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


_A   =			logging.get_logger(__name__)

_A   =			{
    "facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		"timesformer"





     def __init__(self				:   Optional[int]    ,   _A				:   Dict=2_2_4    ,   _A				:   Union[str, Any]=1_6    ,   _A				:   Optional[int]=3    ,   _A				:   Optional[int]=8    ,   _A				:   Union[str, Any]=7_6_8    ,   _A				:   Tuple=1_2    ,   _A				:   Dict=1_2    ,   _A				:   List[Any]=3_0_7_2    ,   _A				:   Tuple="gelu"    ,   _A				:   Any=0.0    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[int]=0.02    ,   _A				:   Any=1E-6    ,   _A				:   Any=True    ,   _A				:   List[Any]="divided_space_time"    ,   _A				:   List[str]=0    ,   **_A				:   List[Any]    ,   )						->						Optional[int]:
         super().__init__(**_A       )

         snake_case								= image_size
         snake_case								= patch_size
         snake_case								= num_channels
         snake_case								= num_frames

         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= initializer_range
         snake_case								= layer_norm_eps
         snake_case								= qkv_bias

         snake_case								= attention_type
         snake_case								= drop_path_rate



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__)

