from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:    Dict     = ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:    int     = [
				    '''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

				__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case__     :			int      			=  {
    '''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:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''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

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






