


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


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''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_     ):
       lowerCamelCase__:  Union[str, Any]				     =  """convnext"""

       def __init__( self   :  str   ,				lowerCamelCase_   :  Union[str, Any]=3   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  int=None   ,				lowerCamelCase_   :  Optional[Any]=None   ,				lowerCamelCase_   :  Dict="gelu"   ,				lowerCamelCase_   :  str=0.0_2   ,				lowerCamelCase_   :  Dict=1E-12   ,				lowerCamelCase_   :  List[str]=1E-6   ,				lowerCamelCase_   :  Union[str, Any]=0.0   ,				lowerCamelCase_   :  Optional[Any]=2_2_4   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Dict=None   ,				**lowerCamelCase_   :  List[Any]   ,				):
            super().__init__(**lowerCamelCase_     )

            a_     :  List[str]				      =     num_channels
            a_     :  int				      =     patch_size
            a_     :  List[Any]				      =     num_stages
            a_     :  Optional[int]				      =     [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
            a_     :  Any				      =     [3, 3, 9, 3] if depths is None else depths
            a_     :  Any				      =     hidden_act
            a_     :  Dict				      =     initializer_range
            a_     :  str				      =     layer_norm_eps
            a_     :  Union[str, Any]				      =     layer_scale_init_value
            a_     :  int				      =     drop_path_rate
            a_     :  Tuple				      =     image_size
            a_     :  Tuple				      =     ["""stem"""] + [F'''stage{idx}''' for idx in range(1   ,				len(self.depths     ) + 1     )]
            a_      ,      a_     :  List[str]				      =     get_aligned_output_features_output_indices(
                out_features=lowerCamelCase_   ,				out_indices=lowerCamelCase_   ,				stage_names=self.stage_names     )


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  Tuple				     =  version.parse("""1.11"""     )

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

       @property
       def     UpperCAmelCase( self   :  Optional[Any]     ):
            return 1E-5





_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

