

import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class SCREAMING_SNAKE_CASE     (  _lowerCamelCase       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=							ConsistencyModelPipeline
  lowerCamelCase   :      Any										=							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowerCamelCase   :      Optional[int]										=							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

  # Override required_optional_params to remove num_images_per_prompt
  lowerCamelCase   :      Optional[int]										=							frozenset(
      [
          "num_inference_steps",
          "generator",
          "latents",
          "output_type",
          "return_dict",
          "callback",
          "callback_steps",
      ]			)







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						UNetaDModel.from_pretrained(
         """diffusers/consistency-models-test"""	,							subfolder="""test_unet"""	,							)
     return unet







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						UNetaDModel.from_pretrained(
         """diffusers/consistency-models-test"""	,							subfolder="""test_unet_class_cond"""	,							)
     return unet







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   str=False   )							->     str:






     """simple docstring"""







     if class_cond:
        __lowerCAmelCase  :      str    =						self.dummy_cond_unet
     else:
        __lowerCAmelCase  :      str    =						self.dummy_uncond_unet

     # Default to CM multistep sampler
     __lowerCAmelCase  :      Dict    =						CMStochasticIterativeScheduler(
         num_train_timesteps=40	,							sigma_min=0.002	,							sigma_max=80.0	,							)

     __lowerCAmelCase  :      Any    =						{
         """unet""": unet,
         """scheduler""": scheduler,
     }

     return components







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   str=0   )							->     List[str]:






     """simple docstring"""







     if str(lowerCAmelCase   ).startswith("""mps"""   ):
        __lowerCAmelCase  :      Tuple    =						torch.manual_seed(lowerCAmelCase   )
     else:
        __lowerCAmelCase  :      List[Any]    =						torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						{
         """batch_size""": 1,
         """num_inference_steps""": None,
         """timesteps""": [22, 0],
         """generator""": generator,
         """output_type""": """np""",
     }

     return inputs







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






     """simple docstring"""







     __lowerCAmelCase  :      str    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      List[str]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Optional[int]    =						ConsistencyModelPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 32, 32, 3)

     __lowerCAmelCase  :      Tuple    =						image[0, -3:, -3:, -1]
     __lowerCAmelCase  :      Union[str, Any]    =						np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      Union[str, Any]    =						self.get_dummy_components(class_cond=lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConsistencyModelPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      Any    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 32, 32, 3)

     __lowerCAmelCase  :      Any    =						image[0, -3:, -3:, -1]
     __lowerCAmelCase  :      List[Any]    =						np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      Union[str, Any]    =						self.get_dummy_components()
     __lowerCAmelCase  :      Dict    =						ConsistencyModelPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						1
     __lowerCAmelCase  :      List[str]    =						None
     __lowerCAmelCase  :      Union[str, Any]    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 32, 32, 3)

     __lowerCAmelCase  :      Union[str, Any]    =						image[0, -3:, -3:, -1]
     __lowerCAmelCase  :      List[str]    =						np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase  :      int    =						self.get_dummy_components(class_cond=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						ConsistencyModelPipeline(**lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						pipe.to(lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						1
     __lowerCAmelCase  :      int    =						None
     __lowerCAmelCase  :      List[str]    =						0
     __lowerCAmelCase  :      Tuple    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 32, 32, 3)

     __lowerCAmelCase  :      str    =						image[0, -3:, -3:, -1]
     __lowerCAmelCase  :      List[str]    =						np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3






@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






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






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Any=0	,							lowerCAmelCase			:   Union[str, Any]=False	,							lowerCAmelCase			:   int="cpu"	,							lowerCAmelCase			:   List[Any]=torch.floataa	,							lowerCAmelCase			:   Union[str, Any]=(1, 3, 64, 64)   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						torch.manual_seed(lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						{
         """num_inference_steps""": None,
         """timesteps""": [22, 0],
         """class_labels""": 0,
         """generator""": generator,
         """output_type""": """np""",
     }

     if get_fixed_latents:
        __lowerCAmelCase  :      List[str]    =						self.get_fixed_latents(seed=lowerCAmelCase	,							device=lowerCAmelCase	,							dtype=lowerCAmelCase	,							shape=lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						latents

     return inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Dict=0	,							lowerCAmelCase			:   Union[str, Any]="cpu"	,							lowerCAmelCase			:   List[Any]=torch.floataa	,							lowerCAmelCase			:   Any=(1, 3, 64, 64)   )							->     Tuple:






