






from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput
class        snake_case_		(			lowercase__):







		def __init__(		self			,     __lowercase			,     __lowercase   )				->   int:
				super().__init__()
				self.register_modules(unet=UpperCAmelCase__			,     scheduler=UpperCAmelCase__   )







		@torch.no_grad()
		def __call__(		self			,     __lowercase = 1			,     __lowercase = None			,     __lowercase = 5_0			,     __lowercase = "pil"			,     __lowercase = True			,     **__lowercase			,     )				->   List[Any]:
				lowerCamelCase   : str								=torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,     generator=UpperCAmelCase__			,     )
				lowerCamelCase   : Dict								=image.to(self.device   )

				# set step values
				self.scheduler.set_timesteps(UpperCAmelCase__   )

				for t in self.progress_bar(self.scheduler.timesteps   ):
						# 1. predict noise model_output
						lowerCamelCase   : Tuple								=self.unet(UpperCAmelCase__			,     UpperCAmelCase__   ).sample

						# 2. predict previous mean of image x_t-1 and add variance depending on eta
						# eta corresponds to η in paper and should be between [0, 1]
						# do x_t -> x_t-1
						lowerCamelCase   : Union[str, Any]								=self.scheduler.step(UpperCAmelCase__			,     UpperCAmelCase__			,     UpperCAmelCase__   ).prev_sample

				lowerCamelCase   : List[Any]								=(image / 2 + 0.5).clamp(0			,     1   )
				lowerCamelCase   : Union[str, Any]								=image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
				if output_type == "pil":
						lowerCamelCase   : Optional[Any]								=self.numpy_to_pil(UpperCAmelCase__   )

				if not return_dict:
						return (image,), "This is a local test"

				return ImagePipelineOutput(images=UpperCAmelCase__   ), "This is a local test"








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = False							,       )   ->     tuple[int, float, str]:
		lowerCamelCase   : Union[str, Any]								=cipher_alphabet or [chr(SCREAMING_SNAKE_CASE_		) for i in range(9_7							,       1_2_3		)]

		# 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)
				lowerCamelCase   : str								={
				    '''a''': 0.0_8_4_9_7,
				    '''b''': 0.0_1_4_9_2,
				    '''c''': 0.0_2_2_0_2,
				    '''d''': 0.0_4_2_5_3,
				    '''e''': 0.1_1_1_6_2,
				    '''f''': 0.0_2_2_2_8,
				    '''g''': 0.0_2_0_1_5,
				    '''h''': 0.0_6_0_9_4,
				    '''i''': 0.0_7_5_4_6,
				    '''j''': 0.0_0_1_5_3,
				    '''k''': 0.0_1_2_9_2,
				    '''l''': 0.0_4_0_2_5,
				    '''m''': 0.0_2_4_0_6,
				    '''n''': 0.0_6_7_4_9,
				    '''o''': 0.0_7_5_0_7,
				    '''p''': 0.0_1_9_2_9,
				    '''q''': 0.0_0_0_9_5,
				    '''r''': 0.0_7_5_8_7,
				    '''s''': 0.0_6_3_2_7,
				    '''t''': 0.0_9_3_5_6,
				    '''u''': 0.0_2_7_5_8,
				    '''v''': 0.0_0_9_7_8,
				    '''w''': 0.0_2_5_6_0,
				    '''x''': 0.0_0_1_5_0,
				    '''y''': 0.0_1_9_9_4,
				    '''z''': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				lowerCamelCase   : Tuple								=frequencies_dict

		if not case_sensitive:
				lowerCamelCase   : Any								=ciphertext.lower()

		# Chi squared statistic values
		lowerCamelCase   : dict[int, tuple[float, str]]								={}

		# cycle through all of the shifts
		for shift in range(len(SCREAMING_SNAKE_CASE_		)		):
				lowerCamelCase   : int								=''''''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								lowerCamelCase   : Dict								=(alphabet_letters.index(letter.lower()		) - shift) % len(
								    SCREAMING_SNAKE_CASE_		)
								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

				lowerCamelCase   : str								=0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								lowerCamelCase   : Union[str, Any]								=letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : List[str]								=decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Optional[Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : Tuple								=((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
										lowerCamelCase   : Optional[int]								=decrypted_with_shift.count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Union[str, Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : int								=((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
				lowerCamelCase   : Dict								=(
				    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(SCREAMING_SNAKE_CASE_		) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		lowerCamelCase   : int								=min(
		    SCREAMING_SNAKE_CASE_							,       key=SCREAMING_SNAKE_CASE_							,       )

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    lowerCamelCase
		)		,   (
		    lowerCamelCase
		)		,   
		)   : Any								=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,
		)

