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      lowerCamelCase    :
  """simple docstring"""







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







  @property
  def       __A   (							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       __A   (							self       :				Dict     ,   __magic_name__       :				str=True     ,   __magic_name__       :				Union[str, Any]=False     ,   __magic_name__       :				Union[str, Any]=False     ,   __magic_name__       :				str=False     ,   )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     4
        SCREAMING_SNAKE_CASE_      =     32
        SCREAMING_SNAKE_CASE_      =     (32, 32)

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     torch.device(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     (batch_size, num_channels) + sizes
        SCREAMING_SNAKE_CASE_      =     randn_tensor(__magic_name__     ,   generator=__magic_name__     ,   device=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {"hidden_states": hidden_states}

        if include_temb:
              SCREAMING_SNAKE_CASE_      =     128
              SCREAMING_SNAKE_CASE_      =     randn_tensor((batch_size, temb_channels)     ,   generator=__magic_name__     ,   device=__magic_name__      )

        if include_res_hidden_states_tuple:
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(1      )
              SCREAMING_SNAKE_CASE_      =     (randn_tensor(__magic_name__     ,   generator=__magic_name__     ,   device=__magic_name__      ),)

        if include_encoder_hidden_states:
              SCREAMING_SNAKE_CASE_      =     floats_tensor((batch_size, 32, 32)      ).to(__magic_name__      )

        if include_skip_sample:
              SCREAMING_SNAKE_CASE_      =     randn_tensor(((batch_size, 3) + sizes)     ,   generator=__magic_name__     ,   device=__magic_name__      )

        return dummy_input







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

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

        SCREAMING_SNAKE_CASE_      =     self.dummy_input
        return init_dict, inputs_dict







  def       __A   (							self       :				Any     ,   __magic_name__       :				List[str]      )     ->		int:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.prepare_init_args_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_      =     self.block_class(**__magic_name__      )
        unet_block.to(__magic_name__      )
        unet_block.eval()

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_      =     unet_block(**__magic_name__      )

        if isinstance(__magic_name__     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     output[0]

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

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







  @unittest.skipIf(torch_device == "mps"     ,   "Training is not supported in mps"      )
  def       __A   (							self       :				List[str]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.prepare_init_args_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_      =     self.block_class(**__magic_name__      )
        model.to(__magic_name__      )
        model.train()
        SCREAMING_SNAKE_CASE_      =     model(**__magic_name__      )

        if isinstance(__magic_name__     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     output[0]

        SCREAMING_SNAKE_CASE_      =     torch.device(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     randn_tensor(output.shape     ,   device=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.nn.functional.mse_loss(__magic_name__     ,   __magic_name__      )
        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()


