



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:






      '''simple docstring'''







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

      if generator_tokenizer_name_or_path is None:
            UpperCAmelCase_  :	Optional[int]				      =		generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            UpperCAmelCase_  :	int				      =		question_encoder_name_or_path

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

      # Save model.
      UpperCAmelCase_  :	Optional[int]				      =		RagConfig.from_pretrained(__snake_case    )
      UpperCAmelCase_  :	Any				      =		AutoConfig.from_pretrained(__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		AutoConfig.from_pretrained(__snake_case    )

      UpperCAmelCase_  :	Optional[Any]				      =		gen_config
      UpperCAmelCase_  :	Optional[Any]				      =		question_encoder_config

      UpperCAmelCase_  :	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.
      UpperCAmelCase_  :	List[Any]				      =		AutoTokenizer.from_pretrained(__snake_case    )
      gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
      UpperCAmelCase_  :	Optional[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,
   )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""lxmert"""
       _lowerCamelCase			    =				{}



       def __init__(     self     ,lowercase=30522     ,lowercase=768     ,lowercase=12     ,lowercase=9500     ,lowercase=1600     ,lowercase=400     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=2     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=9     ,lowercase=5     ,lowercase=5     ,lowercase=2048     ,lowercase=4     ,lowercase=6.67     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_attention_heads
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	List[Any]				      =		intermediate_size
             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Any				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		num_qa_labels
             UpperCAmelCase_  :	str				      =		num_object_labels
             UpperCAmelCase_  :	Dict				      =		num_attr_labels
             UpperCAmelCase_  :	Tuple				      =		l_layers
             UpperCAmelCase_  :	Tuple				      =		x_layers
             UpperCAmelCase_  :	int				      =		r_layers
             UpperCAmelCase_  :	Optional[Any]				      =		visual_feat_dim
             UpperCAmelCase_  :	List[Any]				      =		visual_pos_dim
             UpperCAmelCase_  :	int				      =		visual_loss_normalizer
             UpperCAmelCase_  :	str				      =		task_matched
             UpperCAmelCase_  :	str				      =		task_mask_lm
             UpperCAmelCase_  :	int				      =		task_obj_predict
             UpperCAmelCase_  :	List[str]				      =		task_qa
             UpperCAmelCase_  :	Optional[int]				      =		visual_obj_loss
             UpperCAmelCase_  :	List[str]				      =		visual_attr_loss
             UpperCAmelCase_  :	str				      =		visual_feat_loss
             UpperCAmelCase_  :	List[Any]				      =		{"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
             super().__init__(**lowercase)
