





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       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




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






							@property
							def  A_     ( self     :					str						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						4
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						3
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(3_2, 3_2)

														SCREAMING_SNAKE_CASE__       :      Any     =						floats_tensor((batch_size, num_channels) + sizes						).to(_UpperCAmelCase						)

														return {"sample": image}






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





														"""simple docstring"""
														return (3, 3_2, 3_2)






							@property
							def  A_     ( self     :					Optional[Any]						)				->			Any:





														"""simple docstring"""
														return (3, 3_2, 3_2)






							def  A_     ( self     :					Dict						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						{
														    "block_out_channels": [3_2, 6_4],
														    "in_channels": 3,
														    "out_channels": 3,
														    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
														    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
														    "latent_channels": 4,
														}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.dummy_input
														return init_dict, inputs_dict






							def  A_     ( self     :					Tuple						)				->			Optional[int]:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Tuple						)				->			Any:





														"""simple docstring"""
														pass






							@unittest.skipIf(torch_device == "mps",			"Gradient checkpointing skipped on MPS"						)
							def  A_     ( self     :					Union[str, Any]						)				->			Tuple:





														"""simple docstring"""
														# enable deterministic behavior for gradient checkpointing
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						self.prepare_init_args_and_inputs_for_common()
														SCREAMING_SNAKE_CASE__       :      int     =						self.model_class(**_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)

														assert not model.is_gradient_checkpointing and model.training

														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(**_UpperCAmelCase						).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__       :      Optional[int]     =						torch.randn_like(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						(out - labels).mean()
														loss.backward()

														# re-instantiate the model now enabling gradient checkpointing
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model_class(**_UpperCAmelCase						)
														# clone model
														model_a.load_state_dict(model.state_dict()						)
														model_a.to(_UpperCAmelCase						)
														model_a.enable_gradient_checkpointing()

														assert model_a.is_gradient_checkpointing and model_a.training

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model_a(**_UpperCAmelCase						).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__       :      List[Any]     =						(out_a - labels).mean()
														loss_a.backward()

														# compare the output and parameters gradients
														self.assertTrue((loss - loss_a).abs() < 1E-5						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						dict(model.named_parameters()						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						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  A_     ( self     :					Any						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy",			output_loading_info=_UpperCAmelCase						)
														self.assertIsNotNone(_UpperCAmelCase						)
														self.assertEqual(len(loading_info["missing_keys"]						),			0						)

														model.to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						model(**self.dummy_input						)

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






							def  A_     ( self     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						model.to(_UpperCAmelCase						)
														model.eval()

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

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.randn(
														    1,			model.config.in_channels,			model.config.sample_size,			model.config.sample_size,			generator=torch.manual_seed(0						),			)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image.to(_UpperCAmelCase						)
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(_UpperCAmelCase,			sample_posterior=_UpperCAmelCase,			generator=_UpperCAmelCase						).sample

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						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__       :      Dict     =						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__       :      Union[str, Any]     =						torch.tensor(
																					    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						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(_UpperCAmelCase,			_UpperCAmelCase,			rtol=1E-2						)						)


@slow
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Tuple						)				->			Any:





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






							def  A_     ( self     :					int						)				->			List[str]:





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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					int=0,			_UpperCAmelCase     :					Tuple=(4, 3, 5_1_2, 5_1_2),			_UpperCAmelCase     :					str=False						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						torch.floataa if fpaa else torch.floataa
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.from_numpy(load_hf_numpy(self.get_file_format(_UpperCAmelCase,			_UpperCAmelCase						)						)						).to(_UpperCAmelCase						).to(_UpperCAmelCase						)
														return image






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					Optional[Any]="CompVis/stable-diffusion-v1-4",			_UpperCAmelCase     :					Optional[Any]=False						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"fp16" if fpaa else None
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.floataa if fpaa else torch.floataa

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						AutoencoderKL.from_pretrained(
														    _UpperCAmelCase,			subfolder="vae",			torch_dtype=_UpperCAmelCase,			revision=_UpperCAmelCase,			)
														model.to(_UpperCAmelCase						).eval()

														return model






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[int]=0						)				->			int:





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






							@parameterized.expand(
							    [
							        # fmt: off
							        [3_3, [-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]],
							        [4_7, [-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  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_sd_vae_model()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_sd_image(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						self.get_generator(_UpperCAmelCase						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase,			generator=_UpperCAmelCase,			sample_posterior=_UpperCAmelCase						).sample

														assert sample.shape == image.shape

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

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=3E-3						)






