






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_       ):






				'''simple docstring'''
				# Initialise PyTorch model
				lowercase__     				=    BertConfig.from_json_file(lowerCamelCase_       )
				print(F"""Building PyTorch model from configuration: {config}"""       )
				lowercase__     				=    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__			:    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.'
							)
							A__			:    Any         =				parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								# we need a list not a string, so do something to change the type
								lowercase__     				=    arr.split(''','''						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								for i in range(1,						len(self.array						)						):
												lowercase__     				=    max(
												    int(self.array[i]						) + sum_value[i - 1],						int(self.array[i]						)						)
												lowercase__     				=    max(sum_value[i],						rear[i - 1]						)
								return rear[len(self.array						) - 1]


if __name__ == "__main__":
							A__			:    Dict         =				input('please input some numbers:')
							A__			:    Union[str, Any]         =				SubArray(whole_array)
							A__			:    int         =				array.solve_sub_array()
							print(('the results is:', re))




