


"""simple docstring"""



import argparse
from pathlib import Path

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







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  str						,							lowercase  :  str						,							lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  str = None						,							lowercase  :  str = None						,							)	->						int:


    """simple docstring"""





    if config_name_or_path is None:
        snake_case    :      Tuple      					=    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

    if generator_tokenizer_name_or_path is None:
        snake_case    :      Dict      					=    generator_name_or_path

    if question_encoder_tokenizer_name_or_path is None:
        snake_case    :      Any      					=    question_encoder_name_or_path

    snake_case    :      str      					=    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

    # Save model.
    snake_case    :      str      					=    RagConfig.from_pretrained(lowercase )
    snake_case    :      Dict      					=    AutoConfig.from_pretrained(lowercase )
    snake_case    :      Tuple      					=    AutoConfig.from_pretrained(lowercase )

    snake_case    :      str      					=    gen_config
    snake_case    :      Dict      					=    question_encoder_config

    snake_case    :      Any      					=    model_class.from_pretrained_question_encoder_generator(
        lowercase						,							lowercase						,							config=lowercase )
    rag_model.save_pretrained(lowercase )

    # Sanity check.
    model_class.from_pretrained(lowercase )

    # Save tokenizers.
    snake_case    :      Optional[int]      					=    AutoTokenizer.from_pretrained(lowercase )
    gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
    snake_case    :      Optional[Any]      					=    AutoTokenizer.from_pretrained(lowercase )
    question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )


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

  __snake_case   			=    parser.parse_args()

  __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 collections import OrderedDict
from typing import Mapping

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


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  '''xmod'''







   def __init__(	self						,	UpperCamelCase__=3_0522						,	UpperCamelCase__=768						,	UpperCamelCase__=12						,	UpperCamelCase__=12						,	UpperCamelCase__=3072						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=1e-12						,	UpperCamelCase__=1						,	UpperCamelCase__=0						,	UpperCamelCase__=2						,	UpperCamelCase__="absolute"						,	UpperCamelCase__=True						,	UpperCamelCase__=None						,	UpperCamelCase__=False						,	UpperCamelCase__=2						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=("en_XX",)						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		int:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,	bos_token_id=UpperCamelCase__						,	eos_token_id=UpperCamelCase__						,	**UpperCamelCase__						)

       snake_case    :      List[Any]      					=    vocab_size
       snake_case    :      List[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Any      					=    num_attention_heads
       snake_case    :      List[str]      					=    hidden_act
       snake_case    :      Union[str, Any]      					=    intermediate_size
       snake_case    :      int      					=    hidden_dropout_prob
       snake_case    :      Any      					=    attention_probs_dropout_prob
       snake_case    :      Optional[int]      					=    max_position_embeddings
       snake_case    :      Tuple      					=    type_vocab_size
       snake_case    :      List[str]      					=    initializer_range
       snake_case    :      int      					=    layer_norm_eps
       snake_case    :      Optional[Any]      					=    position_embedding_type
       snake_case    :      int      					=    use_cache
       snake_case    :      Dict      					=    classifier_dropout
       snake_case    :      Dict      					=    pre_norm
       snake_case    :      Union[str, Any]      					=    adapter_reduction_factor
       snake_case    :      Any      					=    adapter_layer_norm
       snake_case    :      Optional[int]      					=    adapter_reuse_layer_norm
       snake_case    :      List[Any]      					=    ln_before_adapter
       snake_case    :      str      					=    list(UpperCamelCase__						)
       snake_case    :      int      					=    default_language







class _lowerCAmelCase				(      snake_case_	):







   @property
   def  lowerCamelCase		(	self						)	->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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





