


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case    :    List[Any]         =     logging.get_logger(__name__)

__snake_case    :    Tuple         =     {"""vocab_file""": """sentencepiece.model"""}

__snake_case    :    Optional[Any]         =     {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__snake_case    :    Any         =     {
    """google/rembert""": 2_56,
}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       List[str]   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase="[CLS]"     ,						_UpperCamelCase="[SEP]"     ,						_UpperCamelCase="[UNK]"     ,						_UpperCamelCase="[SEP]"     ,						_UpperCamelCase="[PAD]"     ,						_UpperCamelCase="[CLS]"     ,						_UpperCamelCase="[MASK]"     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(
												    do_lower_case=__a     ,						remove_space=__a     ,						keep_accents=__a     ,						bos_token=__a     ,						eos_token=__a     ,						unk_token=__a     ,						sep_token=__a     ,						pad_token=__a     ,						cls_token=__a     ,						mask_token=__a     ,						**__a     ,						)

												lowerCAmelCase__    						=						do_lower_case
												lowerCAmelCase__    						=						remove_space
												lowerCAmelCase__    						=						keep_accents
												lowerCAmelCase__    						=						vocab_file

												lowerCAmelCase__    						=						spm.SentencePieceProcessor()
												self.sp_model.Load(__a   )






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return len(self.sp_model   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.convert_ids_to_tokens(__a   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab






						def __getstate__(	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.__dict__.copy()
												lowerCAmelCase__    						=						None
												return state






						def __setstate__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						d
												lowerCAmelCase__    						=						spm.SentencePieceProcessor()
												self.sp_model.Load(self.vocab_file   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.sp_model.EncodeAsPieces(__a   )
												return pieces






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return self.sp_model.PieceToId(__a   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return self.sp_model.IdToPiece(__a   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.sp_model.decode_pieces(__a   )
												return out_string






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[self.sep_token_id]
												lowerCAmelCase__    						=						[self.cls_token_id]
												if token_ids_a is None:
																		return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False   ):






												"""simple docstring"""

												if already_has_special_tokens:
																		if token_ids_a is not None:
																								raise ValueError(
																								    'You should not supply a second sequence if the provided sequence of '
																								    'ids is already formatted with special tokens for the model.'   )
																		return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

												if token_ids_a is not None:
																		return [1] + ([0] * len(__a   )) + [1] + ([0] * len(__a   )) + [1]
												return [1] + ([0] * len(__a   )) + [1]






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[self.sep_token_id]
												lowerCAmelCase__    						=						[self.cls_token_id]

												if token_ids_a is None:
																		return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												if not os.path.isdir(__a   ):
																		logger.error('Vocabulary path ({}) should be a directory'.format(__a   )   )
																		return
												lowerCAmelCase__    						=						os.path.join(
												    __a     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

												if os.path.abspath(self.vocab_file   ) != os.path.abspath(__a   ):
																		copyfile(self.vocab_file     ,						__a   )

												return (out_vocab_file,)













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   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

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

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


if __name__ == "__main__":
					__snake_case    :    Union[str, 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."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





