


import argparse
from pathlib import Path

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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case = None			,							snake_case = None			,							snake_case = None			,							)  ->				Tuple:






       if config_name_or_path is None:
              lowercase__:   Any  			=   'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

       if generator_tokenizer_name_or_path is None:
              lowercase__:   str  			=   generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowercase__:   Union[str, Any]  			=   question_encoder_name_or_path

       lowercase__:   str  			=   RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

       # Save model.
       lowercase__:   Optional[int]  			=   RagConfig.from_pretrained(_snake_case     )
       lowercase__:   Dict  			=   AutoConfig.from_pretrained(_snake_case     )
       lowercase__:   Dict  			=   AutoConfig.from_pretrained(_snake_case     )

       lowercase__:   str  			=   gen_config
       lowercase__:   str  			=   question_encoder_config

       lowercase__:   Tuple  			=   model_class.from_pretrained_question_encoder_generator(
           _snake_case			,							_snake_case			,							config=_snake_case     )
       rag_model.save_pretrained(_snake_case     )

       # Sanity check.
       model_class.from_pretrained(_snake_case     )

       # Save tokenizers.
       lowercase__:   Tuple  			=   AutoTokenizer.from_pretrained(_snake_case     )
       gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'     )
       lowercase__:   Union[str, Any]  			=   AutoTokenizer.from_pretrained(_snake_case     )
       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,
 )





_lowerCAmelCase					:			Optional[int]  				=			"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_lowerCAmelCase					:			Tuple  				=			[{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase					:			Optional[int]  				=			{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






