



'''simple docstring'''



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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

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

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

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





class _UpperCamelCase		(     A      ,       A		):





				'''simple docstring'''





				@register_to_config
				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  str = "fixed_small_log"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[float] = 1.0  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "squaredcos_cap_v2"  ,       ):







											'''simple docstring'''

											if beta_schedule != "squaredcos_cap_v2":
																		raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

											__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)

											__lowercase       	=1.0 - self.betas
											__lowercase       	=torch.cumprod(self.alphas  ,       dim=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       	=variance_type

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None):







											'''simple docstring'''

											return sample

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None):







											'''simple docstring'''

											__lowercase       	=num_inference_steps
											__lowercase       	=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if prev_timestep is None:
																		__lowercase       	=t - 1

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

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / 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
											__lowercase       	=beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																		__lowercase       	=self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																		__lowercase       	=torch.log(torch.clamp(_lowerCAmelCase  ,       min=1e-20))
																		__lowercase       	=torch.exp(0.5 * variance)
											elif variance_type == "learned_range":
																		# NOTE difference with DDPM scheduler
																		__lowercase       	=variance.log()
																		__lowercase       	=beta.log()

																		__lowercase       	=(predicted_variance + 1) / 2
																		__lowercase       	=frac * max_log + (1 - frac) * min_log

											return variance

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																		__lowercase	,				__lowercase       	=torch.split(_lowerCAmelCase  ,       sample.shape[1]  ,       dim=1)
											else:
																		__lowercase       	=None

											# 1. compute alphas, betas
											if prev_timestep is None:
																		__lowercase       	=t - 1

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

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
																		__lowercase       	=self.alphas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev
																		__lowercase       	=1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		__lowercase       	=model_output
											else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																		    ' for the UnCLIPScheduler.')

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		__lowercase       	=torch.clamp(
																		    _lowerCAmelCase  ,       -self.config.clip_sample_range  ,       self.config.clip_sample_range)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowercase       	=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											__lowercase       	=0
											if t > 0:
																		__lowercase       	=randn_tensor(
																		    model_output.shape  ,       dtype=model_output.dtype  ,       generator=_lowerCAmelCase  ,       device=model_output.device)

																		__lowercase       	=self._get_variance(
																		    _lowerCAmelCase  ,       predicted_variance=_lowerCAmelCase  ,       prev_timestep=_lowerCAmelCase  ,       )

																		if self.variance_type == "fixed_small_log":
																									__lowercase       	=variance
																		elif self.variance_type == "learned_range":
																									__lowercase       	=(0.5 * variance).exp()
																		else:
																									raise ValueError(
																									    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																									    ' for the UnCLIPScheduler.')

																		__lowercase       	=variance * variance_noise

											__lowercase       	=pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=_lowerCAmelCase  ,       pred_original_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.IntTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.alphas_cumprod.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											__lowercase       	=timesteps.to(original_samples.device)

											__lowercase       	=alphas_cumprod[timesteps] ** 0.5
											__lowercase       	=sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_alpha_prod.unsqueeze(-1)

											__lowercase       	=(1 - alphas_cumprod[timesteps]) ** 0.5
											__lowercase       	=sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_one_minus_alpha_prod.unsqueeze(-1)

											__lowercase       	=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples








'''simple docstring'''



from __future__ import annotations



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = False  ,   ):



							"""simple docstring"""



							__lowercase       	=cipher_alphabet or [chr(_lowerCAmelCase					) for i in range(97  ,   123					)]

							# If the argument is None or the user provided an empty dictionary
							if not frequencies_dict:
														# Frequencies of letters in the english language (how much they show up)
														__lowercase       	={
														    'a': 0.0_84_97,
														    'b': 0.0_14_92,
														    'c': 0.0_22_02,
														    'd': 0.0_42_53,
														    'e': 0.1_11_62,
														    'f': 0.0_22_28,
														    'g': 0.0_20_15,
														    'h': 0.0_60_94,
														    'i': 0.0_75_46,
														    'j': 0.0_01_53,
														    'k': 0.0_12_92,
														    'l': 0.0_40_25,
														    'm': 0.0_24_06,
														    'n': 0.0_67_49,
														    'o': 0.0_75_07,
														    'p': 0.0_19_29,
														    'q': 0.0_00_95,
														    'r': 0.0_75_87,
														    's': 0.0_63_27,
														    't': 0.0_93_56,
														    'u': 0.0_27_58,
														    'v': 0.0_09_78,
														    'w': 0.0_25_60,
														    'x': 0.0_01_50,
														    'y': 0.0_19_94,
														    'z': 0.0_00_77,
														}
							else:
														# Custom frequencies dictionary
														__lowercase       	=frequencies_dict

							if not case_sensitive:
														__lowercase       	=ciphertext.lower()

							# Chi squared statistic values
							__lowercase       	={}

							# cycle through all of the shifts
							for shift in range(len(_lowerCAmelCase					)					):
														__lowercase       	=''

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												__lowercase       	=(alphabet_letters.index(letter.lower()					) - shift) % len(
																												    _lowerCAmelCase					)
																												decrypted_with_shift += (
																												    alphabet_letters[new_key].upper()
																												    if case_sensitive and letter.isupper()
																												    else alphabet_letters[new_key]
																												)
																					except ValueError:
																												# Append the character if it isn't in the alphabet
																												decrypted_with_shift += letter

														__lowercase       	=0.0

														# Loop through each letter in the decoded message with the shift
														for letter in decrypted_with_shift:
																					if case_sensitive:
																												__lowercase       	=letter.lower()
																												if letter in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase       	=decrypted_with_shift.lower().count(_lowerCAmelCase					)

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase       	=frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value
																					else:
																												if letter.lower() in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase       	=decrypted_with_shift.count(_lowerCAmelCase					)

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase       	=frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
														__lowercase       	=(
														    chi_squared_statistic,
														    decrypted_with_shift,
														)

							# Get the most likely cipher by finding the cipher with the smallest chi squared
							# statistic
							def chi_squared_statistic_values_sorting_key(_lowerCAmelCase					) -> tuple[float, str]:
														return chi_squared_statistic_values[key]

							__lowercase       	=min(
							    _lowerCAmelCase  ,   key=_lowerCAmelCase  ,   )

							# Get all the data from the most likely cipher (key, decoded message)
							(
							    (
							    __lowercase
							)	,				(
							    __lowercase
							)	,				
							)       	=chi_squared_statistic_values[most_likely_cipher]

							# Return the data on the most likely shift
							return (
							    most_likely_cipher,
							    most_likely_cipher_chi_squared_value,
							    decoded_most_likely_cipher,
							)



