


'''simple docstring'''






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


a_   :      int				    =      logging.get_logger(__name__)

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




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""timesformer"""
 def __init__(	self ,			__magic_name__=2_24 ,			__magic_name__=16 ,			__magic_name__=3 ,			__magic_name__=8 ,			__magic_name__=7_68 ,			__magic_name__=12 ,			__magic_name__=12 ,			__magic_name__=30_72 ,			__magic_name__="gelu" ,			__magic_name__=0.0 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			__magic_name__=1e-6 ,			__magic_name__=True ,			__magic_name__="divided_space_time" ,			__magic_name__=0 ,			**__magic_name__ ,			)   ->							List[str]:
      super().__init__(**__magic_name__  )

      _a      =							image_size
      _a      =							patch_size
      _a      =							num_channels
      _a      =							num_frames

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

      _a      =							attention_type
      _a      =							drop_path_rate





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

