


import argparse
from pathlib import Path

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : Path      ,					SCREAMING_SNAKE_CASE__      : str = None      ,					SCREAMING_SNAKE_CASE__      : str = None      ,					SCREAMING_SNAKE_CASE__      : str = None      ,					):
     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(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=gen_config
     __UpperCamelCase   							=question_encoder_config

     __UpperCamelCase   							=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.
     __UpperCamelCase   							=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
     gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'    )
     __UpperCamelCase   							=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
     question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'    )


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

   _A			=						parser.parse_args()

   _A			=						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,
   )









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





