



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 						UpperCAmelCase_					( lowerCamelCase__     ,      lowerCamelCase__     ,      unittest.TestCase):
       lowerCamelCase__         =	AutoencoderKL
       lowerCamelCase__         =	'sample'
       lowerCamelCase__         =	1E-2







       @property
       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							str				     =					4
              _lowerCAmelCase  :							int				     =					3
              _lowerCAmelCase  :							List[Any]				     =					(32, 32)

              _lowerCAmelCase  :							Tuple				     =					floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

              return {"sample": image}







       @property
       def      snake_case__   (     self):

              '''simple docstring'''

              return (3, 32, 32)







       @property
       def      snake_case__   (     self):

              '''simple docstring'''

              return (3, 32, 32)







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							int				     =					{
                  '''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  :							str				     =					self.dummy_input
              return init_dict, inputs_dict







       def      snake_case__   (     self):

              '''simple docstring'''

              pass







       def      snake_case__   (     self):

              '''simple docstring'''

              pass







       @unittest.skipIf(torch_device == "mps",    "Gradient checkpointing skipped on MPS")
       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							int				     =					self.prepare_init_args_and_inputs_for_common()
              _lowerCAmelCase  :							Tuple				     =					self.model_class(**__lowerCamelCase)
              model.to(__lowerCamelCase)

              assert not model.is_gradient_checkpointing and model.training

              _lowerCAmelCase  :							Tuple				     =					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  :							Optional[Any]				     =					torch.randn_like(__lowerCamelCase)
              _lowerCAmelCase  :							Tuple				     =					(out - labels).mean()
              loss.backward()

              # re-instantiate the model now enabling gradient checkpointing
              _lowerCAmelCase  :							str				     =					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  :							Dict				     =					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  :							Any				     =					(out_a - labels).mean()
              loss_a.backward()

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

              '''simple docstring'''

              _lowerCAmelCase  :							str				     =					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  :							Optional[int]				     =					model(**self.dummy_input)

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







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							str				     =					AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
              _lowerCAmelCase  :							Tuple				     =					model.to(__lowerCamelCase)
              model.eval()

              if torch_device == "mps":
                     _lowerCAmelCase  :							Union[str, Any]				     =					torch.manual_seed(0)
              else:
                     _lowerCAmelCase  :							Any				     =					torch.Generator(device=__lowerCamelCase).manual_seed(0)

              _lowerCAmelCase  :							Optional[int]				     =					torch.randn(
                  1,    model.config.in_channels,    model.config.sample_size,    model.config.sample_size,    generator=torch.manual_seed(0),    )
              _lowerCAmelCase  :							Dict				     =					image.to(__lowerCamelCase)
              with torch.no_grad():
                     _lowerCAmelCase  :							List[Any]				     =					model(__lowerCamelCase,    sample_posterior=__lowerCamelCase,    generator=__lowerCamelCase).sample

              _lowerCAmelCase  :							Optional[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":
                     _lowerCAmelCase  :							List[str]				     =					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":
                     _lowerCAmelCase  :							str				     =					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  :							Any				     =					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 						UpperCAmelCase_					( unittest.TestCase):







       def      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a=0,    __a=(4, 3, 512, 512),    __a=False):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[int]				     =					torch.floataa if fpaa else torch.floataa
              _lowerCAmelCase  :							Dict				     =					torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCamelCase,    __lowerCamelCase))).to(__lowerCamelCase).to(__lowerCamelCase)
              return image







       def      snake_case__   (     self,    __a="CompVis/stable-diffusion-v1-4",    __a=False):

              '''simple docstring'''

              _lowerCAmelCase  :							Dict				     =					'''fp16''' if fpaa else None
              _lowerCAmelCase  :							Optional[Any]				     =					torch.floataa if fpaa else torch.floataa

              _lowerCAmelCase  :							List[Any]				     =					AutoencoderKL.from_pretrained(
                  __lowerCamelCase,    subfolder="vae",    torch_dtype=__lowerCamelCase,    revision=__lowerCamelCase,    )
              model.to(__lowerCamelCase).eval()

              return model







       def      snake_case__   (     self,    __a=0):

              '''simple docstring'''

