
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        lowerCAmelCase__      (   unittest.TestCase	):

						@property
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.dummy_uncond_unet
								_UpperCamelCase						    =	KarrasVeScheduler()

								_UpperCamelCase						    =	KarrasVePipeline(unet=a       ,							scheduler=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=2       ,							generator=a       ,							output_type="""numpy"""				).images

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=2       ,							generator=a       ,							output_type="""numpy"""       ,							return_dict=a				)[0]

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

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

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	"""google/ncsnpp-celebahq-256"""
								_UpperCamelCase						    =	UNetaDModel.from_pretrained(a				)
								_UpperCamelCase						    =	KarrasVeScheduler()

								_UpperCamelCase						    =	KarrasVePipeline(unet=a       ,							scheduler=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(num_inference_steps=20       ,							generator=a       ,							output_type="""numpy"""				).images

								_UpperCamelCase						    =	image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_56, 2_56, 3)
								_UpperCamelCase						    =	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





def 							__A(lowerCAmelCase )		->				list[list[int]]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		if len(lowerCAmelCase ) == 1:
				return [nums.copy()]
		for _ in range(len(lowerCAmelCase ) ):
				_UpperCamelCase						    =	nums.pop(0 )
				_UpperCamelCase						    =	permute(lowerCAmelCase )
				for perm in permutations:
						perm.append(lowerCAmelCase )
				result.extend(lowerCAmelCase )
				nums.append(lowerCAmelCase )
		return result


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		def backtrack(lowerCAmelCase ):
				if start == len(lowerCAmelCase ) - 1:
						output.append(nums[:] )
				else:
						for i in range(lowerCAmelCase						,  len(lowerCAmelCase ) ):
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]
								backtrack(start + 1 )
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]  # backtrack

		_UpperCamelCase						    =	[]
		backtrack(0 )
		return output


if __name__ == "__main__":
		import doctest

		# use res to print the data in permute2 function
		lowerCamelCase__	=							permutea([1, 2, 3])
		print(res)
		doctest.testmod()



