





import argparse

import torch

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


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   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__":
		lowercase_		:				Any				=						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.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






