = None		):
       SCREAMING_SNAKE_CASE_      		=     num_inference_steps
       SCREAMING_SNAKE_CASE_      		=     np.arange(0							,							self.num_inference_steps		)[::-1].copy()
       SCREAMING_SNAKE_CASE_      		=     torch.from_numpy(_lowerCAmelCase		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase							,							dtype=torch.floataa							,							device=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						float							,							_lowerCAmelCase    :						Optional[torch.Generator] = None		):
       if self.config.s_min <= sigma <= self.config.s_max:
             SCREAMING_SNAKE_CASE_      		=     min(self.config.s_churn / self.num_inference_steps							,							2**0.5 - 1		)
       else:
             SCREAMING_SNAKE_CASE_      		=     0

       # sample eps ~ N(0, S_noise^2 * I)
       SCREAMING_SNAKE_CASE_      		=     self.config.s_noise * randn_tensor(sample.shape							,							generator=_lowerCAmelCase		).to(sample.device		)
       SCREAMING_SNAKE_CASE_      		=     sigma + gamma * sigma
       SCREAMING_SNAKE_CASE_      		=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						float							,							_lowerCAmelCase    :						float							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						bool = True							,							):
       SCREAMING_SNAKE_CASE_      		=     sample_hat + sigma_hat * model_output
       SCREAMING_SNAKE_CASE_      		=     (sample_hat - pred_original_sample) / sigma_hat
       SCREAMING_SNAKE_CASE_      		=     sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowerCAmelCase							,							derivative=_lowerCAmelCase							,							pred_original_sample=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						float							,							_lowerCAmelCase    :						float							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						torch.FloatTensor							,							_lowerCAmelCase    :						bool = True							,							):
       SCREAMING_SNAKE_CASE_      		=     sample_prev + sigma_prev * model_output
       SCREAMING_SNAKE_CASE_      		=     (sample_prev - pred_original_sample) / sigma_prev
       SCREAMING_SNAKE_CASE_      		=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowerCAmelCase							,							derivative=_lowerCAmelCase							,							pred_original_sample=_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       raise NotImplementedError()





from __future__ import annotations

from typing import TypedDict





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       42
 lowercase_							       =       42


def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					list[str]:
      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    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					BWTTransformDict:
      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.'				)

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

def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int				)   ->					str:
      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:
            SCREAMING_SNAKE_CASE_      		=     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).'				)

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


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