


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_   :				int						      = {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_   :				List[Any]						      = [
       'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CLIPSegModel',
       'CLIPSegPreTrainedModel',
       'CLIPSegTextModel',
       'CLIPSegVisionModel',
       'CLIPSegForImageSegmentation',
   ]

if TYPE_CHECKING:
   from .configuration_clipseg import (
       CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
       CLIPSegConfig,
       CLIPSegTextConfig,
       CLIPSegVisionConfig,
   )
   from .processing_clipseg import CLIPSegProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_clipseg import (
          CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
          CLIPSegForImageSegmentation,
          CLIPSegModel,
          CLIPSegPreTrainedModel,
          CLIPSegTextModel,
          CLIPSegVisionModel,
      )

else:
   import sys

   UpperCAmelCase_   :				Tuple						      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






UpperCAmelCase_   :				Optional[int]						      = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_   :				List[str]						      = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_   :				int						      = {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}


