"""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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		AutoencoderKL
					a   	=		"sample"
					a   	=		1E-2



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				3
									SCREAMING_SNAKE_CASE__		  =				(32, 32)

									SCREAMING_SNAKE_CASE__		  =				floats_tensor((batch_size, num_channels) + sizes		).to(_snake_case		)

									return {"sample": image}



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



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									return (3, 32, 32)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_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,
									}
									SCREAMING_SNAKE_CASE__		  =				self.dummy_input
									return init_dict, inputs_dict



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[Any]:
									pass



					def 			lowerCAmelCase_					(						self			:				int		)							->       Union[str, Any]:
									pass



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

									assert not model.is_gradient_checkpointing and model.training

									SCREAMING_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()

									SCREAMING_SNAKE_CASE__		  =				torch.randn_like(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				(out - labels).mean()
									loss.backward()

									# re-instantiate the model now enabling gradient checkpointing
									SCREAMING_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

									SCREAMING_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()
									SCREAMING_SNAKE_CASE__		  =				(out_a - labels).mean()
									loss_a.backward()

									# compare the output and parameters gradients
									self.assertTrue((loss - loss_a).abs() < 1e-5		)
									SCREAMING_SNAKE_CASE__		  =				dict(model.named_parameters()		)
									SCREAMING_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			:				Optional[int]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_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		)
									SCREAMING_SNAKE_CASE__		  =				model(**self.dummy_input		)

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



					def 			lowerCAmelCase_					(						self			:				int		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
									SCREAMING_SNAKE_CASE__		  =				model.to(_snake_case		)
									model.eval()

									if torch_device == "mps":
													SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(0		)
									else:
													SCREAMING_SNAKE_CASE__		  =				torch.Generator(device=_snake_case		).manual_seed(0		)

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

									SCREAMING_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":
													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [
													        -4.0078e-01,
													        -3.8323e-04,
													        -1.2681e-01,
													        -1.1462e-01,
													        2.0095e-01,
													        1.0893e-01,
													        -8.8247e-02,
													        -3.0361e-01,
													        -9.8644e-03,
													    ]		)
									elif torch_device == "cpu":
													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
									else:
													SCREAMING_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 lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


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



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



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				List[str]=0   , _snake_case			:				int=(4, 3, 512, 512)   , _snake_case			:				Tuple=False		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				torch.floataa if fpaa else torch.floataa
									SCREAMING_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			:				Tuple   , _snake_case			:				Optional[int]="CompVis/stable-diffusion-v1-4"   , _snake_case			:				Any=False		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				"fp16" if fpaa else None
									SCREAMING_SNAKE_CASE__		  =				torch.floataa if fpaa else torch.floataa

									SCREAMING_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			:				List[Any]   , _snake_case			:				Any=0		)							->       Optional[Any]:
									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			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model()
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_generator(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , generator=_snake_case   , sample_posterior=_snake_case		).sample

									assert sample.shape == image.shape

									SCREAMING_SNAKE_CASE__		  =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
									SCREAMING_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			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[int]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model(fpaa=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case   , fpaa=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_generator(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , generator=_snake_case   , sample_posterior=_snake_case		).sample

									assert sample.shape == image.shape

									SCREAMING_SNAKE_CASE__		  =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
									SCREAMING_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			:				Tuple   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model()
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		).sample

									assert sample.shape == image.shape

									SCREAMING_SNAKE_CASE__		  =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
									SCREAMING_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			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model()
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case   , shape=(3, 4, 64, 64)		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model.decode(_snake_case		).sample

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

									SCREAMING_SNAKE_CASE__		  =				sample[-1, -2:, :2, -2:].flatten().cpu()
									SCREAMING_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			:				Optional[int]   , _snake_case			:				Tuple   , _snake_case			:				Dict		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model(fpaa=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case   , shape=(3, 4, 64, 64)   , fpaa=_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model.decode(_snake_case		).sample

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

									SCREAMING_SNAKE_CASE__		  =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
									SCREAMING_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			:				Any   , _snake_case			:				Any		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model(fpaa=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case   , shape=(3, 4, 64, 64)   , fpaa=_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model.decode(_snake_case		).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													SCREAMING_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			:				List[str]   , _snake_case			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model()
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case   , shape=(3, 4, 64, 64)		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model.decode(_snake_case		).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													SCREAMING_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			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				str		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_vae_model()
									SCREAMING_SNAKE_CASE__		  =				self.get_sd_image(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.get_generator(_snake_case		)

									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model.encode(_snake_case		).latent_dist
													SCREAMING_SNAKE_CASE__		  =				dist.sample(generator=_snake_case		)

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

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

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





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




