


# 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 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  int       =		StableDiffusionControlNetImgaImgPipeline
      snake_case__       :  str       =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
      snake_case__       :  List[Any]       =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      snake_case__       :  List[Any]       =		IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}    )
      snake_case__       :  Any       =		IMAGE_TO_IMAGE_IMAGE_PARAMS
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Dict:
             torch.manual_seed(0  )
             a_      :	Optional[Any]							=						UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							sample_size=3_2  ,							in_channels=4  ,							out_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,							cross_attention_dim=3_2  ,							)
             torch.manual_seed(0  )
             a_      :	Dict							=						ControlNetModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							in_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							cross_attention_dim=3_2  ,							conditioning_embedding_out_channels=(1_6, 3_2)  ,							)
             torch.manual_seed(0  )
             a_      :	List[Any]							=						DDIMScheduler(
                 beta_start=0.00085  ,							beta_end=0.012  ,							beta_schedule='scaled_linear'  ,							clip_sample=SCREAMING_SNAKE_CASE__  ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							)
             torch.manual_seed(0  )
             a_      :	Dict							=						AutoencoderKL(
                 block_out_channels=[3_2, 6_4]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,							latent_channels=4  ,							)
             torch.manual_seed(0  )
             a_      :	Dict							=						CLIPTextConfig(
                 bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=3_2  ,							intermediate_size=3_7  ,							layer_norm_eps=1E-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_0_0_0  ,							)
             a_      :	int							=						CLIPTextModel(SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

             a_      :	Optional[Any]							=						{
                 'unet': unet,
                 'controlnet': controlnet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }
             return components
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Any=0  )	->    Any:
             if str(SCREAMING_SNAKE_CASE__  ).startswith('mps'  ):
                    a_      :	List[Any]							=						torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             else:
                    a_      :	int							=						torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						2
             a_      :	List[Any]							=						randn_tensor(
                 (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=torch.device(SCREAMING_SNAKE_CASE__  )  ,							)
             a_      :	Union[str, Any]							=						floats_tensor(control_image.shape  ,							rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						image.cpu().permute(0  ,							2  ,							3  ,							1  )[0]
             a_      :	Union[str, Any]							=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__  )  ).convert('RGB'  ).resize((6_4, 6_4)  )
             a_      :	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  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             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  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[Any]:
             self._test_inference_batch_single_identical(expected_max_diff=2E-3  )
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  int       =		StableDiffusionControlNetImgaImgPipeline
      snake_case__       :  Tuple       =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
      snake_case__       :  Optional[int]       =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      snake_case__       :  str       =		frozenset([]    )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             torch.manual_seed(0  )
             a_      :	Tuple							=						UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							sample_size=3_2  ,							in_channels=4  ,							out_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,							cross_attention_dim=3_2  ,							)
             torch.manual_seed(0  )

             def init_weights(SCREAMING_SNAKE_CASE__       : Optional[Any]  ):
                    if isinstance(SCREAMING_SNAKE_CASE__  ,							torch.nn.Convad  ):
                           torch.nn.init.normal(m.weight  )
                           m.bias.data.fill_(1.0  )

             a_      :	int							=						ControlNetModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							in_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							cross_attention_dim=3_2  ,							conditioning_embedding_out_channels=(1_6, 3_2)  ,							)
             controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE__  )

             torch.manual_seed(0  )
             a_      :	Dict							=						ControlNetModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							in_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							cross_attention_dim=3_2  ,							conditioning_embedding_out_channels=(1_6, 3_2)  ,							)
             controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE__  )

             torch.manual_seed(0  )
             a_      :	Optional[int]							=						DDIMScheduler(
                 beta_start=0.00085  ,							beta_end=0.012  ,							beta_schedule='scaled_linear'  ,							clip_sample=SCREAMING_SNAKE_CASE__  ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							)
             torch.manual_seed(0  )
             a_      :	List[Any]							=						AutoencoderKL(
                 block_out_channels=[3_2, 6_4]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,							latent_channels=4  ,							)
             torch.manual_seed(0  )
             a_      :	Dict							=						CLIPTextConfig(
                 bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=3_2  ,							intermediate_size=3_7  ,							layer_norm_eps=1E-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_0_0_0  ,							)
             a_      :	List[Any]							=						CLIPTextModel(SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

