





import argparse

import torch

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


logging.set_verbosity_info()





def       _UpperCamelCase       (a__				:Union[str, Any]				,     a__				:str				,     a__				:List[Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     BertConfig.from_json_file(a__    )
							print(f"""Building PyTorch model from configuration: {config}"""    )
							UpperCamelCase__			       =     BertForPreTraining(a__    )

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

							# Save pytorch-model
							print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
							torch.save(model.state_dict()				,     a__    )


if __name__ == "__main__":
					UpperCamelCase__     =	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."
					)
					UpperCamelCase__     =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









def       _UpperCamelCase       (a__				:str    ):






							"""simple docstring"""





							UpperCamelCase__			       =     0

							# if input_string is "aba" than new_input_string become "a|b|a"
							UpperCamelCase__			       =     """"""
							UpperCamelCase__			       =     """"""

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(a__    ) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							UpperCamelCase__       ,       UpperCamelCase__			       =     0, 0

							# length[i] shows the length of palindromic substring with center i
							UpperCamelCase__			       =     [1 for i in range(len(a__    )    )]

							# for each character in new_string find corresponding palindromic string
							UpperCamelCase__			       =     0
							for j in range(len(a__    )    ):
														UpperCamelCase__			       =     1 if j > r else min(length[l + r - j] // 2				,     r - j + 1    )
														while (
														    j - k >= 0
														    and j + k < len(a__    )
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														UpperCamelCase__			       =     2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					UpperCamelCase__			       =     j - k + 1  # noqa: E741
																					UpperCamelCase__			       =     j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					UpperCamelCase__			       =     length[j]
																					UpperCamelCase__			       =     j

    # create that string
							UpperCamelCase__			       =     new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
					import doctest

					doctest.testmod()




