





'''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


lowercase_					= logging.get_logger(__name__)

lowercase_					= """▁"""

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

lowercase_					= {
    """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"""
        ),
    }
}

lowercase_					= {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = ['''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 ,		)		->   None:
        # Mask token behave like a normal word, i.e. include the space before it
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else mask_token

        _SCREAMING_SNAKE_CASE									=			{} 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 ,		)

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(str(A       )       )
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			{"""<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
        _SCREAMING_SNAKE_CASE									=			1

        _SCREAMING_SNAKE_CASE									=			len(self.sp_model       ) + self.fairseq_offset
        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


    def __getstate__(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.__dict__.copy()
        _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
        return state


    def __setstate__(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			d

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

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:

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


    def 			snake_case_(		self ,		A ,		A = None ,		A = False       )		->   List[int]:

        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 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[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 			snake_case_(		self       )		->   Tuple:
        return len(self.sp_model       ) + self.fairseq_offset + 1  # Add the <mask> token


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			{self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
        vocab.update(self.added_tokens_encoder       )
        return vocab


    def 			snake_case_(		self ,		A       )		->   List[str]:
        return self.sp_model.encode(A ,		out_type=A       )


    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A       )		->   Dict:
        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 			snake_case_(		self ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			"""""".join(A       ).replace(A ,		""" """       ).strip()
        return out_string





    def 			snake_case_(		self ,		A ,		A = None       )		->   Tuple[str]:
        if not os.path.isdir(A       ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
            return
        _SCREAMING_SNAKE_CASE									=			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:
                _SCREAMING_SNAKE_CASE									=			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,
)


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

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

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

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

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

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """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

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



