


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							:       Tuple										=	logging.get_logger(__name__)

snake_case							:       List[str]										=	{'''vocab_file''': '''spiece.model'''}

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







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=False   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="<unk>"   ,    _lowerCamelCase="<sep>"   ,    _lowerCamelCase="<pad>"   ,    _lowerCamelCase="<cls>"   ,    _lowerCamelCase="<mask>"   ,    _lowerCamelCase=["<eop>", "<eod>"]   ,    _lowerCamelCase = None   ,    **_lowerCamelCase   ,    ):
       a		:Tuple									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else mask_token

       a		:Optional[Any]									=			{} 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   ,    )

       a		:List[str]									=			3

       a		:str									=			do_lower_case
       a		:Dict									=			remove_space
       a		:Dict									=			keep_accents
       a		:Optional[int]									=			vocab_file

       a		:List[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.'''       )
       a		:Union[str, Any]									=			jieba
       a		:Optional[int]									=			str.maketrans(''' \n'''   ,    '''\u2582\u2583'''       )




 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return len(self.sp_model       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, 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       ):
       a		:Dict									=			self.__dict__.copy()
       a		:Dict									=			None
       return state




 def __setstate__(						self   ,    _lowerCamelCase       ):
       a		:str									=			d

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

       a		:Any									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
       self.sp_model.Load(self.vocab_file       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       if self.remove_space:
             a		:Optional[Any]									=			''' '''.join(inputs.strip().split()       )
       else:
             a		:Any									=			inputs
       a		:int									=			outputs.replace('''``'''   ,    '''"'''       ).replace('''\'\''''   ,    '''"'''       )

       if not self.keep_accents:
             a		:Optional[int]									=			unicodedata.normalize('''NFKD'''   ,    _lowerCamelCase       )
             a		:Optional[int]									=			''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase       )]       )
       if self.do_lower_case:
             a		:Union[str, Any]									=			outputs.lower()

       return outputs




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Optional[int]									=			self.preprocess_text(_lowerCamelCase       )
       a		:Any									=			self.sp_model.encode(_lowerCamelCase   ,    out_type=_lowerCamelCase       )
       a		:Tuple									=			[]
       for piece in pieces:
             if len(_lowerCamelCase       ) > 1 and piece[-1] == str(''','''       ) and piece[-2].isdigit():
                   a		: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:
                               a		:Union[str, Any]									=			cur_pieces[1:]
                         else:
                               a		:Optional[int]									=			cur_pieces[0][1:]
                   cur_pieces.append(piece[-1]       )
                   new_pieces.extend(_lowerCamelCase       )
             else:
                   new_pieces.append(_lowerCamelCase       )

       return new_pieces




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.sp_model.PieceToId(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return self.sp_model.IdToPiece(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			''''''.join(_lowerCamelCase       ).replace(_lowerCamelCase   ,    ''' '''       ).strip()
       return out_string




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:List[Any]									=			[self.sep_token_id]
       a		:Any									=			[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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = False       ):

       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Optional[int]									=			[self.sep_token_id]
       a		:List[str]									=			[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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       if not os.path.isdir(_lowerCamelCase       ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
             return
       a		: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:
                   a		:Any									=			self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase       )

       return (out_vocab_file,)




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Tuple									=			super()._decode(*_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Any									=			text.replace(''' '''   ,    ''''''       ).replace('''\u2582'''   ,    ''' '''       ).replace('''\u2583'''   ,    '''\n'''       )
       return text









import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
     from scipy.stats import pearsonr, spearmanr
     from sklearn.metrics import fa_score, matthews_corrcoef


snake_case							:       List[str]										=	(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      assert len(UpperCAmelCase_		) == len(UpperCAmelCase_		), F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''
      if task_name == "cola":
            return {"mcc": matthews_corrcoef(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "sst-2":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mrpc":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "sts-b":
            return pearson_and_spearman(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "qqp":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "mnli":
            return {"mnli/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mnli-mm":
            return {"mnli-mm/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "qnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "rte":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "wnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "hans":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      if len(UpperCAmelCase_		) != len(UpperCAmelCase_		):
            raise ValueError(F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''		)
      if task_name == "xnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)





