
import argparse
from pathlib import Path

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		):
    if config_name_or_path is None:
        snake_case__						=				"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

    if generator_tokenizer_name_or_path is None:
        snake_case__						=				generator_name_or_path

    if question_encoder_tokenizer_name_or_path is None:
        snake_case__						=				question_encoder_name_or_path

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

    # Save model.
    snake_case__						=				RagConfig.from_pretrained(__lowerCAmelCase							)
    snake_case__						=				AutoConfig.from_pretrained(__lowerCAmelCase							)
    snake_case__						=				AutoConfig.from_pretrained(__lowerCAmelCase							)

    snake_case__						=				gen_config
    snake_case__						=				question_encoder_config

    snake_case__						=				model_class.from_pretrained_question_encoder_generator(
        __lowerCAmelCase    ,		__lowerCAmelCase    ,		config=__lowerCAmelCase							)
    rag_model.save_pretrained(__lowerCAmelCase							)

    # Sanity check.
    model_class.from_pretrained(__lowerCAmelCase							)

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


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

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  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,
     )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



