



import argparse
from pathlib import Path

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase = None				,					_lowerCAmelCase = None				,					_lowerCAmelCase = None				,					)	->  Dict:



       """simple docstring"""






       if config_name_or_path is None:
              A			:	Optional[Any]										=       '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

       if generator_tokenizer_name_or_path is None:
              A			:	Union[str, Any]										=       generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              A			:	List[str]										=       question_encoder_name_or_path

       A			:	int										=       RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

       # Save model.
       A			:	Optional[Any]										=       RagConfig.from_pretrained(_lowerCAmelCase		)
       A			:	int										=       AutoConfig.from_pretrained(_lowerCAmelCase		)
       A			:	Any										=       AutoConfig.from_pretrained(_lowerCAmelCase		)

       A			:	str										=       gen_config
       A			:	Tuple										=       question_encoder_config

       A			:	List[Any]										=       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.
       A			:	Tuple										=       AutoTokenizer.from_pretrained(_lowerCAmelCase		)
       gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""		)
       A			:	str										=       AutoTokenizer.from_pretrained(_lowerCAmelCase		)
       question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						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_:Tuple					=						parser.parse_args()

     SCREAMING_SNAKE_CASE_:List[str]					=						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A			:	List[str]										=       vocab_size
        A			:	List[Any]										=       hidden_size
        A			:	List[Any]										=       num_hidden_layers
        A			:	Optional[Any]										=       num_attention_heads
        A			:	Union[str, Any]										=       hidden_act
        A			:	Union[str, Any]										=       intermediate_size
        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	int										=       max_position_embeddings
        A			:	List[str]										=       type_vocab_size
        A			:	List[str]										=       initializer_range
        A			:	Dict										=       layer_norm_eps
        A			:	Optional[int]										=       position_embedding_type
        A			:	Any										=       use_cache
        A			:	Optional[int]										=       classifier_dropout






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


