






'''simple docstring'''







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


_A	:			Any         =logging.get_logger(__name__)

_A	:			List[str]         ={
    '''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 _lowercase	(				_lowercase		):
							a				      =       """decision_transformer"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}




							def __init__(							self:				Optional[Any]			,							UpperCamelCase__:				Dict=17			,							UpperCamelCase__:				int=4			,							UpperCamelCase__:				Optional[int]=128			,							UpperCamelCase__:				int=4_096			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				Optional[Any]=1			,							UpperCamelCase__:				Any=1_024			,							UpperCamelCase__:				Dict=3			,							UpperCamelCase__:				List[str]=1			,							UpperCamelCase__:				int=None			,							UpperCamelCase__:				Union[str, Any]="relu"			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				Optional[int]=1e-5			,							UpperCamelCase__:				str=0.02			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				int=50_256			,							UpperCamelCase__:				Union[str, Any]=50_256			,							UpperCamelCase__:				Optional[int]=False			,							UpperCamelCase__:				Union[str, Any]=False			,							**UpperCamelCase__:				List[Any]			,							):
														lowerCamelCase__					:	Optional[Any]         =       state_dim
														lowerCamelCase__					:	Optional[int]         =       act_dim
														lowerCamelCase__					:	List[str]         =       hidden_size
														lowerCamelCase__					:	str         =       max_ep_len
														lowerCamelCase__					:	Optional[Any]         =       action_tanh
														lowerCamelCase__					:	Dict         =       vocab_size
														lowerCamelCase__					:	Dict         =       n_positions
														lowerCamelCase__					:	Union[str, Any]         =       n_layer
														lowerCamelCase__					:	List[Any]         =       n_head
														lowerCamelCase__					:	int         =       n_inner
														lowerCamelCase__					:	str         =       activation_function
														lowerCamelCase__					:	str         =       resid_pdrop
														lowerCamelCase__					:	Optional[Any]         =       embd_pdrop
														lowerCamelCase__					:	Union[str, Any]         =       attn_pdrop
														lowerCamelCase__					:	int         =       layer_norm_epsilon
														lowerCamelCase__					:	Union[str, Any]         =       initializer_range
														lowerCamelCase__					:	str         =       scale_attn_weights
														lowerCamelCase__					:	Tuple         =       use_cache
														lowerCamelCase__					:	Any         =       scale_attn_by_inverse_layer_idx
														lowerCamelCase__					:	Any         =       reorder_and_upcast_attn

														lowerCamelCase__					:	List[Any]         =       bos_token_id
														lowerCamelCase__					:	Dict         =       eos_token_id

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








'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




