


'''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      lowerCamelCase_  (      __a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	AutoencoderKL
      lowerCAmelCase__							=	'sample'
      lowerCAmelCase__							=	1E-2






      @property
      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


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

          UpperCAmelCase__     :						Dict						=							floats_tensor((batch_size, num_channels) + sizes					).to(UpperCamelCase_					)

          return {"sample": image}






      @property
      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          return (3, 32, 32)






      @property
      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          return (3, 32, 32)






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          pass






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          pass






      @unittest.skipIf(torch_device == '''mps'''		,		'''Gradient checkpointing skipped on MPS'''					)
      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						List[str]						=							self.prepare_init_args_and_inputs_for_common()
          UpperCAmelCase__     :						Any						=							self.model_class(**UpperCamelCase_					)
          model.to(UpperCamelCase_					)

          assert not model.is_gradient_checkpointing and model.training

          UpperCAmelCase__     :						Union[str, Any]						=							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()

          UpperCAmelCase__     :						List[Any]						=							torch.randn_like(UpperCamelCase_					)
          UpperCAmelCase__     :						str						=							(out - labels).mean()
          loss.backward()

          # re-instantiate the model now enabling gradient checkpointing
          UpperCAmelCase__     :						Union[str, 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

          UpperCAmelCase__     :						Tuple						=							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()
          UpperCAmelCase__     :						Tuple						=							(out_a - labels).mean()
          loss_a.backward()

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


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						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_					)
          UpperCAmelCase__     :						List[str]						=							model(**self.dummy_input					)

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






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''					)
          UpperCAmelCase__     :						Dict						=							model.to(UpperCamelCase_					)
          model.eval()

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

          UpperCAmelCase__     :						Union[str, Any]						=							torch.randn(
              1		,		model.config.in_channels		,		model.config.sample_size		,		model.config.sample_size		,		generator=torch.manual_seed(0					)		,		)
          UpperCAmelCase__     :						List[Any]						=							image.to(UpperCamelCase_					)
          with torch.no_grad():
              UpperCAmelCase__     :						str						=							model(UpperCamelCase_		,		sample_posterior=UpperCamelCase_		,		generator=UpperCamelCase_					).sample

          UpperCAmelCase__     :						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":
              UpperCAmelCase__     :						Union[str, Any]						=							torch.tensor(
                  [
                      -4.00_78e-01,
                      -3.83_23e-04,
                      -1.26_81e-01,
                      -1.14_62e-01,
                      2.00_95e-01,
                      1.08_93e-01,
                      -8.82_47e-02,
                      -3.03_61e-01,
                      -9.86_44e-03,
                  ]					)
          elif torch_device == "cpu":
              UpperCAmelCase__     :						str						=							torch.tensor(
                  [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]					)
          else:
              UpperCAmelCase__     :						List[str]						=							torch.tensor(
                  [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]					)

          self.assertTrue(torch_all_close(UpperCamelCase_		,		UpperCamelCase_		,		rtol=1e-2					)					)




@slow
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       Optional[Any]		,		_A							:       List[str]					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       List[str]		,		_A							:       List[str]=0		,		_A							:       Dict=(4, 3, 512, 512)		,		_A							:       Dict=False					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							torch.floataa if fpaa else torch.floataa
          UpperCAmelCase__     :						Union[str, Any]						=							torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCamelCase_		,		UpperCamelCase_					)					)					).to(UpperCamelCase_					).to(UpperCamelCase_					)
          return image






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       List[str]="CompVis/stable-diffusion-v1-4"		,		_A							:       List[Any]=False					):


          '''simple docstring'''


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

          UpperCAmelCase__     :						str						=							AutoencoderKL.from_pretrained(
              UpperCamelCase_		,		subfolder='''vae'''		,		torch_dtype=UpperCamelCase_		,		revision=UpperCamelCase_		,		)
          model.to(UpperCamelCase_					).eval()

          return model






      def lowercase_				(   self							:       Tuple		,		_A							:       Optional[int]=0					):


          '''simple docstring'''


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






      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
              [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
              # fmt: on
          ]					)
      def lowercase_				(   self							:       Dict		,		_A							:       Dict		,		_A							:       Optional[Any]		,		_A							:       int					):


