"""simple docstring"""







import argparse
from pathlib import Path

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  int = None  ,    __SCREAMING_SNAKE_CASE							:  Any = None  ,    __SCREAMING_SNAKE_CASE							:  List[Any] = None  ,    ):






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

     if generator_tokenizer_name_or_path is None:
          lowercase_			:       Tuple									=			generator_name_or_path

     if question_encoder_tokenizer_name_or_path is None:
          lowercase_			:       Optional[int]									=			question_encoder_name_or_path

     lowercase_			:       List[Any]									=			RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

     # Save model.
     lowercase_			:       str									=			RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Any									=			AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[int]									=			AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Optional[Any]									=			gen_config
     lowercase_			:       Optional[int]									=			question_encoder_config

     lowercase_			:       int									=			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.
     lowercase_			:       Optional[Any]									=			AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
     gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'   )
     lowercase_			:       Optional[Any]									=			AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )
     question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=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``"
           ),
       )

       __SCREAMING_SNAKE_CASE								=parser.parse_args()

       __SCREAMING_SNAKE_CASE								=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"""







from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class  UpperCamelCase	:
   pass