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







       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1_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      snake_case__   (     self,    __a,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[int]				     =					self.get_sd_vae_model()
              _lowerCAmelCase  :							Any				     =					self.get_sd_image(__lowerCamelCase)
              _lowerCAmelCase  :							List[str]				     =					self.get_generator(__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							Dict				     =					model(__lowerCamelCase,    generator=__lowerCamelCase,    sample_posterior=__lowerCamelCase).sample

              assert sample.shape == image.shape

              _lowerCAmelCase  :							Optional[Any]				     =					sample[-1, -2:, -2:, :2].flatten().float().cpu()
              _lowerCAmelCase  :							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
               [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      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Union[str, Any]				     =					self.get_sd_vae_model(fpaa=__lowerCamelCase)
              _lowerCAmelCase  :							List[str]				     =					self.get_sd_image(__lowerCamelCase,    fpaa=__lowerCamelCase)
              _lowerCAmelCase  :							List[Any]				     =					self.get_generator(__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							Optional[Any]				     =					model(__lowerCamelCase,    generator=__lowerCamelCase,    sample_posterior=__lowerCamelCase).sample

              assert sample.shape == image.shape

              _lowerCAmelCase  :							Optional[Any]				     =					sample[-1, -2:, :2, -2:].flatten().float().cpu()
              _lowerCAmelCase  :							Any				     =					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      snake_case__   (     self,    __a,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							int				     =					self.get_sd_vae_model()
              _lowerCAmelCase  :							Dict				     =					self.get_sd_image(__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							Tuple				     =					model(__lowerCamelCase).sample

              assert sample.shape == image.shape

              _lowerCAmelCase  :							Dict				     =					sample[-1, -2:, -2:, :2].flatten().float().cpu()
              _lowerCAmelCase  :							List[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, [-0.2_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      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Tuple				     =					self.get_sd_vae_model()
              _lowerCAmelCase  :							Union[str, Any]				     =					self.get_sd_image(__lowerCamelCase,    shape=(3, 4, 64, 64))

              with torch.no_grad():
                     _lowerCAmelCase  :							str				     =					model.decode(__lowerCamelCase).sample

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

              _lowerCAmelCase  :							Dict				     =					sample[-1, -2:, :2, -2:].flatten().cpu()
              _lowerCAmelCase  :							Any				     =					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      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Any				     =					self.get_sd_vae_model(fpaa=__lowerCamelCase)
              _lowerCAmelCase  :							Dict				     =					self.get_sd_image(__lowerCamelCase,    shape=(3, 4, 64, 64),    fpaa=__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							Optional[Any]				     =					model.decode(__lowerCamelCase).sample

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

              _lowerCAmelCase  :							Dict				     =					sample[-1, -2:, :2, -2:].flatten().float().cpu()
              _lowerCAmelCase  :							Union[str, 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      snake_case__   (     self,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Any				     =					self.get_sd_vae_model(fpaa=__lowerCamelCase)
              _lowerCAmelCase  :							str				     =					self.get_sd_image(__lowerCamelCase,    shape=(3, 4, 64, 64),    fpaa=__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							Any				     =					model.decode(__lowerCamelCase).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     _lowerCAmelCase  :							Optional[Any]				     =					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      snake_case__   (     self,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[int]				     =					self.get_sd_vae_model()
              _lowerCAmelCase  :							Dict				     =					self.get_sd_image(__lowerCamelCase,    shape=(3, 4, 64, 64))

              with torch.no_grad():
                     _lowerCAmelCase  :							Tuple				     =					model.decode(__lowerCamelCase).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     _lowerCAmelCase  :							Optional[Any]				     =					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, [-0.3_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      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							Tuple				     =					self.get_sd_vae_model()
              _lowerCAmelCase  :							Tuple				     =					self.get_sd_image(__lowerCamelCase)
              _lowerCAmelCase  :							Optional[Any]				     =					self.get_generator(__lowerCamelCase)

              with torch.no_grad():
                     _lowerCAmelCase  :							List[Any]				     =					model.encode(__lowerCamelCase).latent_dist
                     _lowerCAmelCase  :							int				     =					dist.sample(generator=__lowerCamelCase)

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

              _lowerCAmelCase  :							Optional[int]				     =					sample[0, -1, -3:, -3:].flatten().cpu()
              _lowerCAmelCase  :							int				     =					torch.tensor(__lowerCamelCase)

              _lowerCAmelCase  :							Any				     =					3E-3 if torch_device != '''mps''' else 1E-2
              assert torch_all_close(__lowerCamelCase,    __lowerCamelCase,    atol=__lowerCamelCase)





def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if length <= 0 or not isinstance(_lowerCamelCase       ,				_lowerCamelCase						):
														raise ValueError("Length must be a positive integer."						)
							return [n * (2 * n - 1) for n in range(_lowerCamelCase						)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))
