

'''simple docstring'''







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       )			->			List[Any]:
       if config_name_or_path is None:
              A_						=      """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

       if generator_tokenizer_name_or_path is None:
              A_						=      generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              A_						=      question_encoder_name_or_path

       A_						=      RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

       # Save model.
       A_						=      RagConfig.from_pretrained(UpperCAmelCase__				)
       A_						=      AutoConfig.from_pretrained(UpperCAmelCase__				)
       A_						=      AutoConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      gen_config
       A_						=      question_encoder_config

       A_						=      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.
       A_						=      AutoTokenizer.from_pretrained(UpperCAmelCase__				)
       gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""				)
       A_						=      AutoTokenizer.from_pretrained(UpperCAmelCase__				)
       question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""				)


if __name__ == "__main__":
      __lowerCamelCase		       =    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		       =    parser.parse_args()

      __lowerCamelCase		       =    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'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(x - y				) for x, y in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
