


"""simple docstring"""



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


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    '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 _lowerCAmelCase    (				lowercase__     ):
			__UpperCAmelCase			:	Any							  =  'vivit'







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

							'''simple docstring'''




							snake_case   :				Dict				 =   hidden_size
							snake_case   :				List[str]				 =   num_hidden_layers
							snake_case   :				Any				 =   num_attention_heads
							snake_case   :				Optional[Any]				 =   intermediate_size
							snake_case   :				Tuple				 =   hidden_act
							snake_case   :				str				 =   hidden_dropout_prob
							snake_case   :				str				 =   attention_probs_dropout_prob
							snake_case   :				int				 =   initializer_range
							snake_case   :				Dict				 =   layer_norm_eps

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

							super().__init__(**_UpperCamelCase						)









"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





