






"""simple docstring"""







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					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model"""}

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







class  lowerCamelCase__ (	snake_case			):






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

          UpperCAmelCase          =	{} 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     ,)

          UpperCAmelCase          =	3

          UpperCAmelCase          =	do_lower_case
          UpperCAmelCase          =	remove_space
          UpperCAmelCase          =	keep_accents
          UpperCAmelCase          =	vocab_file

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






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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	{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 ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

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

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






    def     _UpperCamelCase							(			self     ,A ):
          if self.remove_space:
                UpperCAmelCase          =	""" """.join(inputs.strip().split() )
          else:
                UpperCAmelCase          =	inputs
          UpperCAmelCase          =	outputs.replace("""``"""     ,"""\"""" ).replace("""''"""     ,"""\"""" )

          if not self.keep_accents:
                UpperCAmelCase          =	unicodedata.normalize("""NFKD"""     ,A )
                UpperCAmelCase          =	"""""".join([c for c in outputs if not unicodedata.combining(A )] )
          if self.do_lower_case:
                UpperCAmelCase          =	outputs.lower()

          return outputs






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.preprocess_text(A )
          UpperCAmelCase          =	self.sp_model.encode(A     ,out_type=A )
          UpperCAmelCase          =	[]
          for piece in pieces:
                if len(A ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
                      UpperCAmelCase          =	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:
                                  UpperCAmelCase          =	cur_pieces[1:]
                            else:
                                  UpperCAmelCase          =	cur_pieces[0][1:]
                      cur_pieces.append(piece[-1] )
                      new_pieces.extend(A )
                else:
                      new_pieces.append(A )

          return new_pieces






    def     _UpperCamelCase							(			self     ,A ):
          return self.sp_model.PieceToId(A )






    def     _UpperCamelCase							(			self     ,A ):
          return self.sp_model.IdToPiece(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[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     ,A     ,A = None     ,A = 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     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[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     ,A     ,A = None ):
          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	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:
                      UpperCAmelCase          =	self.sp_model.serialized_model_proto()
                      fi.write(A )

          return (out_vocab_file,)






    def     _UpperCamelCase							(			self     ,*A     ,**A ):
          UpperCAmelCase          =	super()._decode(*A     ,**A )
          UpperCAmelCase          =	text.replace(""" """     ,"""""" ).replace("""\u2582"""     ,""" """ ).replace("""\u2583"""     ,"""\n""" )
          return text











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MBartForCausalLM""",
      """MBartForConditionalGeneration""",
      """MBartForQuestionAnswering""",
      """MBartForSequenceClassification""",
      """MBartModel""",
      """MBartPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TFMBartForConditionalGeneration""",
      """TFMBartModel""",
      """TFMBartPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FlaxMBartForConditionalGeneration""",
      """FlaxMBartForQuestionAnswering""",
      """FlaxMBartForSequenceClassification""",
      """FlaxMBartModel""",
      """FlaxMBartPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart import MBartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart_fast import MBartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mbart import (
        MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        MBartForCausalLM,
        MBartForConditionalGeneration,
        MBartForQuestionAnswering,
        MBartForSequenceClassification,
        MBartModel,
        MBartPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        FlaxMBartPreTrainedModel,
    )

else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



