code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowerCAmelCase__   	=				{
      '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:
      lowerCAmelCase__   	=				['MarkupLMTokenizerFast']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          '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
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{
      'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetaImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetaForObjectDetection',
          'DetaModel',
          'DetaPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .image_processing_deta import DetaImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  