


import math






def 		_A		(		_lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     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.
    __snake_case   							=						'''Enter the base and the power separated by a comma: '''
    __snake_case							,	__snake_case   							=						map(int, input(prompt).split(''','''))
    __snake_case							,	__snake_case   							=						map(int, input(prompt).split(''','''))

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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


