code: 
  
  
  
  
  
  
  import os
  
  from packaging import version
  
  from .. import __version__
  from .accelerate_utils import apply_forward_hook
  from .constants import (
      CONFIG_NAME,
      DEPRECATED_REVISION_ARGS,
      DIFFUSERS_CACHE,
      DIFFUSERS_DYNAMIC_MODULE_NAME,
      FLAX_WEIGHTS_NAME,
      HF_MODULES_CACHE,
      HUGGINGFACE_CO_RESOLVE_ENDPOINT,
      ONNX_EXTERNAL_WEIGHTS_NAME,
      ONNX_WEIGHTS_NAME,
      SAFETENSORS_WEIGHTS_NAME,
      TEXT_ENCODER_ATTN_MODULE,
      WEIGHTS_NAME,
  )
  from .deprecation_utils import deprecate
  from .doc_utils import replace_example_docstring
  from .dynamic_modules_utils import get_class_from_dynamic_module
  from .hub_utils import (
      HF_HUB_OFFLINE,
      _add_variant,
      _get_model_file,
      extract_commit_hash,
      http_user_agent,
  )
  from .import_utils import (
      BACKENDS_MAPPING,
      ENV_VARS_TRUE_AND_AUTO_VALUES,
      ENV_VARS_TRUE_VALUES,
      USE_JAX,
      USE_TF,
      USE_TORCH,
      DummyObject,
      OptionalDependencyNotAvailable,
      is_accelerate_available,
      is_accelerate_version,
      is_bsa_available,
      is_flax_available,
      is_ftfy_available,
      is_inflect_available,
      is_invisible_watermark_available,
      is_k_diffusion_available,
      is_k_diffusion_version,
      is_librosa_available,
      is_note_seq_available,
      is_omegaconf_available,
      is_onnx_available,
      is_safetensors_available,
      is_scipy_available,
      is_tensorboard_available,
      is_tf_available,
      is_torch_available,
      is_torch_version,
      is_torchsde_available,
      is_transformers_available,
      is_transformers_version,
      is_unidecode_available,
      is_wandb_available,
      is_xformers_available,
      requires_backends,
  )
  from .logging import get_logger
  from .outputs import BaseOutput
  from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
  from .torch_utils import is_compiled_module, randn_tensor
  
  
  if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph
  
  from .testing_utils import export_to_gif, export_to_video
  
  
  __A  							=  get_logger(__name__)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[str]		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if version.parse(A_		) < version.parse(A_		):
                if "dev" in min_version:
                       __UpperCAmelCase    							=(
                           """This example requires a source install from HuggingFace diffusers (see """
                           """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
                       )
                else:
                       __UpperCAmelCase    							=F'''This example requires a minimum version of {min_version},'''
                error_message += F''' but the version found is {__version__}.\n'''
                raise ImportError(A_		)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import divide, inta, multiply, ravel, sort, zeros_like
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=int(mask / 2		)
         # copy image size
         __UpperCAmelCase    							=zeros_like(A_		)
         for i in range(A_     ,			gray_img.shape[0] - bd		):
                for j in range(A_     ,			gray_img.shape[1] - bd		):
                       # get mask according with mask
                       __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                       # calculate mask median
                       __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                       __UpperCAmelCase    							=median
         return median_img
  
  
  if __name__ == "__main__":
     # read original image
     __A  							=  imread("../image_data/lena.jpg")
     # turn image in gray scale value
     __A  							=  cvtColor(img, COLOR_BGR2GRAY)
  
     # get values with two different mask size
     __A  							=  median_filter(gray, 3)
     __A  							=  median_filter(gray, 5)
  
     # show result images
     imshow("median filter with 3x3 mask", medianaxa)
     imshow("median filter with 5x5 mask", medianaxa)
     waitKey(0)
  
  
  
  
  