






'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       '▁'

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'prophetnet.tokenizer'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = collections.OrderedDict()
      with open(__UpperCamelCase		,						"""r"""		,						encoding="""utf-8"""   ) as reader:
            UpperCamelCase      = reader.readlines()
      for index, token in enumerate(__UpperCamelCase   ):
            UpperCamelCase      = token.rstrip("""\n"""   )
            UpperCamelCase      = index
      return vocab






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="[SEP]"      ,		_SCREAMING_SNAKE_CASE="[SEP]"      ,		_SCREAMING_SNAKE_CASE="[SEP]"      ,		_SCREAMING_SNAKE_CASE="[UNK]"      ,		_SCREAMING_SNAKE_CASE="[PAD]"      ,		_SCREAMING_SNAKE_CASE="[CLS]"      ,		_SCREAMING_SNAKE_CASE="[MASK]"      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        UpperCamelCase      = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		sep_token=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		pad_token=_SCREAMING_SNAKE_CASE      ,		cls_token=_SCREAMING_SNAKE_CASE      ,		mask_token=_SCREAMING_SNAKE_CASE      ,		sp_model_kwargs=self.sp_model_kwargs      ,		**_SCREAMING_SNAKE_CASE      ,		)

        try:
              import sentencepiece as spm
        except ImportError:
              logger.warning(
                  """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                  """ pip install sentencepiece"""				)
              raise

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(str(_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        UpperCamelCase      = {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10				):
              UpperCamelCase      = F"[unused{i}]"
              UpperCamelCase      = 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        UpperCamelCase      = 12
        UpperCamelCase      = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
              self.unique_no_split_tokens.append(_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.__dict__.copy()
        UpperCamelCase      = None
        return state

  def __setstate__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = d
        try:
              import sentencepiece as spm
        except ImportError:
              logger.warning(
                  """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                  """ pip install sentencepiece"""				)
              raise

        # 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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






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

        if token_ids_a is None:
              return ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
        return ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				List[int]:



        """simple docstring"""






        UpperCamelCase      = [self.sep_token_id]

        if token_ids_a is None:
              return len(token_ids_a + sep				) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep				) * [0]

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return len(self.sp_model				) + self.fairseq_offset

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,		out_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        UpperCamelCase      = self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = """""".join(_SCREAMING_SNAKE_CASE				).replace(_SCREAMING_SNAKE_CASE      ,		""" """				).strip()
        return out_string

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






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

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

        return (out_vocab_file,)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				List[int]:



        """simple docstring"""






        if token_ids_a is None:
              return token_ids_a + [self.sep_token_id]
        UpperCamelCase      = [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep








'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<s>"      ,		_SCREAMING_SNAKE_CASE="</s>"      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        UpperCamelCase      = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		pad_token=_SCREAMING_SNAKE_CASE      ,		sp_model_kwargs=self.sp_model_kwargs      ,		**_SCREAMING_SNAKE_CASE      ,		)

        UpperCamelCase      = vocab_file

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.get_piece_size()

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {self.convert_ids_to_tokens(_SCREAMING_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"""






        UpperCamelCase      = self.__dict__.copy()
        UpperCamelCase      = None
        return state

  def __setstate__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        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   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,		out_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
        return token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        UpperCamelCase      = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
                    UpperCamelCase      = []
              else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
        out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
        return out_string.strip()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				List[int]:



        """simple docstring"""






        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






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

        UpperCamelCase      = [1]
        if token_ids_a is None:
              return ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones
        return ([0] * len(_SCREAMING_SNAKE_CASE				)) + ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






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

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

        return (out_vocab_file,)
