





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


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

UpperCAmelCase     :     Union[str, Any]							=	{'vocab_file': 'spiece.model'}

UpperCAmelCase     :     Any							=	{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}







class     __lowerCAmelCase	(   lowerCamelCase__):







      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__		,				)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : int  =AddedToken(__snake_case		,				lstrip=__snake_case		,				rstrip=__snake_case							) if isinstance(__snake_case		,				__snake_case							) else mask_token

        a__     : Tuple  ={} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=__snake_case		,				remove_space=__snake_case		,				keep_accents=__snake_case		,				bos_token=__snake_case		,				eos_token=__snake_case		,				unk_token=__snake_case		,				sep_token=__snake_case		,				pad_token=__snake_case		,				cls_token=__snake_case		,				mask_token=__snake_case		,				additional_special_tokens=__snake_case		,				sp_model_kwargs=self.sp_model_kwargs		,				**__snake_case		,				)

        a__     : List[Any]  =3

        a__     : List[Any]  =do_lower_case
        a__     : List[Any]  =remove_space
        a__     : List[Any]  =keep_accents
        a__     : List[Any]  =vocab_file

        a__     : Any  =spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(__snake_case							)

        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__     : Optional[Any]  =jieba
        a__     : List[str]  =str.maketrans(" \n"		,				"\u2582\u2583"							)







      @property
      # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        return len(self.sp_model							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : List[str]  ={self.convert_ids_to_tokens(__snake_case							): i for i in range(self.vocab_size							)}
        vocab.update(self.added_tokens_encoder							)
        return vocab







      def __getstate__(   self							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Dict  =self.__dict__.copy()
        a__     : int  =None
        return state







      def __setstate__(   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =d

        # for backward compatibility
        if not hasattr(self		,				"sp_model_kwargs"							):
          a__     : int  ={}

        a__     : Tuple  =spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(self.vocab_file							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




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

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

        return outputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : Dict  =self.preprocess_text(__snake_case							)
        a__     : Union[str, Any]  =self.sp_model.encode(__snake_case		,				out_type=__snake_case							)
        a__     : Tuple  =[]
        for piece in pieces:
          if len(__snake_case							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
            a__     : int  =self.sp_model.EncodeAsPieces(piece[:-1].replace(__snake_case		,				""							)							)
            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
              if len(cur_pieces[0]							) == 1:
                a__     : List[str]  =cur_pieces[1:]
              else:
                a__     : Union[str, Any]  =cur_pieces[0][1:]
            cur_pieces.append(piece[-1]							)
            new_pieces.extend(__snake_case							)
          else:
            new_pieces.append(__snake_case							)

        return new_pieces







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        return self.sp_model.PieceToId(__snake_case							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        return self.sp_model.IdToPiece(__snake_case							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Union[str, Any]  =''.join(__snake_case							).replace(__snake_case		,				" "							).strip()
        return out_string







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						int:



        '''simple docstring'''




        a__     : List[Any]  =[self.sep_token_id]
        a__     : Dict  =[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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False							)       ->						str:



        '''simple docstring'''




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

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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						str:



        '''simple docstring'''




        a__     : List[str]  =[self.sep_token_id]
        a__     : Optional[Any]  =[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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						int:



        '''simple docstring'''




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

        if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							) and os.path.isfile(self.vocab_file							):
          copyfile(self.vocab_file		,				__snake_case							)
        elif not os.path.isfile(self.vocab_file							):
          with open(__snake_case		,				"wb"							) as fi:
            a__     : Any  =self.sp_model.serialized_model_proto()
            fi.write(__snake_case							)

        return (out_vocab_file,)







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Any  =super()._decode(*__snake_case		,				**__snake_case							)
        a__     : List[Any]  =text.replace(" "		,				""							).replace("\u2582"		,				" "							).replace("\u2583"		,				"\n"							)
        return text













import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      field(default="""image-classification"""						,    metadata={"""include_in_asdict_even_if_is_default""": True})
      _lowercase    : ClassVar[Features]            =      Features({"""image""": Image()})
      _lowercase    : ClassVar[Features]            =      Features({"""labels""": ClassLabel})
      _lowercase    : str            =      "image"
      _lowercase    : str            =      "labels"







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        if self.label_column not in features:
          raise ValueError(F'''Column {self.label_column} is not present in features.'''							)
        if not isinstance(features[self.label_column]		,				lowerCAmelCase__							):
          raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''							)
        a__     : int  =copy.deepcopy(self							)
        a__     : Dict  =self.label_schema.copy()
        a__     : int  =features[self.label_column]
        a__     : str  =label_schema
        return task_template







      @property
      def        _lowercase  (   self							)       ->						Dict[str, str]:



        '''simple docstring'''




        return {
            self.image_column: "image",
            self.label_column: "labels",
        }