          '''simple docstring'''


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

          with torch.no_grad():
              UpperCAmelCase__     :						Any						=							model(UpperCamelCase_		,		generator=UpperCamelCase_		,		sample_posterior=UpperCamelCase_					).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(UpperCamelCase_		,		UpperCamelCase_		,		atol=3e-3					)






      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
              [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
              # fmt: on
          ]					)
      @require_torch_gpu
      def lowercase_				(   self							:       Optional[Any]		,		_A							:       Union[str, Any]		,		_A							:       int					):


          '''simple docstring'''


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

          with torch.no_grad():
              UpperCAmelCase__     :						Optional[int]						=							model(UpperCamelCase_		,		generator=UpperCamelCase_		,		sample_posterior=UpperCamelCase_					).sample

          assert sample.shape == image.shape

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

          assert torch_all_close(UpperCamelCase_		,		UpperCamelCase_		,		atol=1e-2					)






      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
              [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
              # fmt: on
          ]					)
      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       str		,		_A							:       Optional[int]		,		_A							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							self.get_sd_vae_model()
          UpperCAmelCase__     :						Tuple						=							self.get_sd_image(UpperCamelCase_					)

          with torch.no_grad():
              UpperCAmelCase__     :						Optional[int]						=							model(UpperCamelCase_					).sample

          assert sample.shape == image.shape

          UpperCAmelCase__     :						Optional[int]						=							sample[-1, -2:, -2:, :2].flatten().float().cpu()
          UpperCAmelCase__     :						Optional[int]						=							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, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
              [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
              # fmt: on
          ]					)
      @require_torch_gpu
      def lowercase_				(   self							:       str		,		_A							:       List[str]		,		_A							:       Tuple					):


          '''simple docstring'''


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

          with torch.no_grad():
              UpperCAmelCase__     :						str						=							model.decode(UpperCamelCase_					).sample

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

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

          assert torch_all_close(UpperCamelCase_		,		UpperCamelCase_		,		atol=1e-3					)






      @parameterized.expand(
          [
              # fmt: off
              [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
              [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
              # fmt: on
          ]					)
      @require_torch_gpu
      def lowercase_				(   self							:       List[str]		,		_A							:       Tuple		,		_A							:       int					):


          '''simple docstring'''


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

          with torch.no_grad():
              UpperCAmelCase__     :						Optional[int]						=							model.decode(UpperCamelCase_					).sample

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

          UpperCAmelCase__     :						Union[str, Any]						=							sample[-1, -2:, :2, -2:].flatten().float().cpu()
          UpperCAmelCase__     :						Union[str, Any]						=							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 lowercase_				(   self							:       int		,		_A							:       Optional[Any]					):


          '''simple docstring'''


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

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

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
              UpperCAmelCase__     :						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 lowercase_				(   self							:       List[str]		,		_A							:       Any					):


          '''simple docstring'''


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

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

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
              UpperCAmelCase__     :						Union[str, Any]						=							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, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
              [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
              # fmt: on
          ]					)
      def lowercase_				(   self							:       str		,		_A							:       Any		,		_A							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							self.get_sd_vae_model()
          UpperCAmelCase__     :						str						=							self.get_sd_image(UpperCamelCase_					)
          UpperCAmelCase__     :						Optional[int]						=							self.get_generator(UpperCamelCase_					)

          with torch.no_grad():
              UpperCAmelCase__     :						Optional[int]						=							model.encode(UpperCamelCase_					).latent_dist
              UpperCAmelCase__     :						List[Any]						=							dist.sample(generator=UpperCamelCase_					)

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

          UpperCAmelCase__     :						int						=							sample[0, -1, -3:, -3:].flatten().cpu()
          UpperCAmelCase__     :						Dict						=							torch.tensor(UpperCamelCase_					)

          UpperCAmelCase__     :						Tuple						=							3e-3 if torch_device != '''mps''' else 1e-2
          assert torch_all_close(UpperCamelCase_		,		UpperCamelCase_		,		atol=UpperCamelCase_					)







'''simple docstring'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



