
'''simple docstring'''







from __future__ import annotations
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[str] | None = None      ,      __SCREAMING_SNAKE_CASE					: dict[str, float] | None = None      ,      __SCREAMING_SNAKE_CASE					: bool = False      ,      ):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       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_	:     Tuple													=       {
												    '''a''': 0.08497,
												    '''b''': 0.01492,
												    '''c''': 0.02202,
												    '''d''': 0.04253,
												    '''e''': 0.11162,
												    '''f''': 0.02228,
												    '''g''': 0.02015,
												    '''h''': 0.06094,
												    '''i''': 0.07546,
												    '''j''': 0.00153,
												    '''k''': 0.01292,
												    '''l''': 0.04025,
												    '''m''': 0.02406,
												    '''n''': 0.06749,
												    '''o''': 0.07507,
												    '''p''': 0.01929,
												    '''q''': 0.00095,
												    '''r''': 0.07587,
												    '''s''': 0.06327,
												    '''t''': 0.09356,
												    '''u''': 0.02758,
												    '''v''': 0.00978,
												    '''w''': 0.02560,
												    '''x''': 0.00150,
												    '''y''': 0.01994,
												    '''z''': 0.00077,
												}
						else:
												# Custom frequencies dictionary
												lowercase_	:     Optional[int]													=       frequencies_dict

						if not case_sensitive:
												lowercase_	:     Optional[int]													=       ciphertext.lower()

						# Chi squared statistic values
						lowercase_	:     Union[str, Any]													=       {}

						# cycle through all of the shifts
						for shift in range(len(__lowerCAmelCase							)							):
												lowercase_	:     Union[str, Any]													=       ''''''

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								lowercase_	:     Dict													=       (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_	:     List[str]													=       0.0

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

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

																														# Complete the chi squared statistic formula
																														lowercase_	:     List[Any]													=       ((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_	:     str													=       decrypted_with_shift.count(__lowerCAmelCase							)

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

																														# Complete the chi squared statistic formula
																														lowercase_	:     List[Any]													=       ((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_	:     Union[str, Any]													=       (
												    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]

						lowercase_	:     List[str]													=       min(
						    __lowerCAmelCase      ,      key=__lowerCAmelCase      ,      )

						# Get all the data from the most likely cipher (key, decoded message)
						(
						    (
						    lowercase_					
						)						, (
						    lowercase_					
						)						, 					
						)	:     List[str]													=       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,
						)







import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

							# Load weights from numpy
							load_tf_weights_in_gpta(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--gpt2_config_file''',
						    default='''''',
						    type=str,
						    help=(
						        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




