






'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A      :       Tuple      =	logging.get_logger(__name__)

__A      :       int      =	{
    '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 					a__    (			lowerCamelCase ,						lowerCamelCase						):
					'''simple docstring'''







					_snake_case             =  'convnext'






					def __init__(self    :	Optional[Any]				,				lowercase__    :	Any=3				,				lowercase__    :	int=4				,				lowercase__    :	Dict=4				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Optional[int]=None				,				lowercase__    :	Dict="gelu"				,				lowercase__    :	Optional[Any]=0.02				,				lowercase__    :	Optional[int]=1e-12				,				lowercase__    :	Any=1e-6				,				lowercase__    :	Tuple=0.0				,				lowercase__    :	Any=224				,				lowercase__    :	Tuple=None				,				lowercase__    :	Union[str, Any]=None				,				**lowercase__    :	Union[str, Any]				,				)  ->       List[str]:
											super().__init__(**lowercase__					)

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







class 					a__    (			lowerCamelCase						):
					'''simple docstring'''







					_snake_case             =  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    :	int					)  ->       float:
											return 1e-5










'''simple docstring'''







def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      if n < 0:
            return 0
      # table[i] represents the number of ways to get to amount i
      __A		   =      [0] * (n + 1)

      # There is exactly 1 way to get to zero(You pick no coins).
      __A		   =      1

      # Pick all coins one by one and update table[] values
      # after the index greater than or equal to the value of the
      # picked coin
      for coin_val in s:
            for j in range(snake_case__							,			n + 1							):
                  table[j] += table[j - coin_val]

      return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


