import argparse
from pathlib import Path

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




def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A = None,  __A = None,  __A = None,  )     ->				str:







      '''simple docstring'''


      if config_name_or_path is None:
            UpperCAmelCase__								=  "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

      if generator_tokenizer_name_or_path is None:
            UpperCAmelCase__								=  generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            UpperCAmelCase__								=  question_encoder_name_or_path

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

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

      UpperCAmelCase__								=  gen_config
      UpperCAmelCase__								=  question_encoder_config

      UpperCAmelCase__								=  model_class.from_pretrained_question_encoder_generator(
          __A,  __A,  config=__A							)
      rag_model.save_pretrained(__A							)

      # Sanity check.
      model_class.from_pretrained(__A							)

      # Save tokenizers.
      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"							)
      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"							)


if __name__ == "__main__":
      UpperCamelCase__											=					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``'
          ),
      )

      UpperCamelCase__											=					parser.parse_args()

      UpperCamelCase__											=					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'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





