





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


SCREAMING_SNAKE_CASE__			      =						logging.get_logger(__name__)

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







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[Any]     		=				"data2vec-vision"





   def __init__( self					,			snake_case=768					,			snake_case=12					,			snake_case=12					,			snake_case=3_072					,			snake_case="gelu"					,			snake_case=0.0					,			snake_case=0.0					,			snake_case=0.02					,			snake_case=1E-12					,			snake_case=224					,			snake_case=16					,			snake_case=3					,			snake_case=False					,			snake_case=False					,			snake_case=False					,			snake_case=False					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=True					,			snake_case=[3, 5, 7, 11]					,			snake_case=[1, 2, 3, 6]					,			snake_case=True					,			snake_case=0.4					,			snake_case=256					,			snake_case=1					,			snake_case=False					,			snake_case=255					,			**snake_case					,			)				->  Any:
      """simple docstring"""





      super().__init__(**snake_case      )

      a__	:						Optional[int]      		=  hidden_size
      a__	:						Tuple      		=  num_hidden_layers
      a__	:						Tuple      		=  num_attention_heads
      a__	:						Any      		=  intermediate_size
      a__	:						int      		=  hidden_act
      a__	:						str      		=  hidden_dropout_prob
      a__	:						Dict      		=  attention_probs_dropout_prob
      a__	:						Tuple      		=  initializer_range
      a__	:						Dict      		=  layer_norm_eps

      a__	:						Any      		=  image_size
      a__	:						List[Any]      		=  patch_size
      a__	:						List[str]      		=  num_channels
      a__	:						Optional[int]      		=  use_mask_token
      a__	:						Dict      		=  use_absolute_position_embeddings
      a__	:						Any      		=  use_relative_position_bias
      a__	:						str      		=  use_shared_relative_position_bias
      a__	:						int      		=  layer_scale_init_value
      a__	:						List[str]      		=  drop_path_rate
      a__	:						List[str]      		=  use_mean_pooling
      # decode head attributes (semantic segmentation)
      a__	:						List[Any]      		=  out_indices
      a__	:						Tuple      		=  pool_scales
      # auxiliary head attributes (semantic segmentation)
      a__	:						Tuple      		=  use_auxiliary_head
      a__	:						Optional[int]      		=  auxiliary_loss_weight
      a__	:						List[Any]      		=  auxiliary_channels
      a__	:						Any      		=  auxiliary_num_convs
      a__	:						Optional[int]      		=  auxiliary_concat_input
      a__	:						Tuple      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]      )





   @property
   def 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

