





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       :   int				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   List[str]				       =							{"vocab_file": "spiece.model"}

__UpperCAmelCase       :   Dict				       =							{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    str							,				A :    str							,				A :    Any=False							,				A :    List[Any]=True							,				A :    List[str]=False							,				A :    Any="<s>"							,				A :    Union[str, Any]="</s>"							,				A :    Dict="<unk>"							,				A :    List[str]="<sep>"							,				A :    Optional[Any]="<pad>"							,				A :    Optional[int]="<cls>"							,				A :    Tuple="<mask>"							,				A :    int=["<eop>", "<eod>"]							,				A :    Optional[Dict[str, Any]] = None							,				**A :    Optional[Any]							,				):
         __snake_case:						List[Any]   =						AddedToken(A							,				lstrip=A							,				rstrip=A		) if isinstance(A							,				A		) else mask_token

         __snake_case:						Any   =						{} if sp_model_kwargs is None else sp_model_kwargs

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

         __snake_case:						str   =						3

         __snake_case:						str   =						do_lower_case
         __snake_case:						int   =						remove_space
         __snake_case:						Optional[Any]   =						keep_accents
         __snake_case:						Optional[int]   =						vocab_file

         __snake_case:						Dict   =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(A		)

         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."""		)
         __snake_case:						Union[str, Any]   =						jieba
         __snake_case:						str   =						str.maketrans(""" \n"""							,				"""\u2582\u2583"""		)




       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         return len(self.sp_model		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[Any]   =						{self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab




       def __getstate__(  self :    Any		):
         __snake_case:						Dict   =						self.__dict__.copy()
         __snake_case:						Optional[Any]   =						None
         return state




       def __setstate__(  self :    Optional[Any]							,				A :    List[Any]		):
         __snake_case:						Dict   =						d

         # for backward compatibility
         if not hasattr(self							,				"""sp_model_kwargs"""		):
           __snake_case:						List[str]   =						{}

         __snake_case:						List[Any]   =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    int		):
         if self.remove_space:
           __snake_case:						Union[str, Any]   =						""" """.join(inputs.strip().split()		)
         else:
           __snake_case:						Tuple   =						inputs
         __snake_case:						Any   =						outputs.replace("""``"""							,				"""\""""		).replace("""''"""							,				"""\""""		)

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

         return outputs




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

         return new_pieces




       def 							UpperCAmelCase__ (  self :    int							,				A :    Optional[int]		):
         return self.sp_model.PieceToId(A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    Union[str, Any]		):
         return self.sp_model.IdToPiece(A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    Any		):
         __snake_case:						Tuple   =						"""""".join(A		).replace(A							,				""" """		).strip()
         return out_string




       def 							UpperCAmelCase__ (  self :    Any							,				A :    List[int]							,				A :    Optional[List[int]] = None		):
         __snake_case:						int   =						[self.sep_token_id]
         __snake_case:						Optional[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 							UpperCAmelCase__ (  self :    Dict							,				A :    List[int]							,				A :    Optional[List[int]] = None							,				A :    bool = False		):

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

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




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    List[int]							,				A :    Optional[List[int]] = None		):
         __snake_case:						Tuple   =						[self.sep_token_id]
         __snake_case:						Dict   =						[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 :    Optional[int]							,				A :    str							,				A :    Optional[str] = None		):
         if not os.path.isdir(A		):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
           return
         __snake_case:						Optional[int]   =						os.path.join(
             A							,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

         return (out_vocab_file,)






       def 							UpperCAmelCase__ (  self :    List[Any]							,				*A :    Dict							,				**A :    Tuple		):
         __snake_case:						Any   =						super()._decode(*A							,				**A		)
         __snake_case:						Optional[Any]   =						text.replace(""" """							,				""""""		).replace("""\u2582"""							,				""" """		).replace("""\u2583"""							,				"""\n"""		)
         return text













import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class     __snake_case       (							unittest.TestCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[int]   =						load_tool("""text-classification"""		)
         self.tool.setup()
         __snake_case:						Dict   =						load_tool("""text-classification"""							,				remote=A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						self.tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						self.remote_tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						self.tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						self.remote_tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






