


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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "decision_transformer"
       snake_case      =    ["past_key_values"]
       snake_case      =    {
           "max_position_embeddings": "n_positions",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=17				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=4096				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=1024				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-5				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=5_0256				,       _SCREAMING_SNAKE_CASE=5_0256				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





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

         A_      :				Optional[Any]		     =					bos_token_id
         A_      :				Dict		     =					eos_token_id

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




