


'''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_albert import AlbertTokenizer
else:
      __UpperCAmelCase    :int				       =     None

__UpperCAmelCase    :Any				       =     logging.get_logger(__name__)
__UpperCAmelCase    :Tuple				       =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase    :int				       =     {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase    :Optional[Any]				       =     {
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

__UpperCAmelCase    :str				       =     "▁"



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				int							     =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE :				str							     =     AlbertTokenizer

     def __init__(       self  :	Optional[Any]							,				snake_case  :	Any=None							,				snake_case  :	List[str]=None							,				snake_case  :	str=True							,				snake_case  :	Optional[int]=True							,				snake_case  :	int=False							,				snake_case  :	str="[CLS]"							,				snake_case  :	Any="[SEP]"							,				snake_case  :	Union[str, Any]="<unk>"							,				snake_case  :	Tuple="[SEP]"							,				snake_case  :	Union[str, Any]="<pad>"							,				snake_case  :	Optional[Any]="[CLS]"							,				snake_case  :	Dict="[MASK]"							,				**snake_case  :	Optional[Any]							,				)		->							Optional[int]:
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          __UpperCAmelCase			:				int													=    (
              AddedToken(snake_case							,				lstrip=snake_case							,				rstrip=snake_case							,				normalized=snake_case    )
              if isinstance(snake_case							,				snake_case    )
              else mask_token
          )

          super().__init__(
              snake_case							,				tokenizer_file=snake_case							,				do_lower_case=snake_case							,				remove_space=snake_case							,				keep_accents=snake_case							,				bos_token=snake_case							,				eos_token=snake_case							,				unk_token=snake_case							,				sep_token=snake_case							,				pad_token=snake_case							,				cls_token=snake_case							,				mask_token=snake_case							,				**snake_case							,				)

          __UpperCAmelCase			:				Any													=    do_lower_case
          __UpperCAmelCase			:				Tuple													=    remove_space
          __UpperCAmelCase			:				str													=    keep_accents
          __UpperCAmelCase			:				Union[str, Any]													=    vocab_file
          __UpperCAmelCase			:				int													=    False if not self.vocab_file else True

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None    )		->							List[int]:
          __UpperCAmelCase			:				Optional[Any]													=    [self.sep_token_id]
          __UpperCAmelCase			:				str													=    [self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None    )		->							List[int]:
          __UpperCAmelCase			:				Dict													=    [self.sep_token_id]
          __UpperCAmelCase			:				List[Any]													=    [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    ) * [0] + len(token_ids_a + sep    ) * [1]






     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Optional[str] = None    )		->							Tuple[str]:
          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			:				Union[str, Any]													=    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,)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase    :Optional[Any]				       =     {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :Any				       =     ["LayoutLMv2TokenizerFast"]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     ["LayoutLMv2FeatureExtractor"]
      __UpperCAmelCase    :Optional[int]				       =     ["LayoutLMv2ImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :List[Any]				       =     [
          "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv2ForQuestionAnswering",
          "LayoutLMv2ForSequenceClassification",
          "LayoutLMv2ForTokenClassification",
          "LayoutLMv2Layer",
          "LayoutLMv2Model",
          "LayoutLMv2PreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase    :Any				       =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)