





"""simple docstring"""
import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 1
  lowerCAmelCase    				= 3
  lowerCAmelCase    				= (32, 32)

  lowerCAmelCase    				= floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0     )     ).to(_SCREAMING_SNAKE_CASE     )
  return image



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= 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				,       )
  return model



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= 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				,       )
  return model



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= 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				,       )
  return CLIPTextModel(_SCREAMING_SNAKE_CASE     )



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  def extract(*_snake_case				,       **_snake_case     ):







   class       a							:



    def __init__(     self     ):







     """simple docstring"""






     lowerCAmelCase    				= torch.ones([0]     )



    def    UpperCamelCase__  (     self				,       _snake_case     ):







     """simple docstring"""






     self.pixel_values.to(_SCREAMING_SNAKE_CASE     )
     return self


   return Out()

  return extract



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
  lowerCAmelCase    				= self.dummy_cond_unet
  lowerCAmelCase    				= DDIMScheduler(
      beta_start=0.00_085				,       beta_end=0.012				,       beta_schedule='scaled_linear'				,       clip_sample=_SCREAMING_SNAKE_CASE				,       set_alpha_to_one=_SCREAMING_SNAKE_CASE				,       )

  lowerCAmelCase    				= self.dummy_vae
  lowerCAmelCase    				= self.dummy_text_encoder
  lowerCAmelCase    				= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

  # make sure here that pndm scheduler skips prk
  lowerCAmelCase    				= StableDiffusionPipeline(
      unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE				,       vae=_SCREAMING_SNAKE_CASE				,       text_encoder=_SCREAMING_SNAKE_CASE				,       tokenizer=_SCREAMING_SNAKE_CASE				,       safety_checker=_SCREAMING_SNAKE_CASE				,       feature_extractor=self.dummy_extractor				,       )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= 'A painting of a squirrel eating a burger'

  lowerCAmelCase    				= torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
  lowerCAmelCase    				= sd_pipe([prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='np'     )
  lowerCAmelCase    				= output.images

  lowerCAmelCase    				= torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='np'				,       return_dict=_SCREAMING_SNAKE_CASE				,       )[0]

  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 64, 64, 3)
  lowerCAmelCase    				= np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]     )

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'  # ensure determinism for the device-dependent torch.Generator
  lowerCAmelCase    				= self.dummy_cond_unet
  lowerCAmelCase    				= PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= self.dummy_vae
  lowerCAmelCase    				= self.dummy_text_encoder
  lowerCAmelCase    				= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

  # make sure here that pndm scheduler skips prk
  lowerCAmelCase    				= StableDiffusionPipeline(
      unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE				,       vae=_SCREAMING_SNAKE_CASE				,       text_encoder=_SCREAMING_SNAKE_CASE				,       tokenizer=_SCREAMING_SNAKE_CASE				,       safety_checker=_SCREAMING_SNAKE_CASE				,       feature_extractor=self.dummy_extractor				,       )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= 'A painting of a squirrel eating a burger'
  lowerCAmelCase    				= torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
  lowerCAmelCase    				= sd_pipe([prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='np'     )

  lowerCAmelCase    				= output.images

  lowerCAmelCase    				= torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='np'				,       return_dict=_SCREAMING_SNAKE_CASE				,       )[0]

  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 64, 64, 3)
  lowerCAmelCase    				= np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]     )

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= StableDiffusionPipeline.from_pretrained(
      'hf-internal-testing/tiny-stable-diffusion-lms-pipe'				,       safety_checker=_SCREAMING_SNAKE_CASE     )
  assert isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE     )
  assert isinstance(pipe.scheduler				,       _SCREAMING_SNAKE_CASE     )
  assert pipe.safety_checker is None

  lowerCAmelCase    				= pipe('example prompt'				,       num_inference_steps=2     ).images[0]
  assert image is not None

  # check that there's no error when saving a pipeline with one of the models being None
  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE     )

  # sanity check that the pipeline still works
  assert pipe.safety_checker is None
  lowerCAmelCase    				= pipe('example prompt'				,       num_inference_steps=2     ).images[0]
  assert image is not None



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







  """simple docstring"""






  lowerCAmelCase    				= self.dummy_cond_unet
  lowerCAmelCase    				= PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= self.dummy_vae
  lowerCAmelCase    				= self.dummy_text_encoder
  lowerCAmelCase    				= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

  # put models in fp16
  lowerCAmelCase    				= unet.half()
  lowerCAmelCase    				= vae.half()
  lowerCAmelCase    				= bert.half()

  # make sure here that pndm scheduler skips prk
  lowerCAmelCase    				= StableDiffusionPipeline(
      unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE				,       vae=_SCREAMING_SNAKE_CASE				,       text_encoder=_SCREAMING_SNAKE_CASE				,       tokenizer=_SCREAMING_SNAKE_CASE				,       safety_checker=_SCREAMING_SNAKE_CASE				,       feature_extractor=self.dummy_extractor				,       )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= 'A painting of a squirrel eating a burger'
  lowerCAmelCase    				= sd_pipe([prompt]				,       num_inference_steps=2				,       output_type='np'     ).images

  assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'				,       safety_checker=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= (
      'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
      ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
      ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
      ' children from bahnhof zoo, detailed '
  )
  lowerCAmelCase    				= 40_03_66_03_46
  lowerCAmelCase    				= 7

  # without safety guidance (sld_guidance_scale = 0)
  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=0				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

  assert image.shape == (1, 5_12, 5_12, 3)

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

  # without safety guidance (strong configuration)
  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=20_00				,       sld_warmup_steps=7				,       sld_threshold=0.025				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

  assert image.shape == (1, 5_12, 5_12, 3)

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'				,       safety_checker=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= 'padme amidala taking a bath artwork, safe for work, no nudity'
  lowerCAmelCase    				= 27_34_97_17_55
  lowerCAmelCase    				= 7

  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=0				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

  assert image.shape == (1, 5_12, 5_12, 3)

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

  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=20_00				,       sld_warmup_steps=7				,       sld_threshold=0.025				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

  assert image.shape == (1, 5_12, 5_12, 3)

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     )
  lowerCAmelCase    				= sd_pipe.to(_SCREAMING_SNAKE_CASE     )
  sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase    				= (
      'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
      ' leyendecker'
  )
  lowerCAmelCase    				= 10_44_35_52_34
  lowerCAmelCase    				= 12

  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=0				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]     )

  assert image.shape == (1, 5_12, 5_12, 3)

  assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-7

  lowerCAmelCase    				= torch.manual_seed(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase    				= sd_pipe(
      [prompt]				,       generator=_SCREAMING_SNAKE_CASE				,       guidance_scale=_SCREAMING_SNAKE_CASE				,       num_inference_steps=50				,       output_type='np'				,       width=5_12				,       height=5_12				,       sld_guidance_scale=20_00				,       sld_warmup_steps=7				,       sld_threshold=0.025				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

  lowerCAmelCase    				= output.images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]     )
  assert image.shape == (1, 5_12, 5_12, 3)

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








'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (DDPMScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 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	):





      '''simple docstring'''

      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE	)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "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	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='`custom_timesteps` must be in descending order.'	):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
          scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE      , timesteps=_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _SCREAMING_SNAKE_CASE      , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      , ):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)





