"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	:		Dict   =   {
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   ['''FlavaFeatureExtractor''']
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   ['''FlavaImageProcessor''']
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   ['''FlavaProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Any   =   [
         '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''FlavaForPreTraining''',
         '''FlavaImageCodebook''',
         '''FlavaImageModel''',
         '''FlavaModel''',
         '''FlavaMultimodalModel''',
         '''FlavaPreTrainedModel''',
         '''FlavaTextModel''',
     ]

if TYPE_CHECKING:
     from .configuration_flava import (
         FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         FlavaConfig,
         FlavaImageCodebookConfig,
         FlavaImageConfig,
         FlavaMultimodalConfig,
         FlavaTextConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_flava import FlavaFeatureExtractor
          from .image_processing_flava import FlavaImageProcessor
          from .processing_flava import FlavaProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flava import (
              FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlavaForPreTraining,
              FlavaImageCodebook,
              FlavaImageModel,
              FlavaModel,
              FlavaMultimodalModel,
              FlavaPreTrainedModel,
              FlavaTextModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE	:		int   =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












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:
 a_ 		=  None


a_ 		=  logging.get_logger(__name__)

a_ 		=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_ 		=  {
    "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"
        ),
    },
}

a_ 		=  {
    "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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       VOCAB_FILES_NAMES
      lowerCAmelCase__      :			List[Any]     =       PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :			Optional[Any]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :			Optional[int]     =       ["input_ids", "attention_mask"]
      lowerCAmelCase__      :			Union[str, Any]     =       XLMRobertaTokenizer







      def __init__(  self       :			str			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			Any=None			,							snake_case       :			Union[str, Any]="<s>"			,							snake_case       :			Tuple="</s>"			,							snake_case       :			List[Any]="</s>"			,							snake_case       :			Any="<s>"			,							snake_case       :			str="<unk>"			,							snake_case       :			int="<pad>"			,							snake_case       :			Any="<mask>"			,							**snake_case       :			str			,							):
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase			=    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			,							)

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







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = 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    snake_case						(  self       :			Any			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = 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]







      def    snake_case						(  self       :			Dict			,							snake_case       :			str			,							snake_case       :			Optional[str] = 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(snake_case					):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory."					)
              return
          __UpperCamelCase			=    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,)






