



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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     @property
     def 	__snake_case    (       self :							int				)->Tuple:
      torch.manual_seed(0				)
      __SCREAMING_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 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE       :    int       =	ScoreSdeVeScheduler()

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

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

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

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

      assert image.shape == (1, 3_2, 3_2, 3)
      __SCREAMING_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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Dict				)->int:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	'google/ncsnpp-church-256'
      __SCREAMING_SNAKE_CASE       :    Dict       =	UNetaDModel.from_pretrained(__a				)

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

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

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

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

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

      __SCREAMING_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











from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