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

             a_      :	Any							=						{
                 'unet': unet,
                 'controlnet': controlnet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }
             return components
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  )	->    Optional[Any]:
             if str(SCREAMING_SNAKE_CASE__  ).startswith('mps'  ):
                    a_      :	List[str]							=						torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             else:
                    a_      :	Optional[Any]							=						torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )

             a_      :	Any							=						2

             a_      :	Any							=						[
                 randn_tensor(
                     (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=torch.device(SCREAMING_SNAKE_CASE__  )  ,							),
                 randn_tensor(
                     (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=torch.device(SCREAMING_SNAKE_CASE__  )  ,							),
             ]

             a_      :	Dict							=						floats_tensor(control_image[0].shape  ,							rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						image.cpu().permute(0  ,							2  ,							3  ,							1  )[0]
             a_      :	int							=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__  )  ).convert('RGB'  ).resize((6_4, 6_4)  )
             a_      :	Optional[int]							=						{
                 '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  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    int:
             a_      :	Tuple							=						self.get_dummy_components()
             a_      :	List[Any]							=						self.pipeline_class(**SCREAMING_SNAKE_CASE__  )
             pipe.to(SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						10.0
             a_      :	str							=						4

             a_      :	Any							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						steps
             a_      :	Union[str, Any]							=						scale
             a_      :	List[Any]							=						pipe(**SCREAMING_SNAKE_CASE__  )[0]

             a_      :	Dict							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						steps
             a_      :	List[Any]							=						scale
             a_      :	Tuple							=						pipe(**SCREAMING_SNAKE_CASE__  ,							control_guidance_start=0.1  ,							control_guidance_end=0.2  )[0]

             a_      :	Optional[Any]							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						steps
             a_      :	str							=						scale
             a_      :	str							=						pipe(**SCREAMING_SNAKE_CASE__  ,							control_guidance_start=[0.1, 0.3]  ,							control_guidance_end=[0.2, 0.7]  )[0]

             a_      :	List[Any]							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						steps
             a_      :	int							=						scale
             a_      :	int							=						pipe(**SCREAMING_SNAKE_CASE__  ,							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  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    str:
             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  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[Any]:
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Optional[Any]:
             self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    str:
             a_      :	Optional[int]							=						self.get_dummy_components()
             a_      :	Tuple							=						self.pipeline_class(**SCREAMING_SNAKE_CASE__  )
             pipe.to(SCREAMING_SNAKE_CASE__  )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )
             with tempfile.TemporaryDirectory() as tmpdir:
                    try:
                           # save_pretrained is not implemented for Multi-ControlNet
                           pipe.save_pretrained(SCREAMING_SNAKE_CASE__  )
                    except NotImplementedError:
                           pass
@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[Any]:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             a_      :	int							=						ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

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

             a_      :	List[Any]							=						torch.Generator(device='cpu'  ).manual_seed(0  )
             a_      :	int							=						'evil space-punk bird'
             a_      :	Union[str, Any]							=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((5_1_2, 5_1_2)  )
             a_      :	int							=						load_image(
                 'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((5_1_2, 5_1_2)  )

             a_      :	int							=						pipe(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							control_image=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							output_type='np'  ,							num_inference_steps=5_0  ,							strength=0.6  ,							)

             a_      :	int							=						output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             a_      :	Optional[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 DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 0.32460  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

             a_      :	List[Any]							=						samplea.shape[0]
             a_      :	List[str]							=						torch.stack([samplea, samplea, samplea]  ,							dim=0  )
             a_      :	Union[str, Any]							=						torch.arange(SCREAMING_SNAKE_CASE__  )[0:3, None].repeat(1  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 1147.7904  ) < 1E-2
             assert abs(result_mean.item() - 0.4982  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 172.0067  ) < 1E-2
             assert abs(result_mean.item() - 0.223967  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 52.5302  ) < 1E-2
             assert abs(result_mean.item() - 0.0684  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.8295  ) < 1E-2
             assert abs(result_mean.item() - 0.1951  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.0784  ) < 1E-2
             assert abs(result_mean.item() - 0.1941  ) < 1E-3


