





'''simple docstring'''

import os
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 logging


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Optional[int]		=							'▁'

UpperCAmelCase					:		Union[str, Any]		=							{'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase					:		Tuple		=							{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

UpperCAmelCase					:		Dict		=							{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}





class lowerCamelCase						(UpperCAmelCase__					):
  _lowercase  :	Optional[Any]       					=				VOCAB_FILES_NAMES
  _lowercase  :	List[str]       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Optional[int]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[str]       					=				['input_ids', 'attention_mask']







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






   """simple docstring"""





   _snake_case		:	Optional[int]					     =     AddedToken(lowercase__     ,     lstrip=lowercase__     ,     rstrip=lowercase__		) if isinstance(lowercase__     ,     lowercase__		) else mask_token

   _snake_case		:	List[Any]					     =     {} if sp_model_kwargs is None else sp_model_kwargs

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

   _snake_case		:	Dict					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.Load(str(lowercase__		)		)
   _snake_case		:	List[Any]					     =     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'

   # Mimic fairseq token-to-id alignment for the first 4 token
   _snake_case		:	Optional[int]					     =     {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

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

   _snake_case		:	List[str]					     =     len(self.sp_model		) + self.fairseq_offset
   _snake_case		:	List[Any]					     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(		self		)		->						Tuple:






   """simple docstring"""





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







  def __setstate__(		self     ,     lowercase__		)		->						List[Any]:






   """simple docstring"""





   _snake_case		:	Optional[int]					     =     d

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

   _snake_case		:	Union[str, Any]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






   """simple docstring"""





   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _snake_case		:	List[str]					     =     [self.cls_token_id]
   _snake_case		:	Dict					     =     [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = False		)		->						List[int]:






   """simple docstring"""





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

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






   """simple docstring"""





   _snake_case		:	Dict					     =     [self.sep_token_id]
   _snake_case		:	List[Any]					     =     [self.cls_token_id]

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







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






   """simple docstring"""





   return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token







  def 				UpperCAmelCase_				(		self		)		->						Dict:






   """simple docstring"""





   _snake_case		:	Optional[Any]					     =     {self.convert_ids_to_tokens(lowercase__		): i for i in range(self.vocab_size		)}
   vocab.update(self.added_tokens_encoder		)
   return vocab







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






   """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[Any]:






   """simple docstring"""





   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   _snake_case		:	Dict					     =     self.sp_model.PieceToId(lowercase__		)

   # 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 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






   """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 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






   """simple docstring"""





   _snake_case		:	Dict					     =     """""".join(lowercase__		).replace(lowercase__     ,     ''' '''		).strip()
   return out_string







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






   """simple docstring"""





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

   if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase__		) and os.path.isfile(self.vocab_file		):
    copyfile(self.vocab_file     ,     lowercase__		)
   elif not os.path.isfile(self.vocab_file		):
    with open(lowercase__     ,     '''wb'''		) as fi:
     _snake_case		:	Union[str, Any]					     =     self.sp_model.serialized_model_proto()
     fi.write(lowercase__		)

   return (out_vocab_file,)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


