
'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase			:				str    		=			{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['LayoutLMv3TokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				int    		=			[
          'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv3ForQuestionAnswering',
          'LayoutLMv3ForSequenceClassification',
          'LayoutLMv3ForTokenClassification',
          'LayoutLMv3Model',
          'LayoutLMv3PreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Optional[Any]    		=			[
          'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFLayoutLMv3ForQuestionAnswering',
          'TFLayoutLMv3ForSequenceClassification',
          'TFLayoutLMv3ForTokenClassification',
          'TFLayoutLMv3Model',
          'TFLayoutLMv3PreTrainedModel',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Optional[int]    		=			['LayoutLMv3FeatureExtractor']
      lowercase			:				Optional[Any]    		=			['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
      from .configuration_layoutlmva import (
          LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LayoutLMvaConfig,
          LayoutLMvaOnnxConfig,
      )
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_layoutlmva import (
                TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLayoutLMvaForQuestionAnswering,
                TFLayoutLMvaForSequenceClassification,
                TFLayoutLMvaForTokenClassification,
                TFLayoutLMvaModel,
                TFLayoutLMvaPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
            from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
      import sys

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











"""simple docstring"""
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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)			->      Any:





             """simple docstring"""






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

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

             super().__init__(**SCREAMING_SNAKE_CASE__							)