     """simple docstring"""







     if type(lowerCAmelCase   ) == str:
        __lowerCAmelCase  :      List[Any]    =						torch.device(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						randn_tensor(lowerCAmelCase	,							generator=lowerCAmelCase	,							device=lowerCAmelCase	,							dtype=lowerCAmelCase   )
     return latents







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						UNetaDModel.from_pretrained("""diffusers/consistency_models"""	,							subfolder="""diffusers_cd_imagenet64_l2"""   )
     __lowerCAmelCase  :      List[str]    =						CMStochasticIterativeScheduler(
         num_train_timesteps=40	,							sigma_min=0.002	,							sigma_max=80.0	,							)
     __lowerCAmelCase  :      List[Any]    =						ConsistencyModelPipeline(unet=lowerCAmelCase	,							scheduler=lowerCAmelCase   )
     pipe.to(torch_device=lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						self.get_inputs()
     __lowerCAmelCase  :      Optional[Any]    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 64, 64, 3)

     __lowerCAmelCase  :      List[Any]    =						image[0, -3:, -3:, -1]

     __lowerCAmelCase  :      int    =						np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]   )

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







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						UNetaDModel.from_pretrained("""diffusers/consistency_models"""	,							subfolder="""diffusers_cd_imagenet64_l2"""   )
     __lowerCAmelCase  :      List[str]    =						CMStochasticIterativeScheduler(
         num_train_timesteps=40	,							sigma_min=0.002	,							sigma_max=80.0	,							)
     __lowerCAmelCase  :      Optional[Any]    =						ConsistencyModelPipeline(unet=lowerCAmelCase	,							scheduler=lowerCAmelCase   )
     pipe.to(torch_device=lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						self.get_inputs()
     __lowerCAmelCase  :      Optional[Any]    =						1
     __lowerCAmelCase  :      Dict    =						None
     __lowerCAmelCase  :      Any    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 64, 64, 3)

     __lowerCAmelCase  :      Tuple    =						image[0, -3:, -3:, -1]

     __lowerCAmelCase  :      Optional[Any]    =						np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]   )

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







  @require_torch_a
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						UNetaDModel.from_pretrained("""diffusers/consistency_models"""	,							subfolder="""diffusers_cd_imagenet64_l2"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						CMStochasticIterativeScheduler(
         num_train_timesteps=40	,							sigma_min=0.002	,							sigma_max=80.0	,							)
     __lowerCAmelCase  :      int    =						ConsistencyModelPipeline(unet=lowerCAmelCase	,							scheduler=lowerCAmelCase   )
     pipe.to(torch_device=lowerCAmelCase	,							torch_dtype=torch.floataa   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						self.get_inputs(get_fixed_latents=lowerCAmelCase	,							device=lowerCAmelCase   )
     # Ensure usage of flash attention in torch 2.0
     with sdp_kernel(enable_flash=lowerCAmelCase	,							enable_math=lowerCAmelCase	,							enable_mem_efficient=lowerCAmelCase   ):
        __lowerCAmelCase  :      Optional[int]    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 64, 64, 3)

     __lowerCAmelCase  :      Optional[Any]    =						image[0, -3:, -3:, -1]

     __lowerCAmelCase  :      Union[str, Any]    =						np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







  @require_torch_a
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						UNetaDModel.from_pretrained("""diffusers/consistency_models"""	,							subfolder="""diffusers_cd_imagenet64_l2"""   )
     __lowerCAmelCase  :      Tuple    =						CMStochasticIterativeScheduler(
         num_train_timesteps=40	,							sigma_min=0.002	,							sigma_max=80.0	,							)
     __lowerCAmelCase  :      List[str]    =						ConsistencyModelPipeline(unet=lowerCAmelCase	,							scheduler=lowerCAmelCase   )
     pipe.to(torch_device=lowerCAmelCase	,							torch_dtype=torch.floataa   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						self.get_inputs(get_fixed_latents=lowerCAmelCase	,							device=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						1
     __lowerCAmelCase  :      List[str]    =						None
     # Ensure usage of flash attention in torch 2.0
     with sdp_kernel(enable_flash=lowerCAmelCase	,							enable_math=lowerCAmelCase	,							enable_mem_efficient=lowerCAmelCase   ):
        __lowerCAmelCase  :      List[str]    =						pipe(**lowerCAmelCase   ).images
     assert image.shape == (1, 64, 64, 3)

     __lowerCAmelCase  :      Tuple    =						image[0, -3:, -3:, -1]

     __lowerCAmelCase  :      Dict    =						np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







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






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







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






     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

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

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

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

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

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

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


