





import math






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Any     ):
		if 0 not in (x, y):
				# We use the relation x^y = y*log10(x), where 10 is the base.
				return y * math.logaa(UpperCamelCase__     )
		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 					=      'Enter the base and the power separated by a comma: '
	_lowerCamelCase					,							_lowerCamelCase 					=      map(int, input(prompt).split(','))
	_lowerCamelCase					,							_lowerCamelCase 					=      map(int, input(prompt).split(','))

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






import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""				,					__A				,					)
											SCREAMING_SNAKE_CASE__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""		)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""		)

									super().__init__(__A				,					__A		)


							def __call__(				self     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""simple docstring"""



									if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""		)

									if text is not None:
											if isinstance(__A				,					__A		) or (isinstance(__A				,					__A		) and not isinstance(text[0]				,					__A		)):
													SCREAMING_SNAKE_CASE__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   max([len(__A		) for t in text]		)

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(__A		) != max_num_queries:
																	SCREAMING_SNAKE_CASE__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)
															encodings.append(__A		)
											else:
													raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""		)

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""attention_mask"""] for encoding in encodings]				,					dim=0		)

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											else:
													raise ValueError("""Target return tensor type could not be returned"""		)

											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   self.image_processor(
											    __A				,					return_tensors=__A				,					**__A		).pixel_values
											SCREAMING_SNAKE_CASE__								=   query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Dict		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor.post_process(*__A				,					**__A		)


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Dict				,					**__A     :List[str]		) -> Optional[Any]:



									"""simple docstring"""



									return self.image_processor.post_process_object_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									return self.image_processor.post_process_image_guided_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,					__A				,					)
									return self.image_processor_class


							@property
							def   _snake_case	(				self     :Any		) -> Optional[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,					__A				,					)
									return self.image_processor