





"""simple docstring"""
from PIL import Image



def      __lowerCamelCase   (							a_     :		Image							)			->      Image:
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Dict						=  image.size
      __SCREAMING_SNAKE_CASE   :int						=  0
      __SCREAMING_SNAKE_CASE   :Any						=  image.load()
      for i in range(a_							):
            for j in range(a_							):
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(a_							):
            for i in range(a_							):
                  __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  2_55 if pixels[i, j] > mean else 0
      return image


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






"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)