



'''simple docstring'''
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_			:     Any  	=  logging.get_logger(__name__)

snake_case_			:     str  	=  {'vocab_file': 'sentencepiece.model'}

snake_case_			:     Any  	=  {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

snake_case_			:     List[str]  	=  {
    'google/rembert': 256,
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      VOCAB_FILES_NAMES
	lowercase__      =      PRETRAINED_VOCAB_FILES_MAP
	lowercase__      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Dict=False			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    List[Any]="[CLS]"			,lowerCamelCase__ :    List[str]="[SEP]"			,lowerCamelCase__ :    Union[str, Any]="[UNK]"			,lowerCamelCase__ :    List[Any]="[SEP]"			,lowerCamelCase__ :    Dict="[PAD]"			,lowerCamelCase__ :    List[Any]="[CLS]"			,lowerCamelCase__ :    List[Any]="[MASK]"			,**lowerCamelCase__ :    Union[str, Any]			,):







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

							_UpperCamelCase :		List[str]			    =       do_lower_case
							_UpperCamelCase :		List[Any]			    =       remove_space
							_UpperCamelCase :		Optional[Any]			    =       keep_accents
							_UpperCamelCase :		str			    =       vocab_file

							_UpperCamelCase :		Optional[Any]			    =       spm.SentencePieceProcessor()
							self.sp_model.Load(lowerCamelCase__			)





	@property
	def      UpperCamelCase_  (			self :    int			):







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





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
							vocab.update(self.added_tokens_encoder			)
							return vocab





	def __getstate__(			self :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       self.__dict__.copy()
							_UpperCamelCase :		Any			    =       None
							return state





	def __setstate__(			self :    List[str]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       d
							_UpperCamelCase :		List[str]			    =       spm.SentencePieceProcessor()
							self.sp_model.Load(self.vocab_file			)





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    int=False			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       self.sp_model.EncodeAsPieces(lowerCamelCase__			)
							return pieces





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							return self.sp_model.PieceToId(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Optional[Any]			):







							'''simple docstring'''
							return self.sp_model.IdToPiece(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.sp_model.decode_pieces(lowerCamelCase__			)
							return out_string





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       [self.sep_token_id]
							_UpperCamelCase :		Tuple			    =       [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 :    Optional[Any]			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			,lowerCamelCase__ :    bool = 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(lowerCamelCase__			)) + [1] + ([0] * len(lowerCamelCase__			)) + [1]
							return [1] + ([0] * len(lowerCamelCase__			)) + [1]





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       [self.sep_token_id]
							_UpperCamelCase :		int			    =       [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 :    Dict			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str] = None			):







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

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

							return (out_vocab_file,)







'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase :		Any			    =       LxmertConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		int			    =       LxmertForPreTraining(UpperCAmelCase_							)

						# Load weights from tf checkpoint
						load_tf_weights_in_lxmert(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_			:     int  	=  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(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the pre-trained model. \nThis 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.config_file, args.pytorch_dump_path)


