

'''simple docstring'''




from typing import TYPE_CHECKING

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


_a					:			Any							=    {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Dict							=    ['ReformerTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			str							=    ['ReformerTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			str							=    [
      'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ReformerAttention',
      'ReformerForMaskedLM',
      'ReformerForQuestionAnswering',
      'ReformerForSequenceClassification',
      'ReformerLayer',
      'ReformerModel',
      'ReformerModelWithLMHead',
      'ReformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  _a					:			Optional[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


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



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			*a_ ,			**a_  )				->					None:


    """simple docstring"""
    warnings.warn(
        'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
        ' use DonutImageProcessor instead.' ,			a_ ,			)
    super().__init__(*a_ ,			**a_  )


