

def 							__a       (					__A	):
    if not isinstance(__A ,       __A	):
        SCREAMING_SNAKE_CASE__					     =						(
            "operation can not be conducted on a object of type "
            f"{type(__A	).__name__}"
        )
        raise TypeError(__A	)
    SCREAMING_SNAKE_CASE__					     =						""
    for _ in range(0 ,       32	):
        bit_string += str(number % 2	)
        SCREAMING_SNAKE_CASE__					     =						number >> 1
    return bit_string





def 							__a       (					__A	):
    if number < 0:
        raise ValueError("the value of input must be positive"	)
    elif isinstance(__A ,       __A	):
        raise TypeError("Input value must be a 'int' type"	)
    elif isinstance(__A ,       __A	):
        raise TypeError("'<' not supported between instances of 'str' and 'int'"	)
    SCREAMING_SNAKE_CASE__					     =						0
    # iterator over [1 to 32],since we are dealing with 32 bit integer
    for _ in range(1 ,       33	):
        # left shift the bits by unity
        SCREAMING_SNAKE_CASE__					     =						result << 1
        # get the end bit
        SCREAMING_SNAKE_CASE__					     =						number % 2
        # right shift the bits by unity
        SCREAMING_SNAKE_CASE__					     =						number >> 1
        # add that bit to our ans
        SCREAMING_SNAKE_CASE__					     =						result | end_bit
    return get_reverse_bit_string(__A	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__        =			logging.get_logger(__name__)

A__        =			{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      """convnext"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Any=3     ,  A_    :				Tuple=4     ,  A_    :				Optional[int]=4     ,  A_    :				Dict=None     ,  A_    :				List[Any]=None     ,  A_    :				Union[str, Any]="gelu"     ,  A_    :				Optional[Any]=0.02     ,  A_    :				Union[str, Any]=1E-1_2     ,  A_    :				Dict=1E-6     ,  A_    :				List[str]=0.0     ,  A_    :				int=224     ,  A_    :				Optional[Any]=None     ,  A_    :				List[str]=None     ,  **A_    :				Union[str, Any]     ,  )					-> Tuple:
      super().__init__(**A_	)

      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						patch_size
      SCREAMING_SNAKE_CASE__					     =						num_stages
      SCREAMING_SNAKE_CASE__					     =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE__					     =						[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						initializer_range
      SCREAMING_SNAKE_CASE__					     =						layer_norm_eps
      SCREAMING_SNAKE_CASE__					     =						layer_scale_init_value
      SCREAMING_SNAKE_CASE__					     =						drop_path_rate
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						["stem"] + [f"stage{idx}" for idx in range(1     ,  len(self.depths	) + 1	)]
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						get_aligned_output_features_output_indices(
          out_features=A_     ,  out_indices=A_     ,  stage_names=self.stage_names	)







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      version.parse("""1.11"""			)



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]	)







  @property
  def      _UpperCamelCase    (	self    :				Tuple	)					-> float:
      return 1E-5


