
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class 			lowercase_		(      A  ,      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						StableDiffusionControlNetImgaImgPipeline
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __lowerCamelCase				       =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}       )
       __lowerCamelCase				       =						IMAGE_TO_IMAGE_IMAGE_PARAMS


       def 	_snake_case				(    self				)						->    Optional[Any]:
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=ControlNetModel(
                  block_out_channels=(32, 64)						,       layers_per_block=2						,       in_channels=4						,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,       cross_attention_dim=32						,       conditioning_embedding_out_channels=(16, 32)						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				str		=DDIMScheduler(
                  beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='''scaled_linear'''						,       clip_sample=__A						,       set_alpha_to_one=__A						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=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				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=CLIPTextConfig(
                  bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1e-0_5						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1_000						,       )
              SCREAMING_SNAKE_CASE_							:				List[str]		=CLIPTextModel(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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


       def 	_snake_case				(    self						,       __A						,       __A=0				)						->    int:
              if str(__A				).startswith('''mps'''				):
                     SCREAMING_SNAKE_CASE_							:				Any		=torch.manual_seed(__A				)
              else:
                     SCREAMING_SNAKE_CASE_							:				int		=torch.Generator(device=__A				).manual_seed(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=2
              SCREAMING_SNAKE_CASE_							:				List[str]		=randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,       generator=__A						,       device=torch.device(__A				)						,       )
              SCREAMING_SNAKE_CASE_							:				str		=floats_tensor(control_image.shape						,       rng=random.Random(__A				)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=image.cpu().permute(0						,       2						,       3						,       1				)[0]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=Image.fromarray(np.uinta(__A				)				).convert('''RGB'''				).resize((64, 64)				)
              SCREAMING_SNAKE_CASE_							:				str		={
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''guidance_scale''': 6.0,
                  '''output_type''': '''numpy''',
                  '''image''': image,
                  '''control_image''': control_image,
              }

              return inputs


       def 	_snake_case				(    self				)						->    int:
              return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)


       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()						,       reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,       )
       def 	_snake_case				(    self				)						->    List[str]:
              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)





       def 	_snake_case				(    self				)						->    str:
              self._test_inference_batch_single_identical(expected_max_diff=2e-3				)






class 			lowercase_		(      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						StableDiffusionControlNetImgaImgPipeline
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __lowerCamelCase				       =						frozenset([]       )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


       def 	_snake_case				(    self				)						->    Any:
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Any		=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						,       )
              torch.manual_seed(0				)

              def init_weights(__A				):
                     if isinstance(__A						,       torch.nn.Convad				):
                            torch.nn.init.normal(m.weight				)
                            m.bias.data.fill_(1.0				)

              SCREAMING_SNAKE_CASE_							:				Dict		=ControlNetModel(
                  block_out_channels=(32, 64)						,       layers_per_block=2						,       in_channels=4						,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,       cross_attention_dim=32						,       conditioning_embedding_out_channels=(16, 32)						,       )
              controlneta.controlnet_down_blocks.apply(__A				)

              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=ControlNetModel(
                  block_out_channels=(32, 64)						,       layers_per_block=2						,       in_channels=4						,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,       cross_attention_dim=32						,       conditioning_embedding_out_channels=(16, 32)						,       )
              controlneta.controlnet_down_blocks.apply(__A				)

              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=DDIMScheduler(
                  beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='''scaled_linear'''						,       clip_sample=__A						,       set_alpha_to_one=__A						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=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				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=CLIPTextConfig(
                  bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1e-0_5						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1_000						,       )
              SCREAMING_SNAKE_CASE_							:				List[str]		=CLIPTextModel(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=MultiControlNetModel([controlneta, controlneta]				)

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


       def 	_snake_case				(    self						,       __A						,       __A=0				)						->    Optional[int]:
              if str(__A				).startswith('''mps'''				):
                     SCREAMING_SNAKE_CASE_							:				Dict		=torch.manual_seed(__A				)
              else:
                     SCREAMING_SNAKE_CASE_							:				int		=torch.Generator(device=__A				).manual_seed(__A				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=2

