
import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     BertConfig.from_json_file(lowerCamelCase__       )
				print(F'Building PyTorch model from configuration: {config}'       )
				lowerCamelCase_         =     BertForPreTraining(lowerCamelCase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				# Save pytorch-model
				print(F'Save PyTorch model to {pytorch_dump_path}'       )
				torch.save(model.state_dict()      ,					lowerCamelCase__       )


if __name__ == "__main__":
			__A							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--bert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained BERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			__A							=parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




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''')


