


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    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				@property
				def   _UpperCAmelCase       (    self )						->					str:
					torch.manual_seed(0 )
					A             =					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   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					self.dummy_uncond_unet
					A             =					ScoreSdeVeScheduler()

					A             =					ScoreSdeVePipeline(unet=a__					, scheduler=a__ )
					sde_ve.to(a__ )
					sde_ve.set_progress_bar_config(disable=a__ )

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

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

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

					assert image.shape == (1, 32, 32, 3)
					A             =					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    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					int:
					A             =					"""google/ncsnpp-church-256"""
					A             =					UNetaDModel.from_pretrained(a__ )

					A             =					ScoreSdeVeScheduler.from_pretrained(a__ )

					A             =					ScoreSdeVePipeline(unet=a__					, scheduler=a__ )
					sde_ve.to(a__ )
					sde_ve.set_progress_bar_config(disable=a__ )

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

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

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

					A             =					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







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



