





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			:	int					    =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Union[str, Any]		       =  'mobilenet_v2'





      def __init__(					self			,				A_=3			,				A_=224			,				A_=1.0			,				A_=8			,				A_=8			,				A_=6			,				A_=32			,				A_=True			,				A_=True			,				A_="relu6"			,				A_=True			,				A_=0.8			,				A_=0.02			,				A_=0.0_01			,				A_=255			,				**A_			,				):
       '''simple docstring'''




       super().__init__(**A_  )

       if depth_multiplier <= 0:
        raise ValueError("depth_multiplier must be greater than zero."  )

       UpperCamelCase      :   Any			      =  num_channels
       UpperCamelCase      :   Optional[Any]			      =  image_size
       UpperCamelCase      :   Union[str, Any]			      =  depth_multiplier
       UpperCamelCase      :   Optional[Any]			      =  depth_divisible_by
       UpperCamelCase      :   List[Any]			      =  min_depth
       UpperCamelCase      :   Optional[Any]			      =  expand_ratio
       UpperCamelCase      :   List[Any]			      =  output_stride
       UpperCamelCase      :   str			      =  first_layer_is_expansion
       UpperCamelCase      :   str			      =  finegrained_output
       UpperCamelCase      :   int			      =  hidden_act
       UpperCamelCase      :   Dict			      =  tf_padding
       UpperCamelCase      :   Optional[Any]			      =  classifier_dropout_prob
       UpperCamelCase      :   List[Any]			      =  initializer_range
       UpperCamelCase      :   Union[str, Any]			      =  layer_norm_eps
       UpperCamelCase      :   List[Any]			      =  semantic_loss_ignore_index



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Optional[Any]		       =  version.parse('1.11'     )





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return OrderedDict([("pixel_values", {0: "batch"})]  )





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if self.task == "image-classification":
        return OrderedDict([("logits", {0: "batch"})]  )
       else:
        return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]  )





      @property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return 1e-4







def 						A_							(	_lowerCAmelCase	)		->					bool:
 return str(_lowerCAmelCase	) == str(_lowerCAmelCase	)[::-1]



def 						A_							(	_lowerCAmelCase	)		->					int:
 return int(_lowerCAmelCase	) + int(str(_lowerCAmelCase	)[::-1]	)



def 						A_							(	_lowerCAmelCase = 1_0000	)		->					int:
 UpperCamelCase      :   List[Any]			      =  []
 for num in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Union[str, Any]			      =  0
  UpperCamelCase      :   List[str]			      =  num
  while iterations < 50:
   UpperCamelCase      :   int			      =  sum_reverse(_lowerCAmelCase	)
   iterations += 1
   if is_palindrome(_lowerCAmelCase	):
    break
  else:
   lychrel_nums.append(_lowerCAmelCase	)
 return len(_lowerCAmelCase	)


if __name__ == "__main__":
    print(f"""{solution() = }""")
