


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Union[str, Any]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					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		:						int      )  ->			List[str]:
			__magic_name__	:   Optional[int]					     =					self.dummy_uncond_unet
			__magic_name__	:   Optional[int]					     =					PNDMScheduler()

			__magic_name__	:   Optional[int]					     =					PNDMPipeline(unet=_A  ,					scheduler=_A      )
			pndm.to(_A      )
			pndm.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Optional[int]					     =					torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					pndm(generator=_A  ,					num_inference_steps=20  ,					output_type='numpy'      ).images

			__magic_name__	:   str					     =					torch.manual_seed(0      )
			__magic_name__	:   Tuple					     =					pndm(generator=_A  ,					num_inference_steps=20  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   Dict					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   Tuple					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   Any					     =					np.array([1.0, 1.0, 0.0, 1.0, 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        __lowerCAmelCase		( self		:						Any      )  ->			List[str]:
			__magic_name__	:   Optional[Any]					     =					'google/ddpm-cifar10-32'

			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   Optional[Any]					     =					PNDMScheduler()

			__magic_name__	:   int					     =					PNDMPipeline(unet=_A  ,					scheduler=_A      )
			pndm.to(_A      )
			pndm.set_progress_bar_config(disable=_A      )
			__magic_name__	:   Optional[Any]					     =					torch.manual_seed(0      )
			__magic_name__	:   Any					     =					pndm(generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Tuple					     =					image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   Optional[int]					     =					np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]      )

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



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		lowerCAmelCase		) and isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					len(set_a.intersection(lowerCAmelCase		)		)

		if alternative_union:
			__magic_name__	:   List[str]					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
		else:
			__magic_name__	:   Any					     =					len(set_a.union(lowerCAmelCase		)		)

		return intersection / union

	if isinstance(lowerCAmelCase		,		(list, tuple)		) and isinstance(lowerCAmelCase		,		(list, tuple)		):
		__magic_name__	:   str					     =					[element for element in set_a if element in set_b]

		if alternative_union:
			__magic_name__	:   Dict					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
			return len(lowerCAmelCase		) / union
		else:
			__magic_name__	:   Any					     =					set_a + [element for element in set_b if element not in set_a]
			return len(lowerCAmelCase		) / len(lowerCAmelCase		)

		return len(lowerCAmelCase		) / len(lowerCAmelCase		)
	return None


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      {'''a''', '''b''', '''c''', '''d''', '''e'''}
							lowerCAmelCase				:Tuple        =      {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
							print(jaccard_similarity(set_a, set_b))