



'''simple docstring'''




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


UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)

UpperCamelCase__ :			int												=				{
    '''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 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Dict    					=	'''vivit'''

			def __init__(							self  :				Optional[int]							,  lowerCAmelCase__  :				Dict=2_2_4							,  lowerCAmelCase__  :				str=3_2							,  lowerCAmelCase__  :				Tuple=[2, 1_6, 1_6]							,  lowerCAmelCase__  :				List[Any]=3							,  lowerCAmelCase__  :				List[Any]=7_6_8							,  lowerCAmelCase__  :				int=1_2							,  lowerCAmelCase__  :				Union[str, Any]=1_2							,  lowerCAmelCase__  :				Optional[int]=3_0_7_2							,  lowerCAmelCase__  :				Optional[int]="gelu_fast"							,  lowerCAmelCase__  :				str=0.0							,  lowerCAmelCase__  :				Any=0.0							,  lowerCAmelCase__  :				Union[str, Any]=0.02							,  lowerCAmelCase__  :				Dict=1E-06							,  lowerCAmelCase__  :				Optional[int]=True							,  **lowerCAmelCase__  :				Tuple							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_size
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_hidden_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	num_attention_heads
					__SCREAMING_SNAKE_CASE :	List[str]					 =	intermediate_size
					__SCREAMING_SNAKE_CASE :	Tuple					 =	hidden_act
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_dropout_prob
					__SCREAMING_SNAKE_CASE :	str					 =	attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE :	int					 =	initializer_range
					__SCREAMING_SNAKE_CASE :	Any					 =	layer_norm_eps

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	image_size
					__SCREAMING_SNAKE_CASE :	Tuple					 =	num_frames
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tubelet_size
					__SCREAMING_SNAKE_CASE :	List[str]					 =	num_channels
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	qkv_bias

					super().__init__(**lowerCAmelCase__		)










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			Optional[int]												=				{'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			Tuple												=				[
						    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''WavLMForAudioFrameClassification''',
						    '''WavLMForCTC''',
						    '''WavLMForSequenceClassification''',
						    '''WavLMForXVector''',
						    '''WavLMModel''',
						    '''WavLMPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			List[str]												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)