






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


a	: List[str]      =				logging.get_logger(__name__)

a	: List[Any]      =				{
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}
class 					a					(				A__     ):



      """simple docstring"""


      a						:				List[str]							 =			'''videomae'''
      def __init__(   self      :    List[Any]	,   __lowercase      :    List[Any]=224	,   __lowercase      :    int=16	,   __lowercase      :    Optional[int]=3	,   __lowercase      :    Optional[Any]=16	,   __lowercase      :    Dict=2	,   __lowercase      :    Dict=768	,   __lowercase      :    Any=12	,   __lowercase      :    Any=12	,   __lowercase      :    Dict=3072	,   __lowercase      :    Any="gelu"	,   __lowercase      :    int=0.0	,   __lowercase      :    Optional[Any]=0.0	,   __lowercase      :    Optional[int]=0.02	,   __lowercase      :    List[str]=1e-1_2	,   __lowercase      :    str=True	,   __lowercase      :    int=True	,   __lowercase      :    Optional[Any]=6	,   __lowercase      :    Optional[Any]=384	,   __lowercase      :    str=4	,   __lowercase      :    str=1536	,   __lowercase      :    List[Any]=True	,   **__lowercase      :    Dict	,   )							->   List[str]:
        super().__init__(**__lowercase      )

        __UpperCAmelCase     =						image_size
        __UpperCAmelCase     =						patch_size
        __UpperCAmelCase     =						num_channels
        __UpperCAmelCase     =						num_frames
        __UpperCAmelCase     =						tubelet_size

        __UpperCAmelCase     =						hidden_size
        __UpperCAmelCase     =						num_hidden_layers
        __UpperCAmelCase     =						num_attention_heads
        __UpperCAmelCase     =						intermediate_size
        __UpperCAmelCase     =						hidden_act
        __UpperCAmelCase     =						hidden_dropout_prob
        __UpperCAmelCase     =						attention_probs_dropout_prob
        __UpperCAmelCase     =						initializer_range
        __UpperCAmelCase     =						layer_norm_eps
        __UpperCAmelCase     =						qkv_bias
        __UpperCAmelCase     =						use_mean_pooling

        __UpperCAmelCase     =						decoder_num_attention_heads
        __UpperCAmelCase     =						decoder_hidden_size
        __UpperCAmelCase     =						decoder_num_hidden_layers
        __UpperCAmelCase     =						decoder_intermediate_size
        __UpperCAmelCase     =						norm_pix_loss










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


