





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__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Any							=   AutoencoderKL
      A_						:    Tuple							=   'sample'
      A_						:    List[str]							=   1e-2


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





         """simple docstring"""



         __snake_case								=       4
         __snake_case								=       3
         __snake_case								=       (32, 32)

         __snake_case								=       floats_tensor((batch_size, num_channels) + sizes				).to(a__				)

         return {"sample": image}


      @property
      def        a			(self	:				str				):





         """simple docstring"""



         return (3, 32, 32)


      @property
      def        a			(self	:				Any				):





         """simple docstring"""



         return (3, 32, 32)


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       {
             '''block_out_channels''': [32, 64],
             '''in_channels''': 3,
             '''out_channels''': 3,
             '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
             '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
             '''latent_channels''': 4,
         }
         __snake_case								=       self.dummy_input
         return init_dict, inputs_dict


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         pass


      def        a			(self	:				Any				):





         """simple docstring"""



         pass


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





         """simple docstring"""



         __snake_case	,     __snake_case								=       self.prepare_init_args_and_inputs_for_common()
         __snake_case								=       self.model_class(**a__				)
         model.to(a__				)

         assert not model.is_gradient_checkpointing and model.training

         __snake_case								=       model(**a__				).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()

         __snake_case								=       torch.randn_like(a__				)
         __snake_case								=       (out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         __snake_case								=       self.model_class(**a__				)
         # clone model
         model_a.load_state_dict(model.state_dict()				)
         model_a.to(a__				)
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         __snake_case								=       model_a(**a__				).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()
         __snake_case								=       (out_a - labels).mean()
         loss_a.backward()

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





         """simple docstring"""



         __snake_case	,     __snake_case								=       AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''    ,      output_loading_info=a__				)
         self.assertIsNotNone(a__				)
         self.assertEqual(len(loading_info['''missing_keys''']				)    ,      0				)

         model.to(a__				)
         __snake_case								=       model(**self.dummy_input				)

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


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
         __snake_case								=       model.to(a__				)
         model.eval()

         if torch_device == "mps":
            __snake_case								=       torch.manual_seed(0				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(0				)

         __snake_case								=       torch.randn(
             1    ,      model.config.in_channels    ,      model.config.sample_size    ,      model.config.sample_size    ,      generator=torch.manual_seed(0				)    ,      )
         __snake_case								=       image.to(a__				)
         with torch.no_grad():
            __snake_case								=       model(a__    ,      sample_posterior=a__    ,      generator=a__				).sample

         __snake_case								=       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":
            __snake_case								=       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":
            __snake_case								=       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:
            __snake_case								=       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(a__    ,      a__    ,      rtol=1E-2				)				)






@slow
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int    ,      a__	:				Dict    ,      a__	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]    ,      a__	:				List[Any]=0    ,      a__	:				Union[str, Any]=(4, 3, 512, 512)    ,      a__	:				List[Any]=False				):





         """simple docstring"""



         __snake_case								=       torch.floataa if fpaa else torch.floataa
         __snake_case								=       torch.from_numpy(load_hf_numpy(self.get_file_format(a__    ,      a__				)				)				).to(a__				).to(a__				)
         return image


      def        a			(self	:				int    ,      a__	:				List[str]="CompVis/stable-diffusion-v1-4"    ,      a__	:				Tuple=False				):





         """simple docstring"""



         __snake_case								=       '''fp16''' if fpaa else None
         __snake_case								=       torch.floataa if fpaa else torch.floataa

         __snake_case								=       AutoencoderKL.from_pretrained(
             a__    ,      subfolder='''vae'''    ,      torch_dtype=a__    ,      revision=a__    ,      )
         model.to(a__				).eval()

         return model


      def        a			(self	:				str    ,      a__	:				Tuple=0				):





         """simple docstring"""



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


      @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        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				Optional[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model()
         __snake_case								=       self.get_sd_image(a__				)
         __snake_case								=       self.get_generator(a__				)

         with torch.no_grad():
            __snake_case								=       model(a__    ,      generator=a__    ,      sample_posterior=a__				).sample

         assert sample.shape == image.shape

         __snake_case								=       sample[-1, -2:, -2:, :2].flatten().float().cpu()
         __snake_case								=       torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				Optional[Any]    ,      a__	:				List[str]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model(fpaa=a__				)
         __snake_case								=       self.get_sd_image(a__    ,      fpaa=a__				)
         __snake_case								=       self.get_generator(a__				)

         with torch.no_grad():
            __snake_case								=       model(a__    ,      generator=a__    ,      sample_posterior=a__				).sample

         assert sample.shape == image.shape

         __snake_case								=       sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __snake_case								=       torch.tensor(a__				)

