





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    '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__      =[
           '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__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from PIL import Image






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							image.size
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							image.load()
     for i in range(__lowerCamelCase	):
          for j in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__lowerCamelCase	):
          for i in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	int						    =							255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
       UpperCamelCase__      =mean_threshold(Image.open('path_to_image').convert('L'))
       image.save('output_image_path')