






import math







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[int]	,    snake_case_   :				Optional[int]  ):


	if 0 not in (x, y):
		# We use the relation x^y = y*log10(x), where 10 is the base.
		return y * math.logaa(_lowercase  )
	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.
	__lowerCamelCase   :     List[str] 		= """Enter the base and the power separated by a comma: """
	__lowerCamelCase		,       __lowerCamelCase   :     List[str] 		= map(int, input(prompt).split(""","""))
	__lowerCamelCase		,       __lowerCamelCase   :     Optional[int] 		= map(int, input(prompt).split(""","""))

	# We find the log of each number, using the function res(), which takes two
	# arguments.
	__lowerCamelCase   :     int 		= res(xa, ya)
	__lowerCamelCase   :     Any 		= 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case   							=						{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