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				str    ,      a__	:				Optional[int]    ,      a__	:				Optional[int]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model()
         __snake_case								=       self.get_sd_image(a__				)

         with torch.no_grad():
            __snake_case								=       model(a__				).sample

         assert sample.shape == image.shape

         __snake_case								=       sample[-1, -2:, -2:, :2].flatten().float().cpu()
         __snake_case								=       torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				List[str]    ,      a__	:				str    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model()
         __snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)				)

         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

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

         __snake_case								=       sample[-1, -2:, :2, -2:].flatten().cpu()
         __snake_case								=       torch.tensor(a__				)

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				int    ,      a__	:				Optional[int]    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model(fpaa=a__				)
         __snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)    ,      fpaa=a__				)

         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

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

         __snake_case								=       sample[-1, -2:, :2, -2:].flatten().float().cpu()
         __snake_case								=       torch.tensor(a__				)

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model(fpaa=a__				)
         __snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)    ,      fpaa=a__				)

         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

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

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				Optional[Any]    ,      a__	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model()
         __snake_case								=       self.get_sd_image(a__    ,      shape=(3, 4, 64, 64)				)

         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            __snake_case								=       model.decode(a__				).sample

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

         assert torch_all_close(a__    ,      a__    ,      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        a			(self	:				Optional[int]    ,      a__	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_sd_vae_model()
         __snake_case								=       self.get_sd_image(a__				)
         __snake_case								=       self.get_generator(a__				)

         with torch.no_grad():
            __snake_case								=       model.encode(a__				).latent_dist
            __snake_case								=       dist.sample(generator=a__				)

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

         __snake_case								=       sample[0, -1, -3:, -3:].flatten().cpu()
         __snake_case								=       torch.tensor(a__				)

         __snake_case								=       3E-3 if torch_device != '''mps''' else 1E-2
         assert torch_all_close(a__    ,      a__    ,      atol=a__				)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   CycleDiffusionPipeline
      A_						:    List[Any]							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'negative_prompt',
          'height',
          'width',
          'negative_prompt_embeds',
      }
      A_						:    List[str]							=   PipelineTesterMixin.required_optional_params - {'latents'}
      A_						:    List[str]							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
      A_						:    List[str]							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
      A_						:    Any							=   IMAGE_TO_IMAGE_IMAGE_PARAMS


      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       UNetaDConditionModel(
             block_out_channels=(32, 64)    ,      layers_per_block=2    ,      sample_size=32    ,      in_channels=4    ,      out_channels=4    ,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,      cross_attention_dim=32    ,      )
         __snake_case								=       DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      num_train_timesteps=1000    ,      clip_sample=a__    ,      set_alpha_to_one=a__    ,      )
         torch.manual_seed(0				)
         __snake_case								=       AutoencoderKL(
             block_out_channels=[32, 64]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=4    ,      )
         torch.manual_seed(0				)
         __snake_case								=       CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=32    ,      intermediate_size=37    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1000    ,      )
         __snake_case								=       CLIPTextModel(a__				)
         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         __snake_case								=       {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components


      def        a			(self	:				List[str]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image / 2 + 0.5
         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''prompt''': '''An astronaut riding an elephant''',
             '''source_prompt''': '''An astronaut riding a horse''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''eta''': 0.1,
             '''strength''': 0.8,
             '''guidance_scale''': 3,
             '''source_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case								=       self.get_dummy_components()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @unittest.skipIf(torch_device != '''cuda'''    ,      '''This test requires a GPU'''				)
      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.get_dummy_components()
         for name, module in components.items():
            if hasattr(a__    ,      '''half'''				):
               __snake_case								=       module.half()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_save_load_local()


      @unittest.skip('''non-deterministic pipeline'''				)
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_inference_batch_single_identical()


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def        a			(self	:				str				):





         """simple docstring"""



         return super().test_save_load_optional_components()


      @skip_mps
      def        a			(self	:				Dict				):





         """simple docstring"""



         return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(
             a__    ,      scheduler=a__    ,      safety_checker=a__    ,      torch_dtype=torch.floataa    ,      revision='''fp16'''				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image				).max() < 5E-1


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(a__    ,      scheduler=a__    ,      safety_checker=a__				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         assert np.abs(image - expected_image				).max() < 2E-2

