


'''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 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= AutoencoderKL
							lowercase__     :  Any      		= "sample"
							lowercase__     :  Optional[int]      		= 1e-2







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      4
														lowerCAmelCase__			  =      3
														lowerCAmelCase__			  =      (32, 32)

														lowerCAmelCase__			  =      floats_tensor((batch_size, num_channels) + sizes  ).to(lowerCamelCase_  )

														return {"sample": image}







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return (3, 32, 32)







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return (3, 32, 32)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      {
														    '''block_out_channels''': [32, 64],
														    '''in_channels''': 3,
														    '''out_channels''': 3,
														    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
														    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
														    '''latent_channels''': 4,
														}
														lowerCAmelCase__			  =      self.dummy_input
														return init_dict, inputs_dict







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														pass







							@unittest.skipIf(torch_device == '''mps'''   ,       '''Gradient checkpointing skipped on MPS'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# enable deterministic behavior for gradient checkpointing
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.prepare_init_args_and_inputs_for_common()
														lowerCAmelCase__			  =      self.model_class(**lowerCamelCase_  )
														model.to(lowerCamelCase_  )

														assert not model.is_gradient_checkpointing and model.training

														lowerCAmelCase__			  =      model(**lowerCamelCase_  ).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()

														lowerCAmelCase__			  =      torch.randn_like(lowerCamelCase_  )
														lowerCAmelCase__			  =      (out - labels).mean()
														loss.backward()

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

														assert model_a.is_gradient_checkpointing and model_a.training

														lowerCAmelCase__			  =      model_a(**lowerCamelCase_  ).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()
														lowerCAmelCase__			  =      (out_a - labels).mean()
														loss_a.backward()

														# compare the output and parameters gradients
														self.assertTrue((loss - loss_a).abs() < 1e-5  )
														lowerCAmelCase__			  =      dict(model.named_parameters()  )
														lowerCAmelCase__			  =      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     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''   ,       output_loading_info=lowerCamelCase_  )
														self.assertIsNotNone(lowerCamelCase_  )
														self.assertEqual(len(loading_info['''missing_keys''']  )   ,       0  )

														model.to(lowerCamelCase_  )
														lowerCAmelCase__			  =      model(**self.dummy_input  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''  )
														lowerCAmelCase__			  =      model.to(lowerCamelCase_  )
														model.eval()

														if torch_device == "mps":
																					lowerCAmelCase__			  =      torch.manual_seed(0  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(0  )

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

														lowerCAmelCase__			  =      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":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        -4.00_78e-01,
																					        -3.83_23e-04,
																					        -1.26_81e-01,
																					        -1.14_62e-01,
																					        2.00_95e-01,
																					        1.08_93e-01,
																					        -8.82_47e-02,
																					        -3.03_61e-01,
																					        -9.86_44e-03,
																					    ]  )
														elif torch_device == "cpu":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]  )
														else:
																					lowerCAmelCase__			  =      torch.tensor(
																					    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]  )

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




@slow
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Tuple:
														return F"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_  ) for s in shape]  )}.npy"""







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=(4, 3, 5_12, 5_12)   ,       lowerCamelCase_=False  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.floataa if fpaa else torch.floataa
														lowerCAmelCase__			  =      torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_   ,       lowerCamelCase_  )  )  ).to(lowerCamelCase_  ).to(lowerCamelCase_  )
														return image







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_="CompVis/stable-diffusion-v1-4"   ,       lowerCamelCase_=False  )			->				List[Any]:
														lowerCAmelCase__			  =      '''fp16''' if fpaa else None
														lowerCAmelCase__			  =      torch.floataa if fpaa else torch.floataa

														lowerCAmelCase__			  =      AutoencoderKL.from_pretrained(
														    lowerCamelCase_   ,       subfolder='''vae'''   ,       torch_dtype=lowerCamelCase_   ,       revision=lowerCamelCase_   ,       )
														model.to(lowerCamelCase_  ).eval()

														return model







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=0  )			->				Optional[int]:
														if torch_device == "mps":
																					return torch.manual_seed(lowerCamelCase_  )
														return torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]  )
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_sd_vae_model()
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_generator(lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       sample_posterior=lowerCamelCase_  ).sample

														assert sample.shape == image.shape

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

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







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
							        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
							        # fmt: on
							    ]  )
							@require_torch_gpu
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      self.get_sd_vae_model(fpaa=lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_   ,       fpaa=lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_generator(lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       sample_posterior=lowerCamelCase_  ).sample

														assert sample.shape == image.shape

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

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







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]  )
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_sd_vae_model()
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model(lowerCamelCase_  ).sample

														assert sample.shape == image.shape

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

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







							@parameterized.expand(
							    [
							        # fmt: off
							        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
							        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
							        # fmt: on
							    ]  )
							@require_torch_gpu
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_sd_vae_model()
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_   ,       shape=(3, 4, 64, 64)  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

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

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

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







							@parameterized.expand(
							    [
							        # fmt: off
							        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
							        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
							        # fmt: on
							    ]  )
							@require_torch_gpu
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_sd_vae_model(fpaa=lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_   ,       shape=(3, 4, 64, 64)   ,       fpaa=lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

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

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

														assert torch_all_close(lowerCamelCase_   ,       lowerCamelCase_   ,       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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__			  =      self.get_sd_vae_model(fpaa=lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_   ,       shape=(3, 4, 64, 64)   ,       fpaa=lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

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

														assert torch_all_close(lowerCamelCase_   ,       lowerCamelCase_   ,       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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.get_sd_vae_model()
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_   ,       shape=(3, 4, 64, 64)  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					lowerCAmelCase__			  =      model.decode(lowerCamelCase_  ).sample

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

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







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
							        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
							        # fmt: on
							    ]  )
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Tuple:
														lowerCAmelCase__			  =      self.get_sd_vae_model()
														lowerCAmelCase__			  =      self.get_sd_image(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.get_generator(lowerCamelCase_  )

														with torch.no_grad():
																					lowerCAmelCase__			  =      model.encode(lowerCamelCase_  ).latent_dist
																					lowerCAmelCase__			  =      dist.sample(generator=lowerCamelCase_  )

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

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

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







class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


