
from typing import TYPE_CHECKING

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


__A				={'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''DeiTFeatureExtractor''']
			__A				=['''DeiTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''DeiTForImageClassification''',
			    '''DeiTForImageClassificationWithTeacher''',
			    '''DeiTForMaskedImageModeling''',
			    '''DeiTModel''',
			    '''DeiTPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFDeiTForImageClassification''',
			    '''TFDeiTForImageClassificationWithTeacher''',
			    '''TFDeiTForMaskedImageModeling''',
			    '''TFDeiTModel''',
			    '''TFDeiTPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_deit import DeiTFeatureExtractor
						from .image_processing_deit import DeiTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deit import (
						    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						    DeiTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deit import (
						    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDeiTForImageClassification,
						    TFDeiTForImageClassificationWithTeacher,
						    TFDeiTForMaskedImageModeling,
						    TFDeiTModel,
						    TFDeiTPreTrainedModel,
						)


else:
			import sys

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




import math





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


