


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__    					=      {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BigBirdPegasusForCausalLM''',
      '''BigBirdPegasusForConditionalGeneration''',
      '''BigBirdPegasusForQuestionAnswering''',
      '''BigBirdPegasusForSequenceClassification''',
      '''BigBirdPegasusModel''',
      '''BigBirdPegasusPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''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


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      '''▁'''

UpperCamelCase__    					=      {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

UpperCamelCase__    					=      {
    '''facebook/xglm-564M''': 2_0_4_8,
}




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']






      def __init__(					self				:    Optional[int]						,						_A				:    Optional[Any]						,						_A				:    Optional[Any]="<s>"						,						_A				:    List[str]="</s>"						,						_A				:    Optional[Any]="</s>"						,						_A				:    List[str]="<s>"						,						_A				:    Tuple="<unk>"						,						_A				:    List[str]="<pad>"						,						_A				:    Optional[Dict[str, Any]] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          UpperCAmelCase__			:		Optional[int]													=				7
          UpperCAmelCase__			:		Optional[int]													=				[f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

          UpperCAmelCase__			:		Tuple													=				kwargs.get('''additional_special_tokens'''						,						[] )
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          UpperCAmelCase__			:		Any													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(_A ) )
          UpperCAmelCase__			:		List[str]													=				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'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCAmelCase__			:		Any													=				1

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCAmelCase__			:		Any													=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          UpperCAmelCase__			:		int													=				len(self.sp_model )
          UpperCAmelCase__			:		Optional[int]													=				{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
          self.fairseq_tokens_to_ids.update(_A )

          UpperCAmelCase__			:		Tuple													=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def __getstate__(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.__dict__.copy()
          UpperCAmelCase__			:		Optional[int]													=				None
          UpperCAmelCase__			:		Optional[Any]													=				self.sp_model.serialized_model_proto()
          return state






      def __setstate__(					self				:    Union[str, Any]						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				d

          # for backward compatibility
          if not hasattr(self						,						'''sp_model_kwargs''' ):
              UpperCAmelCase__			:		Dict													=				{}

          UpperCAmelCase__			:		str													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    str						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None						,						_A				:    bool = False ):


          '''simple docstring'''


          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 None:
              return [1] + ([0] * len(_A ))
          return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A ))






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[self.sep_token_id]

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






      @property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    List[str]						,						_A				:    List[Any] ):


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__			:		Union[str, Any]													=				self.sp_model.PieceToId(_A )

          # 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 lowercase_							(					self				:    List[Any]						,						_A				:    str ):


          '''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 lowercase_							(					self				:    int						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				''''''.join(_A ).replace(_A						,						''' ''' ).strip()
          return out_string






      def lowercase_							(					self				:    Any						,						_A				:    str						,						_A				:    Optional[str] = None ):


          '''simple docstring'''


          if not os.path.isdir(_A ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          UpperCAmelCase__			:		List[str]													=				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__			:		List[Any]													=				self.sp_model.serialized_model_proto()
                  fi.write(_A )

          return (out_vocab_file,)
