






"""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					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCamelCase					      =  {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

_UpperCamelCase					      =  {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_UpperCamelCase					      =  """▁"""







class  lowerCamelCase__ (	snake_case			):
				SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






				def __init__(			self     ,A     ,A="<s>"     ,A="</s>"     ,A="</s>"     ,A="<s>"     ,A="<unk>"     ,A="<pad>"     ,A="<mask>"     ,A = None     ,**A     ,):
										# Mask token behave like a normal word, i.e. include the space before it
										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__(
										    bos_token=A     ,eos_token=A     ,unk_token=A     ,sep_token=A     ,cls_token=A     ,pad_token=A     ,mask_token=A     ,sp_model_kwargs=self.sp_model_kwargs     ,**A     ,)

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

										UpperCAmelCase          =	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

										UpperCAmelCase          =	len(self.sp_model ) - 1
										UpperCAmelCase          =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






				def     _UpperCamelCase							(			self     ,A     ,A = None ):

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										UpperCAmelCase          =	[self.cls_token_id]
										UpperCAmelCase          =	[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep






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






				def     _UpperCamelCase							(			self     ,A     ,A = None ):
										UpperCAmelCase          =	[self.sep_token_id]
										UpperCAmelCase          =	[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 ):
										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     _UpperCamelCase							(			self     ,A ):
										return self.sp_model.encode(A     ,out_type=A )






				def     _UpperCamelCase							(			self     ,A ):
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										UpperCAmelCase          =	self.sp_model.PieceToId(A )

										return spm_id if spm_id else self.unk_token_id






				def     _UpperCamelCase							(			self     ,A ):
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(A )






				def     _UpperCamelCase							(			self     ,A ):
										UpperCAmelCase          =	[]
										UpperCAmelCase          =	""""""
										UpperCAmelCase          =	False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(A ) + token
																						UpperCAmelCase          =	True
																						UpperCAmelCase          =	[]
																else:
																						current_sub_tokens.append(A )
																						UpperCAmelCase          =	False
										out_string += self.sp_model.decode(A )
										return out_string.strip()






				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     ,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,)











"""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__)



