






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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	'''megatron-bert'''


     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	List[str]=2_9056					, UpperCAmelCase_       :	Tuple=1024					, UpperCAmelCase_       :	Union[str, Any]=24					, UpperCAmelCase_       :	str=16					, UpperCAmelCase_       :	Dict=4096					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	int=0.1					, UpperCAmelCase_       :	Optional[Any]=0.1					, UpperCAmelCase_       :	Tuple=512					, UpperCAmelCase_       :	Any=2					, UpperCAmelCase_       :	Dict=0.02					, UpperCAmelCase_       :	int=1E-12					, UpperCAmelCase_       :	List[Any]=0					, UpperCAmelCase_       :	Any="absolute"					, UpperCAmelCase_       :	List[Any]=True					, **UpperCAmelCase_       :	str					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      int							=    hidden_act
       SCREAMING_SNAKE_CASE       :      str							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      int							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Tuple							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[str]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache














from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

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






