





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


lowercase_		:				Optional[Any]				=						logging.get_logger(__name__)

lowercase_		:				Optional[int]				=						{
    '''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				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	"decision_transformer"
						_UpperCamelCase  :	List[Any]								=	["past_key_values"]
						_UpperCamelCase  :	Union[str, Any]								=	{
						    "max_position_embeddings": "n_positions",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}







						def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		Any=17 ,				lowerCamelCase_			:		Optional[Any]=4 ,				lowerCamelCase_			:		List[str]=1_28 ,				lowerCamelCase_			:		Dict=40_96 ,				lowerCamelCase_			:		List[str]=True ,				lowerCamelCase_			:		Optional[int]=1 ,				lowerCamelCase_			:		Tuple=10_24 ,				lowerCamelCase_			:		Optional[int]=3 ,				lowerCamelCase_			:		List[str]=1 ,				lowerCamelCase_			:		Tuple=None ,				lowerCamelCase_			:		Dict="relu" ,				lowerCamelCase_			:		int=0.1 ,				lowerCamelCase_			:		Dict=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_02_56 ,				lowerCamelCase_			:		Any=5_02_56 ,				lowerCamelCase_			:		str=False ,				lowerCamelCase_			:		List[str]=False ,				**lowerCamelCase_			:		Any ,				):





										'''simple docstring'''







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

										_snake_case :			List[str]  						=   bos_token_id
										_snake_case :			str  						=   eos_token_id

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






