import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @property
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			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 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.dummy_uncond_unet
    _UpperCamelCase:  Optional[Any]    						=			KarrasVeScheduler()

    _UpperCamelCase:  Any    						=			KarrasVePipeline(unet=_lowercase		,	scheduler=_lowercase				)
    pipe.to(_lowercase				)
    pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  List[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Optional[Any]    						=			pipe(num_inference_steps=2		,	generator=_lowercase		,	output_type='''numpy'''				).images

    _UpperCamelCase:  Optional[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Tuple    						=			pipe(num_inference_steps=2		,	generator=_lowercase		,	output_type='''numpy'''		,	return_dict=_lowercase				)[0]

    _UpperCamelCase:  str    						=			image[0, -3:, -3:, -1]
    _UpperCamelCase:  Optional[Any]    						=			image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _UpperCamelCase:  Optional[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 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			'''google/ncsnpp-celebahq-256'''
    _UpperCamelCase:  Tuple    						=			UNetaDModel.from_pretrained(_lowercase				)
    _UpperCamelCase:  List[Any]    						=			KarrasVeScheduler()

    _UpperCamelCase:  str    						=			KarrasVePipeline(unet=_lowercase		,	scheduler=_lowercase				)
    pipe.to(_lowercase				)
    pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Any    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Tuple    						=			pipe(num_inference_steps=20		,	generator=_lowercase		,	output_type='''numpy'''				).images

    _UpperCamelCase:  int    						=			image[0, -3:, -3:, -1]
    assert image.shape == (1, 256, 256, 3)
    _UpperCamelCase:  Tuple    						=			np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]				)

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




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()