



'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


snake_case_ :	int         =  logging.get_logger(__name__)

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

snake_case_ :	Union[str, Any]         =  {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}






class 							lowercase__	(							lowercase   ):





	def __init__( self					:				Union[str, Any] ,lowerCamelCase__					:				Optional[Any] ,lowerCamelCase__					:				Optional[Any]=False ,lowerCamelCase__					:				Union[str, Any]=True ,lowerCamelCase__					:				str=False ,lowerCamelCase__					:				int="<s>" ,lowerCamelCase__					:				str="</s>" ,lowerCamelCase__					:				List[str]="<unk>" ,lowerCamelCase__					:				List[Any]="<sep>" ,lowerCamelCase__					:				Dict="<pad>" ,lowerCamelCase__					:				Dict="<cls>" ,lowerCamelCase__					:				str="<mask>" ,lowerCamelCase__					:				Dict=["<eop>", "<eod>"] ,lowerCamelCase__					:				Optional[Dict[str, Any]] = None ,**lowerCamelCase__					:				Any ,):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else mask_token

							_UpperCamelCase					:		Dict 				=   {} if sp_model_kwargs is None else sp_model_kwargs

							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__ ,additional_special_tokens=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase__ ,)

							_UpperCamelCase					:		Union[str, Any] 				=   3

							_UpperCamelCase					:		int 				=   do_lower_case
							_UpperCamelCase					:		str 				=   remove_space
							_UpperCamelCase					:		str 				=   keep_accents
							_UpperCamelCase					:		Any 				=   vocab_file

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

							try:
													import jieba
							except ModuleNotFoundError as error:
													raise error.__class__(
													    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
													    'See https://pypi.org/project/jieba/ for installation.' )
							_UpperCamelCase					:		Tuple 				=   jieba
							_UpperCamelCase					:		Optional[Any] 				=   str.maketrans(' \n' ,'\u2582\u2583' )





	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def 	UpperCamelCase_  ( self					:				int ):







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





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   {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					:				List[Any] ):







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





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







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   d

							# for backward compatibility
							if not hasattr(self ,'sp_model_kwargs' ):
													_UpperCamelCase					:		List[Any] 				=   {}

							_UpperCamelCase					:		Optional[int] 				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )





	def 	UpperCamelCase_  ( self					:				int ,lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							if self.remove_space:
													_UpperCamelCase					:		str 				=   ' '.join(inputs.strip().split() )
							else:
													_UpperCamelCase					:		Union[str, Any] 				=   inputs
							_UpperCamelCase					:		List[str] 				=   outputs.replace('``' ,'"' ).replace('\'\'' ,'"' )

							if not self.keep_accents:
													_UpperCamelCase					:		List[str] 				=   unicodedata.normalize('NFKD' ,lowerCamelCase__ )
													_UpperCamelCase					:		Any 				=   ''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase__ )] )
							if self.do_lower_case:
													_UpperCamelCase					:		Any 				=   outputs.lower()

							return outputs





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







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   self.preprocess_text(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   []
							for piece in pieces:
													if len(lowerCamelCase__ ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
																			_UpperCamelCase					:		Union[str, Any] 				=   self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__ ,'' ) )
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																									if len(cur_pieces[0] ) == 1:
																															_UpperCamelCase					:		Tuple 				=   cur_pieces[1:]
																									else:
																															_UpperCamelCase					:		Optional[int] 				=   cur_pieces[0][1:]
																			cur_pieces.append(piece[-1] )
																			new_pieces.extend(lowerCamelCase__ )
													else:
																			new_pieces.append(lowerCamelCase__ )

							return new_pieces





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				int ):







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





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







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





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







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   ''.join(lowerCamelCase__ ).replace(lowerCamelCase__ ,' ' ).strip()
							return out_string





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







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   [self.sep_token_id]
							_UpperCamelCase					:		Tuple 				=   [self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





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







							'''simple docstring'''

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__ ,token_ids_a=lowerCamelCase__ ,already_has_special_tokens=lowerCamelCase__ )

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





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







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   [self.sep_token_id]
							_UpperCamelCase					:		int 				=   [2]

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





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







							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__ ):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
													return
							_UpperCamelCase					:		str 				=   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__ ) and os.path.isfile(self.vocab_file ):
													copyfile(self.vocab_file ,lowerCamelCase__ )
							elif not os.path.isfile(self.vocab_file ):
													with open(lowerCamelCase__ ,'wb' ) as fi:
																			_UpperCamelCase					:		str 				=   self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__ )

							return (out_vocab_file,)





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,*lowerCamelCase__					:				Dict ,**lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   super()._decode(*lowerCamelCase__ ,**lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   text.replace(' ' ,'' ).replace('\u2582' ,' ' ).replace('\u2583' ,'\n' )
							return text







import argparse

import torch

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


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

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


if __name__ == "__main__":
 a_   :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(
     "--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."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






