'''simple docstring'''

from maths.prime_factors import prime_factors







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   if not isinstance(_A	,      _A     ):
      UpperCAmelCase__				:					Dict 					=	F'''Input value of [number={number}] must be an integer'''
      raise TypeError(_A     )
   if number < 1:
      raise ValueError('''Input must be a positive integer'''     )
   return -1 if len(prime_factors(_A     )     ) % 2 else 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''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_				)




