




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 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    @property
    def   a (			self	:	Any  ) ->			List[Any]:
         torch.manual_seed(0  )
         lowerCAmelCase__            =					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	:	Dict  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.dummy_uncond_unet
         lowerCAmelCase__            =					ScoreSdeVeScheduler()

         lowerCAmelCase__            =					ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__  ,      scheduler=SCREAMING_SNAKE_CASE__  )
         sde_ve.to(SCREAMING_SNAKE_CASE__  )
         sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=2  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ).images

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=2  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ,      return_dict=SCREAMING_SNAKE_CASE__  )[
             0
         ]

         lowerCAmelCase__            =					image[0, -3:, -3:, -1]
         lowerCAmelCase__            =					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase__            =					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 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def   a (			self	:	int  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					"google/ncsnpp-church-256"
         lowerCAmelCase__            =					UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE__  ,      scheduler=SCREAMING_SNAKE_CASE__  )
         sde_ve.to(SCREAMING_SNAKE_CASE__  )
         sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.manual_seed(0  )
         lowerCAmelCase__            =					sde_ve(num_inference_steps=10  ,      output_type="numpy"  ,      generator=SCREAMING_SNAKE_CASE__  ).images

         lowerCAmelCase__            =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)

         lowerCAmelCase__            =					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




import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


