
import math







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Tuple:
       if 0 not in (x, y):
              # We use the relation x^y = y*log10(x), where 10 is the base.
              return y * math.logaa(SCREAMING_SNAKE_CASE__    )
       else:
              if x == 0:  # 0 raised to any number is 0
                     return 0
              elif y == 0:
                     return 1  # any number raised to 0 is 1
       raise AssertionError("""This should never happen"""    )


if __name__ == "__main__":  # Main function
  # Read two numbers from input and typecast them to int using map function.
  # Here x is the base and y is the power.
  lowercase  : List[str]						   =							"""Enter the base and the power separated by a comma: """
  lowercase   ,  lowercase  : Optional[int]						   =							map(int, input(prompt).split(""","""))
  lowercase   ,  lowercase  : Optional[int]						   =							map(int, input(prompt).split(""","""))

  # We find the log of each number, using the function res(), which takes two
  # arguments.
  lowercase  : Any						   =							res(xa, ya)
  lowercase  : str						   =							res(xa, ya)

  # We check for the largest number
  if resa > resa:
    print("""Largest number is""", xa, """^""", ya)
  elif resa > resa:
    print("""Largest number is""", xa, """^""", ya)
  else:
    print("""Both are equal""")


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


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

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

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