							@parameterized.expand(
							    [
							        # fmt: off
							        [3_3, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
							        [4_7, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
							        # fmt: on
							    ]						)
							@require_torch_gpu
							def  A_     ( self     :					Any,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Dict						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_sd_image(_UpperCAmelCase,			fpaa=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_generator(_UpperCAmelCase						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase,			generator=_UpperCAmelCase,			sample_posterior=_UpperCAmelCase						).sample

														assert sample.shape == image.shape

														SCREAMING_SNAKE_CASE__       :      Tuple     =						sample[-1, -2:, :2, -2:].flatten().float().cpu()
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.tensor(_UpperCAmelCase						)

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=1E-2						)






							@parameterized.expand(
							    [
							        # fmt: off
							        [3_3, [-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]],
							        [4_7, [-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  A_     ( self     :					int,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Dict						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.get_sd_vae_model()
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_sd_image(_UpperCAmelCase						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      str     =						model(_UpperCAmelCase						).sample

														assert sample.shape == image.shape

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

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=3E-3						)






							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
							        [3_7, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
							        # fmt: on
							    ]						)
							@require_torch_gpu
							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					int						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_sd_vae_model()
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_sd_image(_UpperCAmelCase,			shape=(3, 4, 6_4, 6_4)						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model.decode(_UpperCAmelCase						).sample

														assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						sample[-1, -2:, :2, -2:].flatten().cpu()
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(_UpperCAmelCase						)

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=1E-3						)






							@parameterized.expand(
							    [
							        # fmt: off
							        [2_7, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
							        [1_6, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
							        # fmt: on
							    ]						)
							@require_torch_gpu
							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					str						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_sd_image(_UpperCAmelCase,			shape=(3, 4, 6_4, 6_4),			fpaa=_UpperCAmelCase						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.decode(_UpperCAmelCase						).sample

														assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

														SCREAMING_SNAKE_CASE__       :      str     =						sample[-1, -2:, :2, -2:].flatten().float().cpu()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.tensor(_UpperCAmelCase						)

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=5E-3						)






							@parameterized.expand([(1_3,), (1_6,), (2_7,)]						)
							@require_torch_gpu
							@unittest.skipIf(not is_xformers_available(),			reason="xformers is not required when using PyTorch 2.0."						)
							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_sd_vae_model(fpaa=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_sd_image(_UpperCAmelCase,			shape=(3, 4, 6_4, 6_4),			fpaa=_UpperCAmelCase						)

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

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model.decode(_UpperCAmelCase						).sample

														assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=1E-1						)






							@parameterized.expand([(1_3,), (1_6,), (3_7,)]						)
							@require_torch_gpu
							@unittest.skipIf(not is_xformers_available(),			reason="xformers is not required when using PyTorch 2.0."						)
							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					Dict						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_sd_vae_model()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_sd_image(_UpperCAmelCase,			shape=(3, 4, 6_4, 6_4)						)

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

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model.decode(_UpperCAmelCase						).sample

														assert list(sample.shape						) == [3, 3, 5_1_2, 5_1_2]

														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=1E-2						)






							@parameterized.expand(
							    [
							        # fmt: off
							        [3_3, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
							        [4_7, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
							        # fmt: on
							    ]						)
							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_sd_vae_model()
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_sd_image(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						self.get_generator(_UpperCAmelCase						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      str     =						model.encode(_UpperCAmelCase						).latent_dist
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						dist.sample(generator=_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__       :      str     =						sample[0, -1, -3:, -3:].flatten().cpu()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.tensor(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						3E-3 if torch_device != "mps" else 1E-2
														assert torch_all_close(_UpperCAmelCase,			_UpperCAmelCase,			atol=_UpperCAmelCase						)













from __future__ import annotations




def _a (  SCREAMING_SNAKE_CASE__      :  list[float]			,				SCREAMING_SNAKE_CASE__      :  list[float]       )	->    float:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						sorted(numsa + numsa       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						divmod(len(SCREAMING_SNAKE_CASE__       )			,				2       )
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_lowerCamelCase :    List[str]  							= [float(x) for x in input('''Enter the elements of first array: ''').split()]
		_lowerCamelCase :    Any  							= [float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")






