





from typing import TYPE_CHECKING

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


a_    :				Any					=			{
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a_    :				List[str]					=			['MarkupLMTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a_    :				int					=			[
        'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MarkupLMForQuestionAnswering',
        'MarkupLMForSequenceClassification',
        'MarkupLMForTokenClassification',
        'MarkupLMModel',
        'MarkupLMPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
    from .feature_extraction_markuplm import MarkupLMFeatureExtractor
    from .processing_markuplm import MarkupLMProcessor
    from .tokenization_markuplm import MarkupLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_markuplm_fast import MarkupLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_markuplm import (
            MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarkupLMForQuestionAnswering,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMModel,
            MarkupLMPreTrainedModel,
        )


else:
    import sys

    a_    :				Tuple					=			_LazyModule(__name__, globals()['__file__'], _import_structure)






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)