




'''simple docstring'''







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


A									=logging.get_logger(__name__)

A									={
    '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						(			__a				):
       __a       :  List[Any]            =      """decision_transformer"""
       __a       :  Union[str, Any]            =      ["""past_key_values"""]
       __a       :  List[Any]            =      {
           """max_position_embeddings""": """n_positions""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }
       def __init__(			self		:			str     ,       lowercase		:			Any=17     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			Dict=128     ,       lowercase		:			Union[str, Any]=4_096     ,       lowercase		:			List[Any]=True     ,       lowercase		:			int=1     ,       lowercase		:			Optional[int]=1_024     ,       lowercase		:			Tuple=3     ,       lowercase		:			Optional[int]=1     ,       lowercase		:			Any=None     ,       lowercase		:			Dict="relu"     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Optional[Any]=0.1     ,       lowercase		:			List[Any]=0.1     ,       lowercase		:			List[Any]=1E-5     ,       lowercase		:			Optional[Any]=0.02     ,       lowercase		:			List[Any]=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			str=50_256     ,       lowercase		:			int=50_256     ,       lowercase		:			int=False     ,       lowercase		:			Tuple=False     ,       **lowercase		:			Union[str, Any]     ,       ):



           '''simple docstring'''

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

           UpperCAmelCase     =	bos_token_id
           UpperCAmelCase     =	eos_token_id

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







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

   A									=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




