
"""simple docstring"""





import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = AutoencoderKL
		lowercase__         = 'sample'
		lowercase__         = 1E-2



		@property
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    4
							_UpperCamelCase									=    3
							_UpperCamelCase									=    (32, 32)

							_UpperCamelCase									=    floats_tensor((batch_size, num_channels) + sizes).to(__a)

							return {"sample": image}



		@property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (3, 32, 32)



		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return (3, 32, 32)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''block_out_channels''': [32, 64],
							    '''in_channels''': 3,
							    '''out_channels''': 3,
							    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
							    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
							    '''latent_channels''': 4,
							}
							_UpperCamelCase									=    self.dummy_input
							return init_dict, inputs_dict



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							pass



		@unittest.skipIf(torch_device == '''mps'''							,						'''Gradient checkpointing skipped on MPS''')
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							# enable deterministic behavior for gradient checkpointing
							_UpperCamelCase					,    _UpperCamelCase									=    self.prepare_init_args_and_inputs_for_common()
							_UpperCamelCase									=    self.model_class(**__a)
							model.to(__a)

							assert not model.is_gradient_checkpointing and model.training

							_UpperCamelCase									=    model(**__a).sample
							# run the backwards pass on the model. For backwards pass, for simplicity purpose,
							# we won't calculate the loss and rather backprop on out.sum()
							model.zero_grad()

							_UpperCamelCase									=    torch.randn_like(__a)
							_UpperCamelCase									=    (out - labels).mean()
							loss.backward()

							# re-instantiate the model now enabling gradient checkpointing
							_UpperCamelCase									=    self.model_class(**__a)
							# clone model
							model_a.load_state_dict(model.state_dict())
							model_a.to(__a)
							model_a.enable_gradient_checkpointing()

							assert model_a.is_gradient_checkpointing and model_a.training

							_UpperCamelCase									=    model_a(**__a).sample
							# run the backwards pass on the model. For backwards pass, for simplicity purpose,
							# we won't calculate the loss and rather backprop on out.sum()
							model_a.zero_grad()
							_UpperCamelCase									=    (out_a - labels).mean()
							loss_a.backward()

							# compare the output and parameters gradients
							self.assertTrue((loss - loss_a).abs() < 1e-5)
							_UpperCamelCase									=    dict(model.named_parameters())
							_UpperCamelCase									=    dict(model_a.named_parameters())
							for name, param in named_params.items():
												self.assertTrue(torch_all_close(param.grad.data							,						named_params_a[name].grad.data							,						atol=5e-5))



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							,						output_loading_info=__a)
							self.assertIsNotNone(__a)
							self.assertEqual(len(loading_info['''missing_keys'''])							,						0)

							model.to(__a)
							_UpperCamelCase									=    model(**self.dummy_input)

							assert image is not None, "Make sure output is not None"




		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''')
							_UpperCamelCase									=    model.to(__a)
							model.eval()

							if torch_device == "mps":
												_UpperCamelCase									=    torch.manual_seed(0)
							else:
												_UpperCamelCase									=    torch.Generator(device=__a).manual_seed(0)

							_UpperCamelCase									=    torch.randn(
							    1							,						model.config.in_channels							,						model.config.sample_size							,						model.config.sample_size							,						generator=torch.manual_seed(0)							,						)
							_UpperCamelCase									=    image.to(__a)
							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						sample_posterior=__a							,						generator=__a).sample

							_UpperCamelCase									=    output[0, -1, -3:, -3:].flatten().cpu()

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							if torch_device == "mps":
												_UpperCamelCase									=    torch.tensor(
												    [
												        -4.0_078e-01,
												        -3.8_323e-04,
												        -1.2_681e-01,
												        -1.1_462e-01,
												        2.0_095e-01,
												        1.0_893e-01,
												        -8.8_247e-02,
												        -3.0_361e-01,
												        -9.8_644e-03,
												    ])
							elif torch_device == "cpu":
												_UpperCamelCase									=    torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026])
							else:
												_UpperCamelCase									=    torch.tensor(
												    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485])

							self.assertTrue(torch_all_close(__a							,						__a							,						rtol=1e-2))






