'''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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @property
      def      _A	(       self						:				List[str]       ):
             torch.manual_seed(0       )
             _UpperCAmelCase     :  List[Any]							      =    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						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.dummy_uncond_unet
             _UpperCAmelCase     :  Any							      =    ScoreSdeVeScheduler()

             _UpperCAmelCase     :  str							      =    ScoreSdeVePipeline(unet=A						,					scheduler=A       )
             sde_ve.to(A       )
             sde_ve.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  List[str]							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  List[str]							      =    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A       ).images

             _UpperCAmelCase     :  Optional[int]							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  List[Any]							      =    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A						,					return_dict=A       )[
                 0
             ]

             _UpperCAmelCase     :  Tuple							      =    image[0, -3:, -3:, -1]
             _UpperCAmelCase     :  Optional[Any]							      =    image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             _UpperCAmelCase     :  List[Any]							      =    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       ):
             _UpperCAmelCase     :  int							      =    "google/ncsnpp-church-256"
             _UpperCAmelCase     :  Any							      =    UNetaDModel.from_pretrained(A       )

             _UpperCAmelCase     :  List[Any]							      =    ScoreSdeVeScheduler.from_pretrained(A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    ScoreSdeVePipeline(unet=A						,					scheduler=A       )
             sde_ve.to(A       )
             sde_ve.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  int							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  Tuple							      =    sde_ve(num_inference_steps=10						,					output_type="numpy"						,					generator=A       ).images

             _UpperCAmelCase     :  int							      =    image[0, -3:, -3:, -1]

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

             _UpperCAmelCase     :  int							      =    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"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

