
"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase_						   =   {
    'configuration_esm': ['ESM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EsmConfig'],
    'tokenization_esm': ['EsmTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EsmForMaskedLM',
          'EsmForSequenceClassification',
          'EsmForTokenClassification',
          'EsmModel',
          'EsmPreTrainedModel',
      ]
      UpperCamelCase_						   =   ['EsmForProteinFolding', 'EsmFoldPreTrainedModel']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFEsmForMaskedLM',
          'TFEsmForSequenceClassification',
          'TFEsmForTokenClassification',
          'TFEsmModel',
          'TFEsmPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure)

"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase_						   =   {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileViTV2ForImageClassification',
          'MobileViTV2ForSemanticSegmentation',
          'MobileViTV2Model',
          'MobileViTV2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mobilevitva import (
          MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileViTVaConfig,
          MobileViTVaOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevitva import (
                MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTVaForImageClassification,
                MobileViTVaForSemanticSegmentation,
                MobileViTVaModel,
                MobileViTVaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)