              SCREAMING_SNAKE_CASE_							:				int		=[
                  randn_tensor(
                      (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,       generator=__A						,       device=torch.device(__A				)						,       ),
                  randn_tensor(
                      (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,       generator=__A						,       device=torch.device(__A				)						,       ),
              ]

              SCREAMING_SNAKE_CASE_							:				str		=floats_tensor(control_image[0].shape						,       rng=random.Random(__A				)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=image.cpu().permute(0						,       2						,       3						,       1				)[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=Image.fromarray(np.uinta(__A				)				).convert('''RGB'''				).resize((64, 64)				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''guidance_scale''': 6.0,
                  '''output_type''': '''numpy''',
                  '''image''': image,
                  '''control_image''': control_image,
              }

              return inputs


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				str		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				Dict		=self.pipeline_class(**__A				)
              pipe.to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10.0
              SCREAMING_SNAKE_CASE_							:				str		=4

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_inputs(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=steps
              SCREAMING_SNAKE_CASE_							:				Tuple		=scale
              SCREAMING_SNAKE_CASE_							:				List[str]		=pipe(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=self.get_dummy_inputs(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=steps
              SCREAMING_SNAKE_CASE_							:				Any		=scale
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=pipe(**__A						,       control_guidance_start=0.1						,       control_guidance_end=0.2				)[0]

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_dummy_inputs(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=steps
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scale
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=pipe(**__A						,       control_guidance_start=[0.1, 0.3]						,       control_guidance_end=[0.2, 0.7]				)[0]

              SCREAMING_SNAKE_CASE_							:				int		=self.get_dummy_inputs(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=steps
              SCREAMING_SNAKE_CASE_							:				Any		=scale
              SCREAMING_SNAKE_CASE_							:				Tuple		=pipe(**__A						,       control_guidance_start=0.4						,       control_guidance_end=[0.5, 0.8]				)[0]

              # make sure that all outputs are different
              assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
              assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
              assert np.sum(np.abs(output_a - output_a				)				) > 1e-3


       def 	_snake_case				(    self				)						->    List[Any]:
              return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)


       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()						,       reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,       )
       def 	_snake_case				(    self				)						->    str:
              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)


       def 	_snake_case				(    self				)						->    Any:
              self._test_inference_batch_single_identical(expected_max_diff=2e-3				)





       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.pipeline_class(**__A				)
              pipe.to(__A				)
              pipe.set_progress_bar_config(disable=__A				)
              with tempfile.TemporaryDirectory() as tmpdir:
                     try:
                            # save_pretrained is not implemented for Multi-ControlNet
                            pipe.save_pretrained(__A				)
                     except NotImplementedError:
                            pass






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


       def 	_snake_case				(    self				)						->    Any:
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Tuple		=ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=StableDiffusionControlNetImgaImgPipeline.from_pretrained(
                  '''runwayml/stable-diffusion-v1-5'''						,       safety_checker=__A						,       controlnet=__A				)
              pipe.enable_model_cpu_offload()
              pipe.set_progress_bar_config(disable=__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.Generator(device='''cpu'''				).manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				str		='''evil space-punk bird'''
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''				).resize((512, 512)				)
              SCREAMING_SNAKE_CASE_							:				int		=load_image(
                  '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''				).resize((512, 512)				)

              SCREAMING_SNAKE_CASE_							:				Dict		=pipe(
                  __A						,       __A						,       control_image=__A						,       generator=__A						,       output_type='''np'''						,       num_inference_steps=50						,       strength=0.6						,       )

              SCREAMING_SNAKE_CASE_							:				Dict		=output.images[0]

              assert image.shape == (512, 512, 3)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''				)

              assert np.abs(expected_image - image				).max() < 9e-2


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self				)						->    Any:
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A						,       use_karras_sigmas=__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
