


'''simple docstring'''



from __future__ import annotations






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   list[str] | None = None						,      _SCREAMING_SNAKE_CASE      :   dict[str, float] | None = None						,      _SCREAMING_SNAKE_CASE      :   bool = False						,      ):
					__a			:       str					    =  cipher_alphabet or [chr(_SCREAMING_SNAKE_CASE   ) 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)
										__a			:       int					    =  {
										    '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
										__a			:       int					    =  frequencies_dict

					if not case_sensitive:
										__a			:       Dict					    =  ciphertext.lower()

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

					# cycle through all of the shifts
					for shift in range(len(_SCREAMING_SNAKE_CASE   )   ):
										__a			:       Optional[Any]					    =  ''

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				__a			:       Any					    =  (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

										__a			:       Union[str, Any]					    =  0.0

										# Loop through each letter in the decoded message with the shift
										for letter in decrypted_with_shift:
															if case_sensitive:
																				__a			:       Any					    =  letter.lower()
																				if letter in frequencies:
																									# Get the amount of times the letter occurs in the message
																									__a			:       Optional[int]					    =  decrypted_with_shift.lower().count(_SCREAMING_SNAKE_CASE   )

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

																									# Complete the chi squared statistic formula
																									__a			:       Optional[int]					    =  ((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
																									__a			:       Optional[Any]					    =  decrypted_with_shift.count(_SCREAMING_SNAKE_CASE   )

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

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

					__a			:       int					    =  min(
					    _SCREAMING_SNAKE_CASE						,      key=_SCREAMING_SNAKE_CASE						,      )

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    __a
					)    ,				(
					    __a
					)    ,				
					)			:       Optional[int]					    =  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,
					)







'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



