




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()






"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						UpperCamelCase				:		str				   =			""""""
						UpperCamelCase				:		Dict				   =			""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE     ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0

						# length[i] shows the length of palindromic substring with center i
						UpperCamelCase				:		List[Any]				   =			[1 for i in range(len(SCREAMING_SNAKE_CASE     )     )]

						# for each character in new_string find corresponding palindromic string
						UpperCamelCase				:		Optional[Any]				   =			0
						for j in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		Union[str, Any]				   =			1 if j > r else min(length[l + r - j] // 2							,     r - j + 1     )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE     )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												UpperCamelCase				:		int				   =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		UpperCamelCase				:		int				   =			j - k + 1  # noqa: E741
																		UpperCamelCase				:		List[Any]				   =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		UpperCamelCase				:		List[str]				   =			length[j]
																		UpperCamelCase				:		List[str]				   =			j

    # create that string
						UpperCamelCase				:		Optional[Any]				   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



