





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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vivit"""


      def __init__(		self   ,				a__=224   ,				a__=32   ,				a__=[2, 16, 16]   ,				a__=3   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu_fast"   ,				a__=0.0   ,				a__=0.0   ,				a__=0.02   ,				a__=1e-06   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		hidden_size
             _lowerCamelCase				:  Union[str, Any]						=		num_hidden_layers
             _lowerCamelCase				:  Optional[Any]						=		num_attention_heads
             _lowerCamelCase				:  List[str]						=		intermediate_size
             _lowerCamelCase				:  Dict						=		hidden_act
             _lowerCamelCase				:  Union[str, Any]						=		hidden_dropout_prob
             _lowerCamelCase				:  Union[str, Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  str						=		initializer_range
             _lowerCamelCase				:  str						=		layer_norm_eps

             _lowerCamelCase				:  Any						=		image_size
             _lowerCamelCase				:  Optional[Any]						=		num_frames
             _lowerCamelCase				:  Union[str, Any]						=		tubelet_size
             _lowerCamelCase				:  Dict						=		num_channels
             _lowerCamelCase				:  Dict						=		qkv_bias

             super().__init__(**a__)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       _lowerCamelCase      							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

