'''simple docstring'''

UpperCamelCase__  :						Dict					=      [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

UpperCamelCase__  :						Optional[int]					=      [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

UpperCamelCase__  :						Tuple					=      [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

UpperCamelCase__  :						Dict					=      [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

UpperCamelCase__  :						Any					=      [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

UpperCamelCase__  :						Optional[Any]					=      [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

UpperCamelCase__  :						Optional[int]					=      [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

UpperCamelCase__  :						List[str]					=      [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]





'''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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= AutoencoderKL
      UpperCAmelCase_   :   Any   				= '''sample'''
      UpperCAmelCase_   :   List[str]   				= 1E-2




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	4
         UpperCAmelCase__				:					Optional[int] 					=	3
         UpperCAmelCase__				:					Tuple 					=	(32, 32)

         UpperCAmelCase__				:					Optional[int] 					=	floats_tensor((batch_size, num_channels) + sizes				).to(lowerCamelCase_				)

         return {"sample": image}




      @property
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         return (3, 32, 32)




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return (3, 32, 32)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	{
             '''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__				:					Optional[Any] 					=	self.dummy_input
         return init_dict, inputs_dict




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         pass




      @unittest.skipIf(torch_device == '''mps'''       ,'''Gradient checkpointing skipped on MPS'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase__				:					Union[str, Any] 					=	self.model_class(**lowerCamelCase_				)
         model.to(lowerCamelCase_				)

         assert not model.is_gradient_checkpointing and model.training

         UpperCAmelCase__				:					Any 					=	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()

         UpperCAmelCase__				:					Optional[int] 					=	torch.randn_like(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	(out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         UpperCAmelCase__				:					Optional[int] 					=	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

         UpperCAmelCase__				:					int 					=	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()
         UpperCAmelCase__				:					Union[str, Any] 					=	(out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1e-5				)
         UpperCAmelCase__				:					Union[str, Any] 					=	dict(model.named_parameters()				)
         UpperCAmelCase__				:					int 					=	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:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	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_				)
         UpperCAmelCase__				:					Optional[int] 					=	model(**self.dummy_input				)

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




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
         UpperCAmelCase__				:					Optional[Any] 					=	model.to(lowerCamelCase_				)
         model.eval()

         if torch_device == "mps":
            UpperCAmelCase__				:					Optional[Any] 					=	torch.manual_seed(0				)
         else:
            UpperCAmelCase__				:					Tuple 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(0				)

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

         UpperCAmelCase__				:					int 					=	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__				:					int 					=	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":
            UpperCAmelCase__				:					List[Any] 					=	torch.tensor(
                [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
         else:
            UpperCAmelCase__				:					Optional[Any] 					=	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(lowerCamelCase_       ,lowerCamelCase_       ,rtol=1e-2				)				)




@slow
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase_				) for s in shape]				)}.npy'''




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



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=0       ,lowerCamelCase_=(4, 3, 512, 512)       ,lowerCamelCase_=False				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	torch.floataa if fpaa else torch.floataa
         UpperCAmelCase__				:					Dict 					=	torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_       ,lowerCamelCase_				)				)				).to(lowerCamelCase_				).to(lowerCamelCase_				)
         return image




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_="CompVis/stable-diffusion-v1-4"       ,lowerCamelCase_=False				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	'''fp16''' if fpaa else None
         UpperCAmelCase__				:					str 					=	torch.floataa if fpaa else torch.floataa

         UpperCAmelCase__				:					List[Any] 					=	AutoencoderKL.from_pretrained(
             lowerCamelCase_       ,subfolder='''vae'''       ,torch_dtype=lowerCamelCase_       ,revision=lowerCamelCase_       ,)
         model.to(lowerCamelCase_				).eval()

         return model




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=0				)      ->      Optional[Any]:



         '''simple docstring'''



         if torch_device == "mps":
            return torch.manual_seed(lowerCamelCase_				)
         return torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)




      @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       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					str 					=	self.get_sd_image(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	self.get_generator(lowerCamelCase_				)

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

         assert sample.shape == image.shape

         UpperCAmelCase__				:					List[Any] 					=	sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase__				:					Tuple 					=	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, [-0.0513, 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       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	self.get_sd_image(lowerCamelCase_       ,fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_generator(lowerCamelCase_				)

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

         assert sample.shape == image.shape

         UpperCAmelCase__				:					Optional[Any] 					=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase__				:					List[str] 					=	torch.tensor(lowerCamelCase_				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,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       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Optional[int] 					=	self.get_sd_image(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_				).sample

         assert sample.shape == image.shape

         UpperCAmelCase__				:					str 					=	sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase__				:					Optional[Any] 					=	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, [-0.2051, -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       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Dict 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)				)

         with torch.no_grad():
            UpperCAmelCase__				:					Optional[Any] 					=	model.decode(lowerCamelCase_				).sample

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

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

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,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       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)       ,fpaa=lowerCamelCase_				)

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

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

         UpperCAmelCase__				:					List[str] 					=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase__				:					Any 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)       ,fpaa=lowerCamelCase_				)

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

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

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

         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 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					List[Any] 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)				)

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

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

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

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,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       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_image(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	self.get_generator(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					Optional[int] 					=	model.encode(lowerCamelCase_				).latent_dist
            UpperCAmelCase__				:					Tuple 					=	dist.sample(generator=lowerCamelCase_				)

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

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

         UpperCAmelCase__				:					List[Any] 					=	3e-3 if torch_device != '''mps''' else 1e-2
         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=lowerCamelCase_				)