@slow
class        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							return F'''gaussian_noise_s={seed}_shape={"_".join([str(__a) for s in shape])}.npy'''



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 		UpperCAmelCase				(   self							,						__a=0							,						__a=(4, 3, 5_12, 5_12)							,						__a=False)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    torch.floataa if fpaa else torch.floataa
							_UpperCamelCase									=    torch.from_numpy(load_hf_numpy(self.get_file_format(__a							,						__a))).to(__a).to(__a)
							return image



		def 		UpperCAmelCase				(   self							,						__a="CompVis/stable-diffusion-v1-4"							,						__a=False)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    '''fp16''' if fpaa else None
							_UpperCamelCase									=    torch.floataa if fpaa else torch.floataa

							_UpperCamelCase									=    AutoencoderKL.from_pretrained(
							    __a							,						subfolder='''vae'''							,						torch_dtype=__a							,						revision=__a							,						)
							model.to(__a).eval()

							return model



		def 		UpperCAmelCase				(   self							,						__a=0)			->			Optional[Any]:






							'''simple docstring'''
							if torch_device == "mps":
												return torch.manual_seed(__a)
							return torch.Generator(device=__a).manual_seed(__a)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						generator=__a							,						sample_posterior=__a).sample

							assert sample.shape == image.shape

							_UpperCamelCase									=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
							_UpperCamelCase									=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice)

							assert torch_all_close(__a							,						__a							,						atol=3e-3)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
		        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						fpaa=__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						generator=__a							,						sample_posterior=__a).sample

							assert sample.shape == image.shape

							_UpperCamelCase									=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
							_UpperCamelCase									=    torch.tensor(__a)

							assert torch_all_close(__a							,						__a							,						atol=1e-2)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a).sample

							assert sample.shape == image.shape

							_UpperCamelCase									=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
							_UpperCamelCase									=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice)

							assert torch_all_close(__a							,						__a							,						atol=3e-3)



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
		        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64))

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							assert list(sample.shape) == [3, 3, 5_12, 5_12]

							_UpperCamelCase									=    sample[-1, -2:, :2, -2:].flatten().cpu()
							_UpperCamelCase									=    torch.tensor(__a)

							assert torch_all_close(__a							,						__a							,						atol=1e-3)



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
		        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64)							,						fpaa=__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							assert list(sample.shape) == [3, 3, 5_12, 5_12]

							_UpperCamelCase									=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
							_UpperCamelCase									=    torch.tensor(__a)

							assert torch_all_close(__a							,						__a							,						atol=5e-3)



		@parameterized.expand([(13,), (16,), (27,)])
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()							,						reason='''xformers is not required when using PyTorch 2.0.''')
		def 		UpperCAmelCase				(   self							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64)							,						fpaa=__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							assert list(sample.shape) == [3, 3, 5_12, 5_12]

							assert torch_all_close(__a							,						__a							,						atol=1e-1)



		@parameterized.expand([(13,), (16,), (37,)])
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()							,						reason='''xformers is not required when using PyTorch 2.0.''')
		def 		UpperCAmelCase				(   self							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64))

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							assert list(sample.shape) == [3, 3, 5_12, 5_12]

							assert torch_all_close(__a							,						__a							,						atol=1e-2)




		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
		        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.encode(__a).latent_dist
												_UpperCamelCase									=    dist.sample(generator=__a)

							assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

							_UpperCamelCase									=    sample[0, -1, -3:, -3:].flatten().cpu()
							_UpperCamelCase									=    torch.tensor(__a)

							_UpperCamelCase									=    3e-3 if torch_device != '''mps''' else 1e-2
							assert torch_all_close(__a							,						__a							,						atol=__a)








"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






