


'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    List[Any]          =    {
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    str          =    ["SqueezeBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    str          =    [
     "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "SqueezeBertForMaskedLM",
     "SqueezeBertForMultipleChoice",
     "SqueezeBertForQuestionAnswering",
     "SqueezeBertForSequenceClassification",
     "SqueezeBertForTokenClassification",
     "SqueezeBertModel",
     "SqueezeBertModule",
     "SqueezeBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 a_					:    List[str]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''






import warnings

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


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

