





"""simple docstring"""




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   lowerCamelCase_	:		Dict         =   None


lowerCamelCase_	:		List[Any]         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[str]         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_	:		Dict         =   {
    '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'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase_	:		Dict         =   {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  VOCAB_FILES_NAMES
      lowercase_ :					str             =  PRETRAINED_VOCAB_FILES_MAP
      lowercase_ :					Dict             =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase_ :					List[Any]             =  ["""input_ids""", """attention_mask"""]
      lowercase_ :					Union[str, Any]             =  XLMRobertaTokenizer

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

        """simple docstring"""

        A_    :			List[Any]									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else mask_token

        super().__init__(
            snake_case_   , tokenizer_file=snake_case_   , bos_token=snake_case_   , eos_token=snake_case_   , sep_token=snake_case_   , cls_token=snake_case_   , unk_token=snake_case_   , pad_token=snake_case_   , mask_token=snake_case_   , **snake_case_   , )

        A_    :			Any									=    vocab_file
        A_    :			str									=    False if not self.vocab_file else True

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        A_    :			int									=    [self.sep_token_id]
        A_    :			str									=    [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]

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.'    )

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

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
          copyfile(self.vocab_file   , snake_case_    )

        return (out_vocab_file,)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

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



