





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase							:     Optional[Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Tuple		     ={
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class 					UpperCAmelCase     (	__snake_case    ):
 __lowercase       		=    """data2vec-vision"""
 def __init__(    self				:Optional[int]						,	lowercase_				:str=7_68						,	lowercase_				:Any=12						,	lowercase_				:str=12						,	lowercase_				:Optional[Any]=30_72						,	lowercase_				:Union[str, Any]="gelu"						,	lowercase_				:List[Any]=0.0						,	lowercase_				:Dict=0.0						,	lowercase_				:Union[str, Any]=0.0_2						,	lowercase_				:str=1E-12						,	lowercase_				:Dict=2_24						,	lowercase_				:List[Any]=16						,	lowercase_				:Optional[Any]=3						,	lowercase_				:List[str]=False						,	lowercase_				:List[Any]=False						,	lowercase_				:str=False						,	lowercase_				:List[str]=False						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:Optional[Any]=True						,	lowercase_				:List[Any]=[3, 5, 7, 11]						,	lowercase_				:List[str]=[1, 2, 3, 6]						,	lowercase_				:Union[str, Any]=True						,	lowercase_				:Any=0.4						,	lowercase_				:Union[str, Any]=2_56						,	lowercase_				:List[Any]=1						,	lowercase_				:str=False						,	lowercase_				:Optional[int]=2_55						,	**lowercase_				:Dict						,	)->							List[str]:
     super().__init__(**UpperCamelCase__							)

     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      initializer_range
     A__    =      layer_norm_eps

     A__    =      image_size
     A__    =      patch_size
     A__    =      num_channels
     A__    =      use_mask_token
     A__    =      use_absolute_position_embeddings
     A__    =      use_relative_position_bias
     A__    =      use_shared_relative_position_bias
     A__    =      layer_scale_init_value
     A__    =      drop_path_rate
     A__    =      use_mean_pooling
     # decode head attributes (semantic segmentation)
     A__    =      out_indices
     A__    =      pool_scales
     # auxiliary head attributes (semantic segmentation)
     A__    =      use_auxiliary_head
     A__    =      auxiliary_loss_weight
     A__    =      auxiliary_channels
     A__    =      auxiliary_num_convs
     A__    =      auxiliary_concat_input
     A__    =      semantic_loss_ignore_index






class 					UpperCAmelCase     (	__snake_case    ):
 __lowercase       		=    version.parse("""1.11"""    )
 @property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]							)



 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							float:
     return 1E-4










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase							:     List[Any]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



