
"""simple docstring"""

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

import sentencepiece as spm

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


a         =      logging.get_logger(__name__)

a         =      {'''vocab_file''': '''spiece.model'''}

a         =      {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

a         =      {'''bert_for_seq_generation''': 512}





class     lowercase_	(					UpperCamelCase__							):



 '''simple docstring'''
 UpperCAmelCase   :					List[str]				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					Dict				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					List[str]				  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase   :					List[int]				  =     []
 UpperCAmelCase   :					List[Any]				  =     ['''input_ids''', '''attention_mask''']

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	int="<s>"   ,							_UpperCAmelCase	:	Dict="</s>"   ,							_UpperCAmelCase	:	Dict="<unk>"   ,							_UpperCAmelCase	:	Optional[Any]="<pad>"   ,							_UpperCAmelCase	:	str="<::::>"   ,							_UpperCAmelCase	:	Optional[Dict[str, Any]] = None   ,							**_UpperCAmelCase	:	Optional[Any]   ,							):
       _A        =       {} if sp_model_kwargs is None else sp_model_kwargs

       # Add extra_ids to the special token list
       super().__init__(
           bos_token=UpperCamelCase_   ,							eos_token=UpperCamelCase_   ,							unk_token=UpperCamelCase_   ,							pad_token=UpperCamelCase_   ,							sep_token=UpperCamelCase_   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**UpperCamelCase_   ,							)

       _A        =       vocab_file

       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(UpperCamelCase_		)

 @property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       return self.sp_model.get_piece_size()

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       {self.convert_ids_to_tokens(UpperCamelCase_		): i for i in range(self.vocab_size		)}
       vocab.update(self.added_tokens_encoder		)
       return vocab

 def __getstate__(			self	:	str		):
       _A        =       self.__dict__.copy()
       _A        =       None
       return state

 def __setstate__(			self	:	str   ,							_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       d

       # for backward compatibility
       if not hasattr(self   ,							'sp_model_kwargs'		):
             _A        =       {}

       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(self.vocab_file		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str		):
       return self.sp_model.encode(UpperCamelCase_   ,							out_type=UpperCamelCase_		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Tuple		):
       return self.sp_model.piece_to_id(UpperCamelCase_		)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.sp_model.IdToPiece(UpperCamelCase_		)
       return token

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Any		):
       _A        =       []
       _A        =       ''
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(UpperCamelCase_		) + token
                   _A        =       []
             else:
                   current_sub_tokens.append(UpperCamelCase_		)
       out_string += self.sp_model.decode(UpperCamelCase_		)
       return out_string.strip()



 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str] = None		):







       if not os.path.isdir(UpperCamelCase_		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             return
       _A        =       os.path.join(
           UpperCamelCase_   ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase_		) and os.path.isfile(self.vocab_file		):
             copyfile(self.vocab_file   ,							UpperCamelCase_		)
       elif not os.path.isfile(self.vocab_file		):
             with open(UpperCamelCase_   ,							'wb'		) as fi:
                   _A        =       self.sp_model.serialized_model_proto()
                   fi.write(UpperCamelCase_		)

       return (out_vocab_file,)







"""simple docstring"""

import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


a         =      {
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _A        =       finetuning_task.lower() if finetuning_task is not None else ''
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''							)
            _A        =       finetuning_task
            _A        =       GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A        =       XLNetForSequenceClassification(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(_snake_case    ,  _snake_case    ,  _snake_case							)

      # Save pytorch-model
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      print(F'''Save PyTorch model to {os.path.abspath(_snake_case							)}'''							)
      torch.save(model.state_dict()    ,  _snake_case							)
      print(F'''Save configuration file to {os.path.abspath(_snake_case							)}'''							)
      with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
            f.write(config.to_json_string()							)


if __name__ == "__main__":
       a         =      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(
           '''--xlnet_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained XLNet model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--finetuning_task''',
           default=None,
           type=str,
           help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
       )
       a         =      parser.parse_args()
       print(args)

       convert_xlnet_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
       )

