
"""simple docstring"""







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


lowercase_         =       logging.get_logger(__name__)

lowercase_         =       {
    "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 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Union[str, Any]					 =     """trajectory_transformer"""
 UpperCamelCase_    :  Any					 =     ["""past_key_values"""]
 UpperCamelCase_    :  Union[str, Any]					 =     {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }




 def __init__(			self							:			Optional[Any]  , a_							:			Optional[int]=1_00  , a_							:			List[Any]=5  , a_							:			int=1  , a_							:			int=1  , a_							:			Dict=2_49  , a_							:			Dict=6  , a_							:			List[Any]=17  , a_							:			List[Any]=25  , a_							:			Union[str, Any]=4  , a_							:			List[str]=4  , a_							:			Any=1_28  , a_							:			Any=0.1  , a_							:			Dict=0.1  , a_							:			Optional[int]=0.1  , a_							:			List[str]=0.0006  , a_							:			Optional[int]=5_12  , a_							:			List[str]=0.02  , a_							:			Optional[Any]=1E-12  , a_							:			int=1  , a_							:			Optional[int]=True  , a_							:			Optional[Any]=1  , a_							:			Tuple=5_02_56  , a_							:			Tuple=5_02_56  , **a_							:			Any  , )->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	vocab_size
      UpperCAmelCase_    :     Tuple            =	action_weight
      UpperCAmelCase_    :     Any            =	reward_weight
      UpperCAmelCase_    :     Tuple            =	value_weight
      UpperCAmelCase_    :     Optional[int]            =	max_position_embeddings
      UpperCAmelCase_    :     Optional[Any]            =	block_size
      UpperCAmelCase_    :     str            =	action_dim
      UpperCAmelCase_    :     List[str]            =	observation_dim
      UpperCAmelCase_    :     List[Any]            =	transition_dim
      UpperCAmelCase_    :     int            =	learning_rate
      UpperCAmelCase_    :     Dict            =	n_layer
      UpperCAmelCase_    :     Optional[int]            =	n_head
      UpperCAmelCase_    :     List[Any]            =	n_embd
      UpperCAmelCase_    :     int            =	embd_pdrop
      UpperCAmelCase_    :     List[Any]            =	attn_pdrop
      UpperCAmelCase_    :     Optional[int]            =	resid_pdrop
      UpperCAmelCase_    :     str            =	initializer_range
      UpperCAmelCase_    :     str            =	layer_norm_eps
      UpperCAmelCase_    :     str            =	kaiming_initializer_range
      UpperCAmelCase_    :     Tuple            =	use_cache
      super().__init__(pad_token_id=a_  , bos_token_id=a_  , eos_token_id=a_  , **a_			)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_         =       {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_         =       [
           "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TimesformerModel",
           "TimesformerForVideoClassification",
           "TimesformerPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timesformer import (
                  TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TimesformerForVideoClassification,
                  TimesformerModel,
                  TimesformerPreTrainedModel,
              )

else:
       import sys

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

