
import math
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      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     torch.FloatTensor
						UpperCamelCase			:     torch.FloatTensor




def    __snake_case       (  __magic_name__		,						__magic_name__=0.9_9_9		,						__magic_name__="cosine"		,						):







		'''simple docstring'''


		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__magic_name__	):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__magic_name__	):
						return math.exp(t * -12.0	)

		else:
				raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

		lowercase						    =			[]
		for i in range(__magic_name__	):
				lowercase						    =			i / num_diffusion_timesteps
				lowercase						    =			(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__magic_name__	) / alpha_bar_fn(__magic_name__	)		,						__magic_name__	)	)
		return torch.tensor(__magic_name__		,						dtype=torch.floataa	)





class      UpperCamelCase_			(			__a		,						__a		):







						'''simple docstring'''



						UpperCamelCase			:     Tuple							      =				1





						@register_to_config
						def __init__( self       :Dict		,						lowerCAmelCase__       :Union[str, Any] = 1000		,						lowerCAmelCase__       :Optional[Any] = 0.00_01		,						lowerCAmelCase__       :str = 0.02		,						lowerCAmelCase__       :Optional[int] = "linear"		,						lowerCAmelCase__       :Any = 0.0		,						lowerCAmelCase__       :Tuple = None		,						lowerCAmelCase__       :Union[str, Any] = True		,						)     ->Optional[Any]:






								if trained_betas is not None:
										lowercase						    =			torch.from_numpy(lowerCAmelCase__    )
								elif beta_schedule == "linear":
										lowercase						    =			torch.linspace(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										lowercase						    =			(
										    torch.linspace(beta_start**0.5		,						beta_end**0.5		,						lowerCAmelCase__		,						dtype=torch.floataa    ) ** 2
										)
								elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										lowercase						    =			betas_for_alpha_bar(lowerCAmelCase__    )
								elif beta_schedule == "sigmoid":
										# GeoDiff sigmoid schedule
										lowercase						    =			torch.linspace(-6		,						6		,						lowerCAmelCase__    )
										lowercase						    =			torch.sigmoid(lowerCAmelCase__    ) * (beta_end - beta_start) + beta_start
								else:
										raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

								lowercase						    =			1.0 - self.betas
								lowercase						    =			torch.cumprod(self.alphas		,						dim=0    )
								lowercase						    =			torch.tensor(1.0    )

								lowercase						    =			torch.tensor(1.0    )

								# standard deviation of the initial noise distribution
								lowercase						    =			1.0

								# setable values
								lowercase						    =			None
								lowercase						    =			torch.from_numpy(np.arange(0		,						lowerCAmelCase__    )[::-1].copy()    )

								lowercase						    =			eta





						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Tuple = None    )     ->torch.FloatTensor:
								return sample





						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Any = 10		,						lowerCAmelCase__       :Tuple = 10		,						lowerCAmelCase__       :Dict = None		,						)     ->Optional[int]:
								lowercase						    =			min(self.config.num_train_timesteps		,						lowerCAmelCase__    )
								lowercase						    =			num_inference_steps

								lowercase						    =			[]

								lowercase						    =			{}
								for j in range(0		,						num_inference_steps - jump_length		,						lowerCAmelCase__    ):
										lowercase						    =			jump_n_sample - 1

								lowercase						    =			num_inference_steps
								while t >= 1:
										lowercase						    =			t - 1
										timesteps.append(lowerCAmelCase__    )

										if jumps.get(lowerCAmelCase__		,						0    ) > 0:
												lowercase						    =			jumps[t] - 1
												for _ in range(lowerCAmelCase__    ):
														lowercase						    =			t + 1
														timesteps.append(lowerCAmelCase__    )

								lowercase						    =			np.array(lowerCAmelCase__    ) * (self.config.num_train_timesteps // self.num_inference_steps)
								lowercase						    =			torch.from_numpy(lowerCAmelCase__    ).to(lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :List[Any]    )     ->Union[str, Any]:
								lowercase						    =			t - self.config.num_train_timesteps // self.num_inference_steps

								lowercase						    =			self.alphas_cumprod[t]
								lowercase						    =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								lowercase						    =			1 - alpha_prod_t
								lowercase						    =			1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								lowercase						    =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Dict = None		,						lowerCAmelCase__       :List[Any] = True		,						)     ->Union[RePaintSchedulerOutput, Tuple]:
								lowercase						    =			timestep
								lowercase						    =			timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								lowercase						    =			self.alphas_cumprod[t]
								lowercase						    =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								lowercase						    =			1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								lowercase						    =			(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
										lowercase						    =			torch.clamp(lowerCAmelCase__		,						-1		,						1    )

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								lowercase						    =			model_output.device
								lowercase						    =			randn_tensor(model_output.shape		,						generator=lowerCAmelCase__		,						device=lowerCAmelCase__		,						dtype=model_output.dtype    )
								lowercase						    =			self.eta * self._get_variance(lowerCAmelCase__    ) ** 0.5

								lowercase						    =			0
								if t > 0 and self.eta > 0:
										lowercase						    =			std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								lowercase						    =			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								lowercase						    =			alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								lowercase						    =			(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								lowercase						    =			mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
										return (
										    pred_prev_sample,
										    pred_original_sample,
										)

								return RePaintSchedulerOutput(prev_sample=lowerCAmelCase__		,						pred_original_sample=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :List[Any]=None    )     ->str:
								lowercase						    =			self.config.num_train_timesteps // self.num_inference_steps

								for i in range(lowerCAmelCase__    ):
										lowercase						    =			self.betas[timestep + i]
										if sample.device.type == "mps":
												# randn does not work reproducibly on mps
												lowercase						    =			randn_tensor(sample.shape		,						dtype=sample.dtype		,						generator=lowerCAmelCase__    )
												lowercase						    =			noise.to(sample.device    )
										else:
												lowercase						    =			randn_tensor(sample.shape		,						generator=lowerCAmelCase__		,						device=sample.device		,						dtype=sample.dtype    )

										# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
										lowercase						    =			(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :Optional[int]		,						)     ->torch.FloatTensor:
								raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."    )





						def __len__( self       :List[Any]    )     ->Optional[Any]:
								return self.config.num_train_timesteps


_snake_case			:       List[Any]      =						{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				return [alphabet[char] for char in key.upper()]

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			""
				lowercase						    =			0
				lowercase						    =			generate_table(__magic_name__	)
				for char in words.upper():
								cipher += get_opponent(table[count]		,						__magic_name__	)
								lowercase						    =			(count + 1) % len(__magic_name__	)
				return cipher

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return encrypt(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			0 if char in table[0] else 1
				lowercase						    =			table[row].index(__magic_name__	)
				return row, col

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase    ,  lowercase						    =			get_position(__magic_name__		,						char.upper()	)
				if row == 1:
								return table[0][col]
				else:
								return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						_snake_case			:       List[Any]      =						input("Enter key: ").strip()
						_snake_case			:       Optional[int]      =						input("Enter text to encrypt: ").strip()
						_snake_case			:       Optional[Any]      =						encrypt(key, text)

						print(F"Encrypted: {cipher_text}")
						print(F"Decrypted with key: {decrypt(key, cipher_text)}")
