





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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     AutoencoderKL
 lowercase_						=     "sample"
 lowercase_						=     1E-2

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



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =4
   lowerCamelCase__: Union[str, Any]         =3
   lowerCamelCase__: Tuple         =(32, 32)

   lowerCamelCase__: Optional[Any]         =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)

   return {"sample": image}

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Optional[int]:



   '''simple docstring'''



   return (3, 32, 32)

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->List[str]:



   '''simple docstring'''



   return (3, 32, 32)

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



   '''simple docstring'''



   lowerCamelCase__: Optional[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__: Optional[int]         =self.dummy_input
   return init_dict, inputs_dict

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->List[Any]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__    ,   lowerCamelCase__: str         =self.prepare_init_args_and_inputs_for_common()
   lowerCamelCase__: Union[str, Any]         =self.model_class(**UpperCAmelCase_)
   model.to(UpperCAmelCase_)

   assert not model.is_gradient_checkpointing and model.training

   lowerCamelCase__: Tuple         =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()

   lowerCamelCase__: List[str]         =torch.randn_like(UpperCAmelCase_)
   lowerCamelCase__: Dict         =(out - labels).mean()
   loss.backward()

   # re-instantiate the model now enabling gradient checkpointing
   lowerCamelCase__: List[Any]         =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

   lowerCamelCase__: Optional[int]         =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()
   lowerCamelCase__: Optional[int]         =(out_a - labels).mean()
   loss_a.backward()

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



   '''simple docstring'''



   lowerCamelCase__    ,   lowerCamelCase__: int         =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_)
   lowerCamelCase__: List[Any]         =model(**self.dummy_input)

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







 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Any         =AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
   lowerCamelCase__: str         =model.to(UpperCAmelCase_)
   model.eval()

   if torch_device == "mps":
     lowerCamelCase__: Optional[int]         =torch.manual_seed(0)
   else:
     lowerCamelCase__: Optional[Any]         =torch.Generator(device=UpperCAmelCase_).manual_seed(0)

   lowerCamelCase__: int         =torch.randn(
       1			,					model.config.in_channels			,					model.config.sample_size			,					model.config.sample_size			,					generator=torch.manual_seed(0)			,					)
   lowerCamelCase__: Tuple         =image.to(UpperCAmelCase_)
   with torch.no_grad():
     lowerCamelCase__: Dict         =model(UpperCAmelCase_			,					sample_posterior=UpperCAmelCase_			,					generator=UpperCAmelCase_).sample

   lowerCamelCase__: str         =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(
         [
             -4.0_0_7_8E-0_1,
             -3.8_3_2_3E-0_4,
             -1.2_6_8_1E-0_1,
             -1.1_4_6_2E-0_1,
             2.0_0_9_5E-0_1,
             1.0_8_9_3E-0_1,
             -8.8_2_4_7E-0_2,
             -3.0_3_6_1E-0_1,
             -9.8_6_4_4E-0_3,
         ])
   elif torch_device == "cpu":
     lowerCamelCase__: str         =torch.tensor(
         [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026])
   else:
     lowerCamelCase__: Optional[int]         =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   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Optional[int])			->Union[str, Any]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->List[Any]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Optional[int]=0			,					UpperCAmelCase_							:  Any=(4, 3, 512, 512)			,					UpperCAmelCase_							:  int=False)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =torch.floataa if fpaa else torch.floataa
   lowerCamelCase__: Optional[Any]         =torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCAmelCase_			,					UpperCAmelCase_))).to(UpperCAmelCase_).to(UpperCAmelCase_)
   return image

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[Any]="CompVis/stable-diffusion-v1-4"			,					UpperCAmelCase_							:  Any=False)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         ="fp16" if fpaa else None
   lowerCamelCase__: Optional[Any]         =torch.floataa if fpaa else torch.floataa

   lowerCamelCase__: str         =AutoencoderKL.from_pretrained(
       UpperCAmelCase_			,					subfolder="vae"			,					torch_dtype=UpperCAmelCase_			,					revision=UpperCAmelCase_			,					)
   model.to(UpperCAmelCase_).eval()

   return model

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  Optional[int]=0)			->List[str]:



   '''simple docstring'''



