






"""simple docstring"""

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:Union[str, Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     BertConfig.from_json_file(__lowerCamelCase   )
						print(f'''Building PyTorch model from configuration: {config}'''   )
						__magic_name__            =     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    			=       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    			=       parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(a - b   ) for a, b in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )






def _lowerCAmelCase	(  __lowerCamelCase:list[float]   ):

						'''simple docstring'''






						if point:
												if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
																		for item in point:
																								if not isinstance(__lowerCamelCase ,						(int, float)   ):
																														__magic_name__            =     (
																														    "Expected a list of numbers as input, found "
																														    f'''{type(__lowerCamelCase   ).__name__}'''
																														)
																														raise TypeError(__lowerCamelCase   )
												else:
																		__magic_name__            =     f'''Expected a list of numbers as input, found {type(__lowerCamelCase   ).__name__}'''
																		raise TypeError(__lowerCamelCase   )
						else:
												raise ValueError("Missing an input"   )






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(x - y   ) for x, y in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



