




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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    '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(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''vivit'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=224  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=[2, 16, 16]  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=3_072  ,_SCREAMING_SNAKE_CASE="gelu_fast"  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-06  ,_SCREAMING_SNAKE_CASE=True  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         UpperCAmelCase_     :				List[str]  =     hidden_size
         UpperCAmelCase_     :				Union[str, Any]  =     num_hidden_layers
         UpperCAmelCase_     :				List[str]  =     num_attention_heads
         UpperCAmelCase_     :				List[str]  =     intermediate_size
         UpperCAmelCase_     :				Tuple  =     hidden_act
         UpperCAmelCase_     :				Any  =     hidden_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Optional[Any]  =     initializer_range
         UpperCAmelCase_     :				Tuple  =     layer_norm_eps

         UpperCAmelCase_     :				List[str]  =     image_size
         UpperCAmelCase_     :				str  =     num_frames
         UpperCAmelCase_     :				str  =     tubelet_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     qkv_bias

         super().__init__(**_SCREAMING_SNAKE_CASE    )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__     					=			{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			["OwlViTFeatureExtractor"]
		SCREAMING_SNAKE_CASE__     					=			["OwlViTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			[
		    "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OwlViTModel",
		    "OwlViTPreTrainedModel",
		    "OwlViTTextModel",
		    "OwlViTVisionModel",
		    "OwlViTForObjectDetection",
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

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






