



"""simple docstring"""






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


lowerCAmelCase__        =				logging.get_logger(__name__)

lowerCAmelCase__        =				{
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class        __snake_case       (	A__):
  snake_case__				:   Any  			=						"decision_transformer"
  snake_case__				:   Optional[int]  			=						["past_key_values"]
  snake_case__				:   Optional[int]  			=						{
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }




  def __init__( self     :						int						,					__lowerCAmelCase     :						Optional[int]=1_7						,					__lowerCAmelCase     :						List[str]=4						,					__lowerCAmelCase     :						Any=1_2_8						,					__lowerCAmelCase     :						Optional[Any]=4_0_9_6						,					__lowerCAmelCase     :						Tuple=True						,					__lowerCAmelCase     :						Any=1						,					__lowerCAmelCase     :						str=1_0_2_4						,					__lowerCAmelCase     :						int=3						,					__lowerCAmelCase     :						Optional[Any]=1						,					__lowerCAmelCase     :						int=None						,					__lowerCAmelCase     :						List[str]="relu"						,					__lowerCAmelCase     :						List[str]=0.1						,					__lowerCAmelCase     :						Any=0.1						,					__lowerCAmelCase     :						Optional[Any]=0.1						,					__lowerCAmelCase     :						Union[str, Any]=1E-5						,					__lowerCAmelCase     :						Tuple=0.02						,					__lowerCAmelCase     :						int=True						,					__lowerCAmelCase     :						List[str]=True						,					__lowerCAmelCase     :						List[Any]=5_0_2_5_6						,					__lowerCAmelCase     :						Any=5_0_2_5_6						,					__lowerCAmelCase     :						Optional[Any]=False						,					__lowerCAmelCase     :						str=False						,					**__lowerCAmelCase     :						str						,					):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= state_dim
         _lowerCamelCase						:				Union[str, Any]    			= act_dim
         _lowerCamelCase						:				Optional[Any]    			= hidden_size
         _lowerCamelCase						:				Optional[int]    			= max_ep_len
         _lowerCamelCase						:				Optional[int]    			= action_tanh
         _lowerCamelCase						:				Any    			= vocab_size
         _lowerCamelCase						:				List[Any]    			= n_positions
         _lowerCamelCase						:				Tuple    			= n_layer
         _lowerCamelCase						:				Union[str, Any]    			= n_head
         _lowerCamelCase						:				str    			= n_inner
         _lowerCamelCase						:				List[Any]    			= activation_function
         _lowerCamelCase						:				int    			= resid_pdrop
         _lowerCamelCase						:				Dict    			= embd_pdrop
         _lowerCamelCase						:				Union[str, Any]    			= attn_pdrop
         _lowerCamelCase						:				Union[str, Any]    			= layer_norm_epsilon
         _lowerCamelCase						:				List[Any]    			= initializer_range
         _lowerCamelCase						:				Optional[Any]    			= scale_attn_weights
         _lowerCamelCase						:				Optional[int]    			= use_cache
         _lowerCamelCase						:				Optional[int]    			= scale_attn_by_inverse_layer_idx
         _lowerCamelCase						:				int    			= reorder_and_upcast_attn

         _lowerCamelCase						:				Any    			= bos_token_id
         _lowerCamelCase						:				Tuple    			= eos_token_id

         super().__init__(bos_token_id=_A						,					eos_token_id=_A						,					**_A		)



import argparse
from pathlib import Path

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




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    question_encoder_name_or_path

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    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.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[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``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	int       =  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,
		)
