






"""simple docstring"""





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class      a						:






    @property
    def    UpperCamelCase       (       self     :							Dict					)     ->		Any:
      return self.get_dummy_input()






    @property
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      if self.block_type == "down":
        return (4, 32, 16, 16)
      elif self.block_type == "mid":
        return (4, 32, 32, 32)
      elif self.block_type == "up":
        return (4, 32, 64, 64)

      raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''					)






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=False       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=False       ,						__SCREAMING_SNAKE_CASE     :							str=False       ,						)     ->		Tuple:
      lowerCamelCase_         =						4
      lowerCamelCase_         =						32
      lowerCamelCase_         =						(32, 32)

      lowerCamelCase_         =						torch.manual_seed(0					)
      lowerCamelCase_         =						torch.device(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						(batch_size, num_channels) + sizes
      lowerCamelCase_         =						randn_tensor(__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						{'hidden_states': hidden_states}

      if include_temb:
        lowerCamelCase_         =						128
        lowerCamelCase_         =						randn_tensor((batch_size, temb_channels)       ,						generator=__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE					)

      if include_res_hidden_states_tuple:
        lowerCamelCase_         =						torch.manual_seed(1					)
        lowerCamelCase_         =						(randn_tensor(__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE					),)

      if include_encoder_hidden_states:
        lowerCamelCase_         =						floats_tensor((batch_size, 32, 32)					).to(__SCREAMING_SNAKE_CASE					)

      if include_skip_sample:
        lowerCamelCase_         =						randn_tensor(((batch_size, 3) + sizes)       ,						generator=__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE					)

      return dummy_input






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Dict:
      lowerCamelCase_         =						{
          'in_channels': 32,
          'out_channels': 32,
          'temb_channels': 128,
      }
      if self.block_type == "up":
        lowerCamelCase_         =						32

      if self.block_type == "mid":
        init_dict.pop('out_channels'					)

      lowerCamelCase_         =						self.dummy_input
      return init_dict, inputs_dict






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		int:
      lowerCamelCase_     ,	lowerCamelCase_         =						self.prepare_init_args_and_inputs_for_common()
      lowerCamelCase_         =						self.block_class(**__SCREAMING_SNAKE_CASE					)
      unet_block.to(__SCREAMING_SNAKE_CASE					)
      unet_block.eval()

      with torch.no_grad():
        lowerCamelCase_         =						unet_block(**__SCREAMING_SNAKE_CASE					)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						output[0]

      self.assertEqual(output.shape       ,						self.output_shape					)

      lowerCamelCase_         =						output[0, -1, -3:, -3:]
      lowerCamelCase_         =						torch.tensor(__SCREAMING_SNAKE_CASE					).to(__SCREAMING_SNAKE_CASE					)
      assert torch_all_close(output_slice.flatten()       ,						__SCREAMING_SNAKE_CASE       ,						atol=5e-3					)






    @unittest.skipIf(torch_device == 'mps'       ,						'Training is not supported in mps'					)
    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[Any]:
      lowerCamelCase_     ,	lowerCamelCase_         =						self.prepare_init_args_and_inputs_for_common()
      lowerCamelCase_         =						self.block_class(**__SCREAMING_SNAKE_CASE					)
      model.to(__SCREAMING_SNAKE_CASE					)
      model.train()
      lowerCamelCase_         =						model(**__SCREAMING_SNAKE_CASE					)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						output[0]

      lowerCamelCase_         =						torch.device(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						randn_tensor(output.shape       ,						device=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						torch.nn.functional.mse_loss(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      loss.backward()





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


