


"""simple docstring"""


import argparse
from pathlib import Path

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




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						str = None			,				)   ->      int:
      if config_name_or_path is None:
            lowerCamelCase					:					str        =	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            lowerCamelCase					:					List[str]        =	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowerCamelCase					:					str        =	question_encoder_name_or_path

      lowerCamelCase					:					Optional[int]        =	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      lowerCamelCase					:					Tuple        =	RagConfig.from_pretrained(UpperCamelCase__    )
      lowerCamelCase					:					Any        =	AutoConfig.from_pretrained(UpperCamelCase__    )
      lowerCamelCase					:					List[str]        =	AutoConfig.from_pretrained(UpperCamelCase__    )

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

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

      # Sanity check.
      model_class.from_pretrained(UpperCamelCase__    )

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


if __name__ == "__main__":
    __lowerCamelCase      :str	    =		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``'
        ),
    )

    __lowerCamelCase      :Dict	    =		parser.parse_args()

    __lowerCamelCase      :int	    =		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,
    )








"""simple docstring"""


from __future__ import annotations

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




