"""simple docstring"""






from __future__ import annotations




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = False  ,					)							->   tuple[int, float, str]:
				SCREAMING_SNAKE_CASE__		  =				cipher_alphabet or [chr(__UpperCAmelCase       ) 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)
								SCREAMING_SNAKE_CASE__		  =				{
								    "a": 0.08_497,
								    "b": 0.01_492,
								    "c": 0.02_202,
								    "d": 0.04_253,
								    "e": 0.11_162,
								    "f": 0.02_228,
								    "g": 0.02_015,
								    "h": 0.06_094,
								    "i": 0.07_546,
								    "j": 0.00_153,
								    "k": 0.01_292,
								    "l": 0.04_025,
								    "m": 0.02_406,
								    "n": 0.06_749,
								    "o": 0.07_507,
								    "p": 0.01_929,
								    "q": 0.00_095,
								    "r": 0.07_587,
								    "s": 0.06_327,
								    "t": 0.09_356,
								    "u": 0.02_758,
								    "v": 0.00_978,
								    "w": 0.02_560,
								    "x": 0.00_150,
								    "y": 0.01_994,
								    "z": 0.00_077,
								}
				else:
								# Custom frequencies dictionary
								SCREAMING_SNAKE_CASE__		  =				frequencies_dict

				if not case_sensitive:
								SCREAMING_SNAKE_CASE__		  =				ciphertext.lower()

				# Chi squared statistic values
				SCREAMING_SNAKE_CASE__		  =				{}

				# cycle through all of the shifts
				for shift in range(len(__UpperCAmelCase       )       ):
								SCREAMING_SNAKE_CASE__		  =				""

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

								SCREAMING_SNAKE_CASE__		  =				0.0

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

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

																				# Complete the chi squared statistic formula
																				SCREAMING_SNAKE_CASE__		  =				((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
																				SCREAMING_SNAKE_CASE__		  =				decrypted_with_shift.count(__UpperCAmelCase       )

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

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

				SCREAMING_SNAKE_CASE__		  =				min(
				    __UpperCAmelCase  ,					key=__UpperCAmelCase  ,					)

				# Get all the data from the most likely cipher (key, decoded message)
				(
				    (
				    SCREAMING_SNAKE_CASE__
				)  ,       (
				    SCREAMING_SNAKE_CASE__
				)  ,       
				)		  =				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 argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




