
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      len(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      sum(_lowerCamelCase)

							__UpperCamelCase			:     List[str]         =      [[False for x in range(s + 1)] for y in range(n + 1)]

							for i in range(1		,						n + 1):
														__UpperCamelCase			:     str         =      True

							for i in range(1		,						s + 1):
														__UpperCamelCase			:     Any         =      False

							for i in range(1		,						n + 1):
														for j in range(1		,						s + 1):
																					__UpperCamelCase			:     str         =      dp[i][j - 1]

																					if arr[i - 1] <= j:
																												__UpperCamelCase			:     List[Any]         =      dp[i][j] or dp[i - 1][j - arr[i - 1]]

							for j in range(int(s / 2)		,						-1		,						-1):
														if dp[n][j] is True:
																					__UpperCamelCase			:     Tuple         =      s - 2 * j
																					break

							return diff

import argparse

import torch

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


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      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						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)