

'''simple docstring'''



import argparse
from pathlib import Path

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

def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__ = None      ,   a__ = None      ,   a__ = None      ,   )		->     Tuple:



    """simple docstring"""







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

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

    if question_encoder_tokenizer_name_or_path is None:
        _snake_case			:			Tuple				       =		question_encoder_name_or_path

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

    # Save model.
    _snake_case			:			Tuple				       =		RagConfig.from_pretrained(__lowerCamelCase)
    _snake_case			:			int				       =		AutoConfig.from_pretrained(__lowerCamelCase)
    _snake_case			:			List[str]				       =		AutoConfig.from_pretrained(__lowerCamelCase)

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

    _snake_case			:			int				       =		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.
    _snake_case			:			Union[str, Any]				       =		AutoTokenizer.from_pretrained(__lowerCamelCase)
    gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/')
    _snake_case			:			str				       =		AutoTokenizer.from_pretrained(__lowerCamelCase)
    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''decision_transformer'''
						_lowercase						: Optional[Any]										=  ['''past_key_values''']
						_lowercase						: str										=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self       ,  _lowercase=17       ,  _lowercase=4       ,  _lowercase=128       ,  _lowercase=4_096       ,  _lowercase=True       ,  _lowercase=1       ,  _lowercase=1_024       ,  _lowercase=3       ,  _lowercase=1       ,  _lowercase=None       ,  _lowercase="relu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=1e-5       ,  _lowercase=0.02       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=50_256       ,  _lowercase=50_256       ,  _lowercase=False       ,  _lowercase=False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					state_dim
												_lowerCAmelCase    		=					act_dim
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					max_ep_len
												_lowerCAmelCase    		=					action_tanh
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					n_positions
												_lowerCAmelCase    		=					n_layer
												_lowerCAmelCase    		=					n_head
												_lowerCAmelCase    		=					n_inner
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					resid_pdrop
												_lowerCAmelCase    		=					embd_pdrop
												_lowerCAmelCase    		=					attn_pdrop
												_lowerCAmelCase    		=					layer_norm_epsilon
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					scale_attn_weights
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					scale_attn_by_inverse_layer_idx
												_lowerCAmelCase    		=					reorder_and_upcast_attn

												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					eos_token_id

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