




'''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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@property
					def     __snake_case						(	self     :						List[Any])    ->							Union[str, Any]:
								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     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= self.dummy_uncond_unet
								A_									= ScoreSdeVeScheduler()

								A_									= ScoreSdeVePipeline(unet=_lowercase      ,       scheduler=_lowercase)
								sde_ve.to(_lowercase)
								sde_ve.set_progress_bar_config(disable=_lowercase)

								A_									= torch.manual_seed(0)
								A_									= sde_ve(num_inference_steps=2      ,       output_type='numpy'      ,       generator=_lowercase).images

								A_									= torch.manual_seed(0)
								A_									= sde_ve(num_inference_steps=2      ,       output_type='numpy'      ,       generator=_lowercase      ,       return_dict=_lowercase)[
								    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     __snake_case						(	self     :						List[Any])    ->							Optional[int]:
								A_									= 'google/ncsnpp-church-256'
								A_									= UNetaDModel.from_pretrained(_lowercase)

								A_									= ScoreSdeVeScheduler.from_pretrained(_lowercase)

								A_									= ScoreSdeVePipeline(unet=_lowercase      ,       scheduler=_lowercase)
								sde_ve.to(_lowercase)
								sde_ve.set_progress_bar_config(disable=_lowercase)

								A_									= torch.manual_seed(0)
								A_									= sde_ve(num_inference_steps=10      ,       output_type='numpy'      ,       generator=_lowercase).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










'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




