





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCamelCase   :							List[str]     =	{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase   :							Union[str, Any]     =	[
        """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvNextV2ForImageClassification""",
        """ConvNextV2Model""",
        """ConvNextV2PreTrainedModel""",
        """ConvNextV2Backbone""",
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase   :							Optional[Any]     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)








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


_UpperCamelCase   :							Dict     =	logging.get_logger(__name__)

_UpperCamelCase   :							Optional[int]     =	{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''videomae'''

  def __init__( self  ,    UpperCAmelCase=2_24  ,    UpperCAmelCase=16  ,    UpperCAmelCase=3  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=7_68  ,    UpperCAmelCase=12  ,    UpperCAmelCase=12  ,    UpperCAmelCase=30_72  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=1e-12  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase=6  ,    UpperCAmelCase=3_84  ,    UpperCAmelCase=4  ,    UpperCAmelCase=15_36  ,    UpperCAmelCase=True  ,    **UpperCAmelCase  ,    )-> Any:
     super().__init__(**UpperCAmelCase     )

     __A				 =       image_size
     __A				 =       patch_size
     __A				 =       num_channels
     __A				 =       num_frames
     __A				 =       tubelet_size

     __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				 =       use_mean_pooling

     __A				 =       decoder_num_attention_heads
     __A				 =       decoder_hidden_size
     __A				 =       decoder_num_hidden_layers
     __A				 =       decoder_intermediate_size
     __A				 =       norm_pix_loss

