

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:
       UpperCamelCase__     :					List[Any]								=			None


UpperCamelCase__     :					Any								=			logging.get_logger(__name__)

UpperCamelCase__     :					Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__     :					Optional[int]								=			{
    "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"
        ),
    },
}

UpperCamelCase__     :					List[Any]								=			{
    "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 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Optional[Any]         =     VOCAB_FILES_NAMES
  __lowerCAmelCase   :		Optional[Any]         =     PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase   :		Tuple         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase   :		int         =     ['input_ids', 'attention_mask']
  __lowerCAmelCase   :		str         =     XLMRobertaTokenizer

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

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

      SCREAMING_SNAKE_CASE_													=  vocab_file
      SCREAMING_SNAKE_CASE_													=  False if not self.vocab_file else True

  def   lowerCAmelCase__   (    self  ,						_A  ,						_A = None):

      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   lowerCAmelCase__   (    self  ,						_A  ,						_A = None):
      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]


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A = None):
      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(_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):
          copyfile(self.vocab_file  ,						_A)

      return (out_vocab_file,)





from typing import TYPE_CHECKING

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


UpperCamelCase__     :					Dict								=			{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[int]								=			[
           "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DonutSwinModel",
           "DonutSwinPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					List[Any]								=			["DonutFeatureExtractor"]
       UpperCamelCase__     :					Dict								=			["DonutImageProcessor"]


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       UpperCamelCase__     :					Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


