



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers


class 	a__       :


					"""simple docstring"""



					def __init__(		self     :Tuple      ,  lowercase__     :List[str]    ):
										lowercase						   =       vocab
										lowercase						   =       normalizers.BertNormalizer(
										    clean_text=lowercase__      ,  handle_chinese_chars=lowercase__      ,  strip_accents=lowercase__      ,  lowercase=lowercase__      ,  )
										try:
															import rjieba
										except ImportError:
															raise ImportError(
															    'You need to install rjieba to use RoFormerTokenizer. '
															    'See https://pypi.org/project/rjieba/ for installation.'    )
										lowercase						   =       rjieba

					def 				__UpperCAmelCase		(		self     :Optional[int]      ,  lowercase__     :int      ,  lowercase__     :NormalizedString    ):
										lowercase						   =       []

										# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
										for token, start, end in self.jieba.tokenize(str(lowercase__    )      ,  hmm=lowercase__    ):
															if token in self.vocab:
																				splits.append(normalized_string[start:end]    )
															else:
																				lowercase						   =       self.normalizers.normalize_str(lowercase__    ).split()
																				for token in token_list:
																									if token:
																														lowercase						   =       start + len(lowercase__    )
																														splits.append(normalized_string[start:end]    )
																														lowercase						   =       end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

										return splits



					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :PreTokenizedString    ):
										pretok.split(self.jieba_split    )











import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

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


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






