






'''simple docstring'''







import pytest

import datasets


# Import fixture modules as plugins
_A	:			Union[str, Any]         =['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
							for item in items:
														if any(marker in item.keywords for marker in ["""integration""", """unit"""]      ):
																					continue
														item.add_marker(pytest.mark.unit      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							config.addinivalue_line("""markers"""				,			"""torchaudio_latest: mark test to run with torchaudio>=0.12"""      )





@pytest.fixture(autouse=UpperCamelCase      )
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
							lowerCamelCase__					:	Dict         =       tmp_path_factory.getbasetemp() / """cache"""
							lowerCamelCase__					:	Optional[Any]         =       test_hf_cache_home / """datasets"""
							lowerCamelCase__					:	List[Any]         =       test_hf_cache_home / """metrics"""
							lowerCamelCase__					:	List[str]         =       test_hf_cache_home / """modules"""
							monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""				,			str(UpperCamelCase      )      )
							monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""				,			str(UpperCamelCase      )      )
							monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""				,			str(UpperCamelCase      )      )
							lowerCamelCase__					:	str         =       test_hf_datasets_cache / """downloads"""
							monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""				,			str(UpperCamelCase      )      )
							lowerCamelCase__					:	Optional[Any]         =       test_hf_datasets_cache / """downloads""" / """extracted"""
							monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""				,			str(UpperCamelCase      )      )





@pytest.fixture(autouse=UpperCamelCase				,			scope="""session"""      )
def  SCREAMING_SNAKE_CASE_       ()      ->       int:
							datasets.disable_progress_bar()





@pytest.fixture(autouse=UpperCamelCase      )
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Union[str, Any]:
							# don't take tests into account when counting downloads
							monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""				,			UpperCamelCase      )





@pytest.fixture
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Optional[int]:
							# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
							# To be removed once SQLAlchemy 2.0 supported
							monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""				,			UpperCamelCase      )


'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					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):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[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([1.0, 1.0, 0.0, 1.0, 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	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

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




