

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	CONFIG_NAME




 @register_to_config
 def __init__(			self  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = 8  ,  _UpperCAmelCase = "lanczos"  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = False  ,  ):
   super().__init__()




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   if images.ndim == 3:
     __UpperCAmelCase	:  int		=     images[None, ...]
   __UpperCAmelCase	:  List[Any]		=     (images * 255).round().astype('uint8'							)
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCAmelCase	:  List[Any]		=     [Image.fromarray(image.squeeze()  ,  mode='L'							) for image in images]
   else:
     __UpperCAmelCase	:  Union[str, Any]		=     [Image.fromarray(_UpperCAmelCase							) for image in images]

   return pil_images




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   if not isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  Dict		=     [images]
   __UpperCAmelCase	:  int		=     [np.array(_UpperCAmelCase							).astype(np.floataa							) / 2_5_5.0 for image in images]
   __UpperCAmelCase	:  List[Any]		=     np.stack(_UpperCAmelCase  ,  axis=0							)

   return images




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   if images.ndim == 3:
     __UpperCAmelCase	:  Optional[int]		=     images[..., None]

   __UpperCAmelCase	:  Dict		=     torch.from_numpy(images.transpose(0  ,  3  ,  1  ,  2							)							)
   return images




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   __UpperCAmelCase	:  Optional[Any]		=     images.cpu().permute(0  ,  2  ,  3  ,  1							).float().numpy()
   return images




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   return 2.0 * images - 1.0




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   return (images / 2 + 0.5).clamp(0  ,  1							)




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     image.convert('RGB'							)
   return image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  ):
   if height is None:
     __UpperCAmelCase	:  Tuple		=     image.height
   if width is None:
     __UpperCAmelCase	:  str		=     image.width

   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     (
       x - x % self.config.vae_scale_factor for x in (width, height)
   )  # resize to integer multiple of vae_scale_factor
   __UpperCAmelCase	:  Dict		=     image.resize((width, height)  ,  resample=PIL_INTERPOLATION[self.config.resample]							)
   return image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  ):
   __UpperCAmelCase	:  List[Any]		=     (PIL.Image.Image, np.ndarray, torch.Tensor)
   if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  int		=     [image]
   elif not (isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							) and all(isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							) for i in image							)):
     raise ValueError(
         F"Input is in incorrect format: {[type(_UpperCAmelCase							) for i in image]}. Currently, we only support {', '.join(_UpperCAmelCase							)}"							)

   if isinstance(image[0]  ,  PIL.Image.Image							):
     if self.config.do_convert_rgb:
       __UpperCAmelCase	:  Dict		=     [self.convert_to_rgb(_UpperCAmelCase							) for i in image]
     if self.config.do_resize:
       __UpperCAmelCase	:  List[Any]		=     [self.resize(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							) for i in image]
     __UpperCAmelCase	:  List[str]		=     self.pil_to_numpy(_UpperCAmelCase							)  # to np
     __UpperCAmelCase	:  List[Any]		=     self.numpy_to_pt(_UpperCAmelCase							)  # to pt

   elif isinstance(image[0]  ,  np.ndarray							):
     __UpperCAmelCase	:  Dict		=     np.concatenate(_UpperCAmelCase  ,  axis=0							) if image[0].ndim == 4 else np.stack(_UpperCAmelCase  ,  axis=0							)
     __UpperCAmelCase	:  Optional[int]		=     self.numpy_to_pt(_UpperCAmelCase							)
     __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[int]		=     image.shape
     if self.config.do_resize and (
         height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
     ):
       raise ValueError(
           F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
           F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"							)

   elif isinstance(image[0]  ,  torch.Tensor							):
     __UpperCAmelCase	:  Any		=     torch.cat(_UpperCAmelCase  ,  axis=0							) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase  ,  axis=0							)
     __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  str		=     image.shape

     # don't need any preprocess if the image is latents
     if channel == 4:
       return image

     if self.config.do_resize and (
         height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
     ):
       raise ValueError(
           F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
           F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"							)

        # expected range [0,1], normalize to [-1,1]
   __UpperCAmelCase	:  str		=     self.config.do_normalize
   if image.min() < 0:
     warnings.warn(
         'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
         F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"  ,  _UpperCAmelCase  ,  )
     __UpperCAmelCase	:  List[Any]		=     False

   if do_normalize:
     __UpperCAmelCase	:  List[Any]		=     self.normalize(_UpperCAmelCase							)

   return image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = "pil"  ,  _UpperCAmelCase = None  ,  ):
   if not isinstance(_UpperCAmelCase  ,  torch.Tensor							):
     raise ValueError(
         F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase							)}. We only support pytorch tensor"							)
   if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCAmelCase	:  Optional[int]		=     (
         F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
         '`pil`, `np`, `pt`, `latent`'
     )
     deprecate('Unsupported output_type'  ,  '1.0.0'  ,  _UpperCAmelCase  ,  standard_warn=_UpperCAmelCase							)
     __UpperCAmelCase	:  Dict		=     'np'

   if output_type == "latent":
     return image

   if do_denormalize is None:
     __UpperCAmelCase	:  Optional[Any]		=     [self.config.do_normalize] * image.shape[0]

