


'''simple docstring'''







from typing import TYPE_CHECKING

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


a__    :   Tuple								=			{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    :   List[str]								=			[
      "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "LayoutLMForMaskedLM",
      "LayoutLMForSequenceClassification",
      "LayoutLMForTokenClassification",
      "LayoutLMForQuestionAnswering",
      "LayoutLMModel",
      "LayoutLMPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    :   int								=			[
      "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFLayoutLMForMaskedLM",
      "TFLayoutLMForSequenceClassification",
      "TFLayoutLMForTokenClassification",
      "TFLayoutLMForQuestionAnswering",
      "TFLayoutLMMainLayer",
      "TFLayoutLMModel",
      "TFLayoutLMPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  a__    :   Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








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

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


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_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 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,)


