






'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class a_    (    unittest.TestCase							):







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= UNetaDModel(
          block_out_channels=(3_2, 6_4)					,   layers_per_block=2					,   sample_size=3_2					,   in_channels=3					,   out_channels=3					,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')					,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')					,   )
      return model







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= self.dummy_uncond_unet
      __snake_case				:      int     							= ScoreSdeVeScheduler()

      __snake_case				:      int     							= ScoreSdeVePipeline(unet=__a					,   scheduler=__a)
      sde_ve.to(__a)
      sde_ve.set_progress_bar_config(disable=__a)

      __snake_case				:      Tuple     							= torch.manual_seed(0)
      __snake_case				:      Any     							= sde_ve(num_inference_steps=2					,   output_type='numpy'					,   generator=__a).images

      __snake_case				:      Any     							= torch.manual_seed(0)
      __snake_case				:      Dict     							= sde_ve(num_inference_steps=2					,   output_type='numpy'					,   generator=__a					,   return_dict=__a)[
          0
      ]

      __snake_case				:      str     							= image[0, -3:, -3:, -1]
      __snake_case				:      int     							= image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      __snake_case				:      str     							= 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 a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Tuple     							= 'google/ncsnpp-church-256'
      __snake_case				:      Dict     							= UNetaDModel.from_pretrained(__a)

      __snake_case				:      List[Any]     							= ScoreSdeVeScheduler.from_pretrained(__a)

      __snake_case				:      Tuple     							= ScoreSdeVePipeline(unet=__a					,   scheduler=__a)
      sde_ve.to(__a)
      sde_ve.set_progress_bar_config(disable=__a)

      __snake_case				:      Optional[int]     							= torch.manual_seed(0)
      __snake_case				:      Optional[Any]     							= sde_ve(num_inference_steps=1_0					,   output_type='numpy'					,   generator=__a).images

      __snake_case				:      Optional[int]     							= image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)

      __snake_case				:      Optional[Any]     							= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

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







'''simple docstring'''






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A    	=   abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> str:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Optional[int]:







   """simple docstring"""
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   __snake_case				:      Any     							= terminalreporter.config.getoption('--make-reports'  )
   if make_reports:
      pytest_terminal_summary_main(A  ,		id=A  )