




from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					list[str] | None = None  ,			A__       :					dict[str, float] | None = None  ,			A__       :					bool = False  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						cipher_alphabet or [chr(A__	) 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)
              __lowerCamelCase   =						{
                  """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
              __lowerCamelCase   =						frequencies_dict

       if not case_sensitive:
              __lowerCamelCase   =						ciphertext.lower()

       # Chi squared statistic values
       __lowerCamelCase   =						{}

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

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            __lowerCamelCase   =						(alphabet_letters.index(letter.lower()	) - shift) % len(
                                A__	)
                            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   =						0.0

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

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

                                   # Complete the chi squared statistic formula
                                   __lowerCamelCase   =						((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   =						decrypted_with_shift.count(A__	)

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

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

       __lowerCamelCase   =						min(
           A__  ,			key=A__  ,			)

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           __lowerCamelCase
       ),   (
           __lowerCamelCase
       ),   
       )   =						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
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






