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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE     =     {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}







class        UpperCAmelCase_					(				lowerCAmelCase__     ):




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

      super().__init__(
          vocab_file=_UpperCAmelCase   ,   tokenizer_file=_UpperCAmelCase   ,   do_lower_case=_UpperCAmelCase   ,   remove_space=_UpperCAmelCase   ,   keep_accents=_UpperCAmelCase   ,   bos_token=_UpperCAmelCase   ,   eos_token=_UpperCAmelCase   ,   unk_token=_UpperCAmelCase   ,   sep_token=_UpperCAmelCase   ,   pad_token=_UpperCAmelCase   ,   cls_token=_UpperCAmelCase   ,   mask_token=_UpperCAmelCase   ,   additional_special_tokens=_UpperCAmelCase   ,   **_UpperCAmelCase   ,   )

      a__  		=  3
      a__  		=  do_lower_case
      a__  		=  remove_space
      a__  		=  keep_accents
      a__  		=  vocab_file
      a__  		=  False if not self.vocab_file else True

      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__  		=  jieba
      a__  		=  str.maketrans(" \n"   ,   "\u2582\u2583" )




    def     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: List[int]   ,   lowerCAmelCase	: Optional[List[int]] = None )						->			List[int]:
      a__  		=  [self.sep_token_id]
      a__  		=  [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     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: List[int]   ,   lowerCAmelCase	: Optional[List[int]] = None )						->			List[int]:
      a__  		=  [self.sep_token_id]
      a__  		=  [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     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Optional[str] = None )						->			Tuple[str]:


      if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer." )

      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 ):
        copyfile(self.vocab_file   ,   _UpperCAmelCase )

      return (out_vocab_file,)




    def     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: Any   ,   *lowerCAmelCase	: Optional[Any]   ,   **lowerCAmelCase	: Optional[int] )						->			int:
      a__  		=  [
          " ".join([x.translate(self.translator ) for x in self.jieba.cut(_UpperCAmelCase   ,   cut_all=_UpperCAmelCase )] )
          for text in batch_text_or_text_pairs
      ]
      return super()._batch_encode_plus(_UpperCAmelCase   ,   *_UpperCAmelCase   ,   **_UpperCAmelCase )




    def     __lowerCamelCase			(       self	: Any   ,   *lowerCAmelCase	: List[str]   ,   **lowerCAmelCase	: List[str] )						->			int:
      a__  		=  super()._decode(*_UpperCAmelCase   ,   **_UpperCAmelCase )
      a__  		=  text.replace(" "   ,   "" ).replace("\u2582"   ,   " " ).replace("\u2583"   ,   "\n" )
      return text



from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





