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


_A   =			logging.get_logger(__name__)

_A   =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		"trajectory_transformer"
     UpperCAmelCase__    :							Tuple									=		["past_key_values"]
     UpperCAmelCase__    :							Union[str, Any]									=		{
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(self				:   List[str]    ,   _A				:   Any=1_0_0    ,   _A				:   Any=5    ,   _A				:   Optional[int]=1    ,   _A				:   Tuple=1    ,   _A				:   Dict=2_4_9    ,   _A				:   List[Any]=6    ,   _A				:   List[Any]=1_7    ,   _A				:   List[str]=2_5    ,   _A				:   Union[str, Any]=4    ,   _A				:   int=4    ,   _A				:   int=1_2_8    ,   _A				:   int=0.1    ,   _A				:   str=0.1    ,   _A				:   Dict=0.1    ,   _A				:   Dict=0.00_06    ,   _A				:   Any=5_1_2    ,   _A				:   List[str]=0.02    ,   _A				:   Optional[int]=1E-12    ,   _A				:   Tuple=1    ,   _A				:   Any=True    ,   _A				:   List[Any]=1    ,   _A				:   Optional[int]=5_0_2_5_6    ,   _A				:   int=5_0_2_5_6    ,   **_A				:   Optional[int]    ,   )						->						Union[str, Any]:
         snake_case								= vocab_size
         snake_case								= action_weight
         snake_case								= reward_weight
         snake_case								= value_weight
         snake_case								= max_position_embeddings
         snake_case								= block_size
         snake_case								= action_dim
         snake_case								= observation_dim
         snake_case								= transition_dim
         snake_case								= learning_rate
         snake_case								= n_layer
         snake_case								= n_head
         snake_case								= n_embd
         snake_case								= embd_pdrop
         snake_case								= attn_pdrop
         snake_case								= resid_pdrop
         snake_case								= initializer_range
         snake_case								= layer_norm_eps
         snake_case								= kaiming_initializer_range
         snake_case								= use_cache
         super().__init__(pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   **_A       )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

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

