


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """swinv2"""

     _lowerCamelCase   =      {
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
     }


     def __init__(	self  ,       __A=224  ,       __A=4  ,       __A=3  ,       __A=96  ,       __A=[2, 2, 6, 2]  ,       __A=[3, 6, 12, 24]  ,       __A=7  ,       __A=4.0  ,       __A=True  ,       __A=0.0  ,       __A=0.0  ,       __A=0.1  ,       __A="gelu"  ,       __A=False  ,       __A=0.02  ,       __A=1E-5  ,       __A=32  ,       **__A  ,       ):
        super().__init__(**__A    )

        __a					=  image_size
        __a					=  patch_size
        __a					=  num_channels
        __a					=  embed_dim
        __a					=  depths
        __a					=  len(__A    )
        __a					=  num_heads
        __a					=  window_size
        __a					=  mlp_ratio
        __a					=  qkv_bias
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  drop_path_rate
        __a					=  hidden_act
        __a					=  use_absolute_embeddings
        __a					=  layer_norm_eps
        __a					=  initializer_range
        __a					=  encoder_stride
        # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        __a					=  int(embed_dim * 2 ** (len(__A    ) - 1)    )
        __a					=  (0, 0, 0, 0)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

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