   __UpperCAmelCase	:  str		=     torch.stack(
       [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

   if output_type == "pt":
     return image

   __UpperCAmelCase	:  Tuple		=     self.pt_to_numpy(_UpperCAmelCase							)

   if output_type == "np":
     return image

   if output_type == "pil":
     return self.numpy_to_pil(_UpperCAmelCase							)



class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	CONFIG_NAME




 @register_to_config
 def __init__(			self  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = 8  ,  _UpperCAmelCase = "lanczos"  ,  _UpperCAmelCase = True  ,  ):
   super().__init__()




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   if images.ndim == 3:
     __UpperCAmelCase	:  Optional[Any]		=     images[None, ...]
   __UpperCAmelCase	:  Union[str, Any]		=     (images * 255).round().astype('uint8'							)
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     __UpperCAmelCase	:  Any		=     [Image.fromarray(image.squeeze()  ,  mode='L'							) for image in images]
   else:
     __UpperCAmelCase	:  Tuple		=     [Image.fromarray(image[:, :, :3]							) for image in images]

   return pil_images




 @staticmethod
 def     a       (			_UpperCAmelCase							):
   return image[:, :, 1] * 2**8 + image[:, :, 2]




 def     a       (			self  ,  _UpperCAmelCase							):
   if images.ndim == 3:
     __UpperCAmelCase	:  Union[str, Any]		=     images[None, ...]
   __UpperCAmelCase	:  int		=     images[:, :, :, 3:]
   if images.shape[-1] == 6:
     __UpperCAmelCase	:  Optional[Any]		=     (images_depth * 255).round().astype('uint8'							)
     __UpperCAmelCase	:  Tuple		=     [
         Image.fromarray(self.rgblike_to_depthmap(_UpperCAmelCase							)  ,  mode='I;16'							) for image_depth in images_depth
     ]
   elif images.shape[-1] == 4:
     __UpperCAmelCase	:  List[Any]		=     (images_depth * 6_5_5_3_5.0).astype(np.uintaa							)
     __UpperCAmelCase	:  Optional[Any]		=     [Image.fromarray(_UpperCAmelCase  ,  mode='I;16'							) for image_depth in images_depth]
   else:
     raise Exception('Not supported'							)

   return pil_images




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = "pil"  ,  _UpperCAmelCase = None  ,  ):
   if not isinstance(_UpperCAmelCase  ,  torch.Tensor							):
     raise ValueError(
         F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase							)}. We only support pytorch tensor"							)
   if output_type not in ["latent", "pt", "np", "pil"]:
     __UpperCAmelCase	:  List[str]		=     (
         F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
         '`pil`, `np`, `pt`, `latent`'
     )
     deprecate('Unsupported output_type'  ,  '1.0.0'  ,  _UpperCAmelCase  ,  standard_warn=_UpperCAmelCase							)
     __UpperCAmelCase	:  List[str]		=     'np'

   if do_denormalize is None:
     __UpperCAmelCase	:  List[Any]		=     [self.config.do_normalize] * image.shape[0]

   __UpperCAmelCase	:  List[str]		=     torch.stack(
       [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

   __UpperCAmelCase	:  Any		=     self.pt_to_numpy(_UpperCAmelCase							)

   if output_type == "np":
     if image.shape[-1] == 6:
       __UpperCAmelCase	:  Dict		=     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]  ,  axis=0							)
     else:
       __UpperCAmelCase	:  int		=     image[:, :, :, 3:]
     return image[:, :, :, :3], image_depth

   if output_type == "pil":
     return self.numpy_to_pil(_UpperCAmelCase							), self.numpy_to_depth(_UpperCAmelCase							)
   else:
     raise Exception(F"This type {output_type} is not supported"							)








"""simple docstring"""



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE_       :						int					=    imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_       :						str					=    cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    median_filter(gray, 3)
 SCREAMING_SNAKE_CASE_       :						str					=    median_filter(gray, 5)

 # show result images
 imshow('median filter with 3x3 mask', medianaxa)
 imshow('median filter with 5x5 mask', medianaxa)
 waitKey(0)