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

 @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 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  int)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_sd_vae_model()
   lowerCamelCase__: str         =self.get_sd_image(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =self.get_generator(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Tuple         =model(UpperCAmelCase_			,					generator=UpperCAmelCase_			,					sample_posterior=UpperCAmelCase_).sample

   assert sample.shape == image.shape

   lowerCamelCase__: List[Any]         =sample[-1, -2:, -2:, :2].flatten().float().cpu()
   lowerCamelCase__: 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
         [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 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  Optional[int])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.get_sd_vae_model(fpaa=UpperCAmelCase_)
   lowerCamelCase__: List[str]         =self.get_sd_image(UpperCAmelCase_			,					fpaa=UpperCAmelCase_)
   lowerCamelCase__: List[str]         =self.get_generator(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Union[str, Any]         =model(UpperCAmelCase_			,					generator=UpperCAmelCase_			,					sample_posterior=UpperCAmelCase_).sample

   assert sample.shape == image.shape

   lowerCamelCase__: Any         =sample[-1, -2:, :2, -2:].flatten().float().cpu()
   lowerCamelCase__: str         =torch.tensor(UpperCAmelCase_)

   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					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 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Any)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.get_sd_vae_model()
   lowerCamelCase__: Optional[Any]         =self.get_sd_image(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Any         =model(UpperCAmelCase_).sample

   assert sample.shape == image.shape

   lowerCamelCase__: Dict         =sample[-1, -2:, -2:, :2].flatten().float().cpu()
   lowerCamelCase__: Dict         =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
         [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 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Tuple)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.get_sd_vae_model()
   lowerCamelCase__: List[str]         =self.get_sd_image(UpperCAmelCase_			,					shape=(3, 4, 64, 64))

   with torch.no_grad():
     lowerCamelCase__: Optional[Any]         =model.decode(UpperCAmelCase_).sample

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

   lowerCamelCase__: str         =sample[-1, -2:, :2, -2:].flatten().cpu()
   lowerCamelCase__: str         =torch.tensor(UpperCAmelCase_)

   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					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 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Tuple)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.get_sd_vae_model(fpaa=UpperCAmelCase_)
   lowerCamelCase__: List[Any]         =self.get_sd_image(UpperCAmelCase_			,					shape=(3, 4, 64, 64)			,					fpaa=UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Optional[Any]         =model.decode(UpperCAmelCase_).sample

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

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

   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					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 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  str)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =self.get_sd_vae_model(fpaa=UpperCAmelCase_)
   lowerCamelCase__: Tuple         =self.get_sd_image(UpperCAmelCase_			,					shape=(3, 4, 64, 64)			,					fpaa=UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: str         =model.decode(UpperCAmelCase_).sample

   model.enable_xformers_memory_efficient_attention()
   with torch.no_grad():
     lowerCamelCase__: Union[str, Any]         =model.decode(UpperCAmelCase_).sample

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

   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					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 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Dict)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_sd_vae_model()
   lowerCamelCase__: str         =self.get_sd_image(UpperCAmelCase_			,					shape=(3, 4, 64, 64))

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

   model.enable_xformers_memory_efficient_attention()
   with torch.no_grad():
     lowerCamelCase__: str         =model.decode(UpperCAmelCase_).sample

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

   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					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 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Optional[int])			->Any:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =self.get_sd_vae_model()
   lowerCamelCase__: List[Any]         =self.get_sd_image(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =self.get_generator(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Any         =model.encode(UpperCAmelCase_).latent_dist
     lowerCamelCase__: Optional[Any]         =dist.sample(generator=UpperCAmelCase_)

   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__: Any         =torch.tensor(UpperCAmelCase_)

   lowerCamelCase__: Tuple         =3E-3 if torch_device != "mps" else 1E-2
   assert torch_all_close(UpperCAmelCase_			,					UpperCAmelCase_			,					atol=UpperCAmelCase_)



"""simple docstring"""


from maths.prime_check import is_prime






def    _snake_case					(		snake_case__ :  int  ):
	if not isinstance(snake_case__						,   snake_case__  ):
		A     = F'Input value of [number={number}] must be an integer'
		raise TypeError(snake_case__  )
	if is_prime(snake_case__  ) and is_prime(number + 2  ):
		return number + 2
	else:
		return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()