





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__    :       Dict              ={'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Any              =['''DeiTFeatureExtractor''']
      A__    :       Optional[Any]              =['''DeiTImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       List[Any]              =[
          '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DeiTForImageClassification''',
          '''DeiTForImageClassificationWithTeacher''',
          '''DeiTForMaskedImageModeling''',
          '''DeiTModel''',
          '''DeiTPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       str              =[
          '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDeiTForImageClassification''',
          '''TFDeiTForImageClassificationWithTeacher''',
          '''TFDeiTForMaskedImageModeling''',
          '''TFDeiTModel''',
          '''TFDeiTPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deit import DeiTFeatureExtractor
            from .image_processing_deit import DeiTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deit import (
                DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeiTForImageClassification,
                DeiTForImageClassificationWithTeacher,
                DeiTForMaskedImageModeling,
                DeiTModel,
                DeiTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deit import (
                TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDeiTForImageClassification,
                TFDeiTForImageClassificationWithTeacher,
                TFDeiTForMaskedImageModeling,
                TFDeiTModel,
                TFDeiTPreTrainedModel,
            )


else:
      import sys

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













'''simple docstring'''






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(snake_case_       )							,		'''Tatoeba directory does not exist.'''       )
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @cached_property
      def     lowercase__   (      self      :			int  )    ->				Any:
            _lowerCAmelCase												=							tempfile.mkdtemp()
            return TatoebaConverter(save_dir=__snake_case  )




      @slow
      def     lowercase__   (      self      :			Dict  )    ->				int:
            self.resolver.convert_models(["""heb-eng"""]  )




      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.resolver.write_model_card("""opus-mt-he-en"""      ,      dry_run=__snake_case  )
            assert mmeta["long_pair"] == "heb-eng"






