






'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""vivit"""




 def __init__(      self	:							Union[str, Any]					,      __lowercase	:							Dict=2_24					,      __lowercase	:							List[str]=32					,      __lowercase	:							Optional[Any]=[2, 16, 16]					,      __lowercase	:							str=3					,      __lowercase	:							int=7_68					,      __lowercase	:							List[str]=12					,      __lowercase	:							str=12					,      __lowercase	:							List[str]=30_72					,      __lowercase	:							str="gelu_fast"					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							List[str]=0.0					,      __lowercase	:							int=0.02					,      __lowercase	:							int=1e-06					,      __lowercase	:							Optional[int]=True					,      **__lowercase	:							Optional[int]					,      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_size
       SCREAMING_SNAKE_CASE__     :				Tuple									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				List[Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_act
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__     :				List[str]									=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__     :				List[str]									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Tuple									=layer_norm_eps

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=num_frames
       SCREAMING_SNAKE_CASE__     :				List[str]									=tubelet_size
       SCREAMING_SNAKE_CASE__     :				str									=num_channels
       SCREAMING_SNAKE_CASE__     :				Tuple									=qkv_bias

       super().__init__(**__lowercase      )







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

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