


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      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		torch.FloatTensor
	_lowerCAmelCase    :		torch.FloatTensor
	_lowerCAmelCase    :		Optional[torch.FloatTensor]        =	None






class        lowerCamelCase      (     _UpperCamelCase       ,	_UpperCamelCase   ):
	_lowerCAmelCase    :		Tuple        =	2


	@register_to_config
	def __init__(						self							,    lowercase__ = 0.0_2							,    lowercase__ = 1_0_0							,    lowercase__ = 1.0_0_7							,    lowercase__ = 8_0							,    lowercase__ = 0.0_5							,    lowercase__ = 5_0							,    ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase :       str  						=  sigma_max

							# setable values
							__UpperCAmelCase :       int  						=  None
							__UpperCAmelCase :       np.IntTensor  						=  None
							__UpperCAmelCase :       torch.FloatTensor  						=  None  # sigma(t_i)


	def    A(						self							,    lowercase__							,    lowercase__ = None):
							return sample


	def    A(						self							,    lowercase__							,    lowercase__ = None):
							__UpperCAmelCase :       str  						=  num_inference_steps
							__UpperCAmelCase :       Optional[int]  						=  np.arange(0							,    self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase :       List[Any]  						=  torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase :       List[Any]  						=  [
							    (
							        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
							]
							__UpperCAmelCase :       Any  						=  torch.tensor(lowercase__							,    dtype=torch.floataa							,    device=lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase :       Optional[Any]  						=  min(self.config.s_churn / self.num_inference_steps							,    2**0.5 - 1)
							else:
													__UpperCAmelCase :       Tuple  						=  0

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

							return sample_hat, sigma_hat


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       Dict  						=  sample_hat + sigma_hat * model_output
							__UpperCAmelCase :       List[str]  						=  (sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase :       Dict  						=  sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       Optional[int]  						=  sample_prev + sigma_prev * model_output
							__UpperCAmelCase :       Optional[Any]  						=  (sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase :       Optional[Any]  						=  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=lowercase__							,    derivative=lowercase__							,    pred_original_sample=lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							raise NotImplementedError()





from string import ascii_uppercase

lowerCAmelCase							=						{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase							=						dict(enumerate(ascii_uppercase))




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  len(lowercase_			)
						__UpperCAmelCase :       int  						=  0
						while True:
												if x == i:
																		__UpperCAmelCase :       List[str]  						=  0
												if len(lowercase_			) == len(lowercase_			):
																		break
												key += key[i]
												i += 1
						return key




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       str  						=  ''''''
						__UpperCAmelCase :       List[str]  						=  0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__UpperCAmelCase :       Optional[int]  						=  (dicta[letter] - dicta[key_new[i]]) % 26
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  ''''''
						__UpperCAmelCase :       List[str]  						=  0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__UpperCAmelCase :       int  						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
																		i += 1
																		or_txt += dicta[x]
						return or_txt




def 						__SCREAMING_SNAKE_CASE       (					)		->    None:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  '''THE GERMAN ATTACK'''
						__UpperCAmelCase :       List[Any]  						=  '''SECRET'''
						__UpperCAmelCase :       Optional[int]  						=  generate_key(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[str]  						=  cipher_text(lowercase_			,					lowercase_			)
						print(f"Encrypted Text = {s}"			)
						print(f"Original Text = {original_text(lowercase_			,					lowercase_			)}"			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()

