
"""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 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   AutoencoderKL
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   'sample'
					_SCREAMING_SNAKE_CASE       :					Dict     							=   1E-2





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       4
												__snake_case     			=       3
												__snake_case     			=       (32, 32)

												__snake_case     			=       floats_tensor((batch_size, num_channels) + sizes    ).to(snake_case_    )

												return {"sample": image}





					@property
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												return (3, 32, 32)





					@property
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												return (3, 32, 32)





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {
												    "block_out_channels": [32, 64],
												    "in_channels": 3,
												    "out_channels": 3,
												    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
												    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
												    "latent_channels": 4,
												}
												__snake_case     			=       self.dummy_input
												return init_dict, inputs_dict





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												pass





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												pass





					@unittest.skipIf(torch_device == "mps"							,						"Gradient checkpointing skipped on MPS"    )
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												# enable deterministic behavior for gradient checkpointing
												__snake_case			,   __snake_case     			=       self.prepare_init_args_and_inputs_for_common()
												__snake_case     			=       self.model_class(**snake_case_    )
												model.to(snake_case_    )

												assert not model.is_gradient_checkpointing and model.training

												__snake_case     			=       model(**snake_case_    ).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()

												__snake_case     			=       torch.randn_like(snake_case_    )
												__snake_case     			=       (out - labels).mean()
												loss.backward()

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

												assert model_a.is_gradient_checkpointing and model_a.training

												__snake_case     			=       model_a(**snake_case_    ).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()
												__snake_case     			=       (out_a - labels).mean()
												loss_a.backward()

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

												model.to(snake_case_    )
												__snake_case     			=       model(**self.dummy_input    )

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





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"    )
												__snake_case     			=       model.to(snake_case_    )
												model.eval()

												if torch_device == "mps":
																			__snake_case     			=       torch.manual_seed(0    )
												else:
																			__snake_case     			=       torch.Generator(device=snake_case_    ).manual_seed(0    )

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

												__snake_case     			=       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":
																			__snake_case     			=       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":
																			__snake_case     			=       torch.tensor(
																			    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]    )
												else:
																			__snake_case     			=       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(snake_case_							,						snake_case_							,						rtol=1e-2    )    )







@slow
class 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							Any    ):
												return F'''gaussian_noise_s={seed}_shape={"_".join([str(snake_case_    ) for s in shape]    )}.npy'''





					def 							lowerCAmelCase      (				self   :							Any    ):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[Any]=0							,						snake_case_   :							List[str]=(4, 3, 512, 512)							,						snake_case_   :							Tuple=False    ):
												__snake_case     			=       torch.floataa if fpaa else torch.floataa
												__snake_case     			=       torch.from_numpy(load_hf_numpy(self.get_file_format(snake_case_							,						snake_case_    )    )    ).to(snake_case_    ).to(snake_case_    )
												return image





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							str="CompVis/stable-diffusion-v1-4"							,						snake_case_   :							str=False    ):
												__snake_case     			=       "fp16" if fpaa else None
												__snake_case     			=       torch.floataa if fpaa else torch.floataa

												__snake_case     			=       AutoencoderKL.from_pretrained(
												    snake_case_							,						subfolder="vae"							,						torch_dtype=snake_case_							,						revision=snake_case_							,						)
												model.to(snake_case_    ).eval()

												return model





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							int=0    ):
												if torch_device == "mps":
																			return torch.manual_seed(snake_case_    )
												return torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )





					@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 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str							,						snake_case_   :							Optional[int]							,						snake_case_   :							List[str]    ):
												__snake_case     			=       self.get_sd_vae_model()
												__snake_case     			=       self.get_sd_image(snake_case_    )
												__snake_case     			=       self.get_generator(snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model(snake_case_							,						generator=snake_case_							,						sample_posterior=snake_case_    ).sample

												assert sample.shape == image.shape

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

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							int    ):
												__snake_case     			=       self.get_sd_vae_model(fpaa=snake_case_    )
												__snake_case     			=       self.get_sd_image(snake_case_							,						fpaa=snake_case_    )
												__snake_case     			=       self.get_generator(snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model(snake_case_							,						generator=snake_case_							,						sample_posterior=snake_case_    ).sample

												assert sample.shape == image.shape

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

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Tuple    ):
												__snake_case     			=       self.get_sd_vae_model()
												__snake_case     			=       self.get_sd_image(snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model(snake_case_    ).sample

												assert sample.shape == image.shape

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

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       self.get_sd_vae_model()
												__snake_case     			=       self.get_sd_image(snake_case_							,						shape=(3, 4, 64, 64)    )

												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												assert list(sample.shape    ) == [3, 3, 512, 512]

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

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       self.get_sd_vae_model(fpaa=snake_case_    )
												__snake_case     			=       self.get_sd_image(snake_case_							,						shape=(3, 4, 64, 64)							,						fpaa=snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												assert list(sample.shape    ) == [3, 3, 512, 512]

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

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple    ):
												__snake_case     			=       self.get_sd_vae_model(fpaa=snake_case_    )
												__snake_case     			=       self.get_sd_image(snake_case_							,						shape=(3, 4, 64, 64)							,						fpaa=snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												model.enable_xformers_memory_efficient_attention()
												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												assert list(sample.shape    ) == [3, 3, 512, 512]

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.get_sd_vae_model()
												__snake_case     			=       self.get_sd_image(snake_case_							,						shape=(3, 4, 64, 64)    )

												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												model.enable_xformers_memory_efficient_attention()
												with torch.no_grad():
																			__snake_case     			=       model.decode(snake_case_    ).sample

												assert list(sample.shape    ) == [3, 3, 512, 512]

												assert torch_all_close(snake_case_							,						snake_case_							,						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 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       self.get_sd_vae_model()
												__snake_case     			=       self.get_sd_image(snake_case_    )
												__snake_case     			=       self.get_generator(snake_case_    )

												with torch.no_grad():
																			__snake_case     			=       model.encode(snake_case_    ).latent_dist
																			__snake_case     			=       dist.sample(generator=snake_case_    )

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

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

												__snake_case     			=       3e-3 if torch_device != "mps" else 1e-2
												assert torch_all_close(snake_case_							,						snake_case_							,						atol=snake_case_    )


"""simple docstring"""







import baseaa
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bytes:







							"""simple docstring"""


							return baseaa.baaencode(string.encode("utf-8" ) )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							return baseaa.baadecode(SCREAMING_SNAKE_CASE ).decode("utf-8" )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      """Hello World!"""
		_SCREAMING_SNAKE_CASE	       =      baseaa_encode(test)
		print(encoded)

		_SCREAMING_SNAKE_CASE	       =      baseaa_decode(encoded)
		print(decoded)
