# 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 __snake_case				(						__UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase   ,		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 	__a						(    self			:				str      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_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       ,	)
     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      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__										=      DDIMScheduler(
         beta_start=0.0_00_85       ,	beta_end=0.0_12       ,	beta_schedule="""scaled_linear"""       ,	clip_sample=UpperCamelCase__       ,	set_alpha_to_one=UpperCamelCase__       ,	)
     torch.manual_seed(0      )
     SCREAMING_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      )
     SCREAMING_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=10_00       ,	)
     SCREAMING_SNAKE_CASE__										=      CLIPTextModel(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """controlnet""": controlnet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def 	__a						(    self			:				int       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[str]=0      ):



     """simple docstring"""






     if str(UpperCamelCase__      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(UpperCamelCase__      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=UpperCamelCase__      ).manual_seed(UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      2
     SCREAMING_SNAKE_CASE__										=      randn_tensor(
         (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,	generator=UpperCamelCase__       ,	device=torch.device(UpperCamelCase__      )       ,	)
     SCREAMING_SNAKE_CASE__										=      floats_tensor(control_image.shape       ,	rng=random.Random(UpperCamelCase__      )      ).to(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(UpperCamelCase__      )      ).convert("""RGB"""      ).resize((64, 64)      )
     SCREAMING_SNAKE_CASE__										=      {
         """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 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     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 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     self._test_inference_batch_single_identical(expected_max_diff=2E-3      )







class __snake_case				(						__UpperCAmelCase   ,		__UpperCAmelCase   ,		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 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_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       ,	)
     torch.manual_seed(0      )

     def init_weights(_lowercase			:				Union[str, Any]      ):
       if isinstance(UpperCamelCase__       ,	torch.nn.Convad      ):
         torch.nn.init.normal(m.weight      )
         m.bias.data.fill_(1.0      )

     SCREAMING_SNAKE_CASE__										=      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(UpperCamelCase__      )

     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      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(UpperCamelCase__      )

     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(
         beta_start=0.0_00_85       ,	beta_end=0.0_12       ,	beta_schedule="""scaled_linear"""       ,	clip_sample=UpperCamelCase__       ,	set_alpha_to_one=UpperCamelCase__       ,	)
     torch.manual_seed(0      )
     SCREAMING_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      )
     SCREAMING_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=10_00       ,	)
     SCREAMING_SNAKE_CASE__										=      CLIPTextModel(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

     SCREAMING_SNAKE_CASE__										=      MultiControlNetModel([controlneta, controlneta]      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """controlnet""": controlnet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def 	__a						(    self			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				int=0      ):



     """simple docstring"""






     if str(UpperCamelCase__      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(UpperCamelCase__      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=UpperCamelCase__      ).manual_seed(UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      2

     SCREAMING_SNAKE_CASE__										=      [
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,	generator=UpperCamelCase__       ,	device=torch.device(UpperCamelCase__      )       ,	),
         randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,	generator=UpperCamelCase__       ,	device=torch.device(UpperCamelCase__      )       ,	),
     ]

     SCREAMING_SNAKE_CASE__										=      floats_tensor(control_image[0].shape       ,	rng=random.Random(UpperCamelCase__      )      ).to(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(UpperCamelCase__      )      ).convert("""RGB"""      ).resize((64, 64)      )
     SCREAMING_SNAKE_CASE__										=      {
         """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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**UpperCamelCase__      )
     pipe.to(UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      10.0
     SCREAMING_SNAKE_CASE__										=      4

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      steps
     SCREAMING_SNAKE_CASE__										=      scale
     SCREAMING_SNAKE_CASE__										=      pipe(**UpperCamelCase__      )[0]

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      steps
     SCREAMING_SNAKE_CASE__										=      scale
     SCREAMING_SNAKE_CASE__										=      pipe(**UpperCamelCase__       ,	control_guidance_start=0.1       ,	control_guidance_end=0.2      )[0]

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      steps
     SCREAMING_SNAKE_CASE__										=      scale
     SCREAMING_SNAKE_CASE__										=      pipe(**UpperCamelCase__       ,	control_guidance_start=[0.1, 0.3]       ,	control_guidance_end=[0.2, 0.7]      )[0]

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      steps
     SCREAMING_SNAKE_CASE__										=      scale
     SCREAMING_SNAKE_CASE__										=      pipe(**UpperCamelCase__       ,	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 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     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 	__a						(    self			:				str      ):



     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     self._test_inference_batch_single_identical(expected_max_diff=2E-3      )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**UpperCamelCase__      )
     pipe.to(UpperCamelCase__      )
     pipe.set_progress_bar_config(disable=UpperCamelCase__      )
     with tempfile.TemporaryDirectory() as tmpdir:
       try:
         # save_pretrained is not implemented for Multi-ControlNet
         pipe.save_pretrained(UpperCamelCase__      )
       except NotImplementedError:
         pass







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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""      )

     SCREAMING_SNAKE_CASE__										=      StableDiffusionControlNetImgaImgPipeline.from_pretrained(
         """runwayml/stable-diffusion-v1-5"""       ,	safety_checker=UpperCamelCase__       ,	controlnet=UpperCamelCase__      )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=UpperCamelCase__      )

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cpu"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      """evil space-punk bird"""
     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""      ).resize((5_12, 5_12)      )
     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""      ).resize((5_12, 5_12)      )

     SCREAMING_SNAKE_CASE__										=      pipe(
         UpperCamelCase__       ,	UpperCamelCase__       ,	control_image=UpperCamelCase__       ,	generator=UpperCamelCase__       ,	output_type="""np"""       ,	num_inference_steps=50       ,	strength=0.6       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__										=      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 UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




