

import math

def 			lowerCamelCase__			(       _a				,      _a):
	if 0 not in (x, y):
		# We use the relation x^y = y*log10(x), where 10 is the base.
		return y * math.logaa(_a)
	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')


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

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