
"""simple docstring"""





import math



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple:


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

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








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_a														=		{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TrOCRForCausalLM""",
				    """TrOCRPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
				from .processing_trocr import TrOCRProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






