




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	List[str]      ):
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    UNetaDModel(
           block_out_channels=(32, 64)					, layers_per_block=2					, sample_size=32					, in_channels=3					, out_channels=3					, down_block_types=("DownBlock2D", "AttnDownBlock2D")					, up_block_types=("AttnUpBlock2D", "UpBlock2D")					, )
       return model






     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.dummy_uncond_unet
       SCREAMING_SNAKE_CASE       :      Tuple							=    KarrasVeScheduler()

       SCREAMING_SNAKE_CASE       :      List[Any]							=    KarrasVePipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       pipe.to(UpperCAmelCase_      )
       pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    pipe(num_inference_steps=2					, generator=UpperCAmelCase_					, output_type="numpy"      ).images

       SCREAMING_SNAKE_CASE       :      Tuple							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Dict							=    pipe(num_inference_steps=2					, generator=UpperCAmelCase_					, output_type="numpy"					, return_dict=UpperCAmelCase_      )[0]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE       :      Dict							=    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2





@slow
@require_torch
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      int							=    "google/ncsnpp-celebahq-256"
       SCREAMING_SNAKE_CASE       :      Tuple							=    UNetaDModel.from_pretrained(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    KarrasVeScheduler()

       SCREAMING_SNAKE_CASE       :      Any							=    KarrasVePipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       pipe.to(UpperCAmelCase_      )
       pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Dict							=    pipe(num_inference_steps=20					, generator=UpperCAmelCase_					, output_type="numpy"      ).images

       SCREAMING_SNAKE_CASE       :      List[str]							=    image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2














from __future__ import annotations

from cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






