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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "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       a		(   __UpperCAmelCase					):
							lowercase_		:						Optional[int]   							=  'decision_transformer'
							lowercase_		:						List[Any]   							=  ['past_key_values']
							lowercase_		:						Optional[Any]   							=  {
							    'max_position_embeddings': 'n_positions',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(						self  :  str      ,				snake_case__  :  Any=17      ,				snake_case__  :  List[str]=4      ,				snake_case__  :  Optional[int]=128      ,				snake_case__  :  str=4_096      ,				snake_case__  :  Dict=True      ,				snake_case__  :  Optional[Any]=1      ,				snake_case__  :  int=1_024      ,				snake_case__  :  int=3      ,				snake_case__  :  str=1      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  Tuple="relu"      ,				snake_case__  :  str=0.1      ,				snake_case__  :  Optional[Any]=0.1      ,				snake_case__  :  Union[str, Any]=0.1      ,				snake_case__  :  Tuple=1E-5      ,				snake_case__  :  str=0.0_2      ,				snake_case__  :  int=True      ,				snake_case__  :  Union[str, Any]=True      ,				snake_case__  :  List[str]=50_256      ,				snake_case__  :  str=50_256      ,				snake_case__  :  Dict=False      ,				snake_case__  :  Optional[int]=False      ,				**snake_case__  :  Optional[int]      ,				):
								"""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=snake_case__      ,				eos_token_id=snake_case__      ,				**snake_case__ )








import fire

from utils import calculate_rouge, save_json






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]=None			,							**UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()]
	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()][: len(UpperCamelCase  )]
	__lowerCAmelCase         =							calculate_rouge(UpperCamelCase			,							UpperCamelCase			,							**UpperCamelCase  )
	if save_path is not None:
		save_json(UpperCamelCase			,							UpperCamelCase			,							indent=UpperCamelCase  )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)






