





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class      A       (	__lowercase						):



      def __init__(	self:						Optional[int]		,  _lowerCAmelCase:						UNetaDModel		,  _lowerCAmelCase:						UNetaDModel		,  _lowerCAmelCase:						DDPMScheduler		,  _lowerCAmelCase:						List[str]		,  )						->    int:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_											=value_function
         UpperCAmelCase_											=unet
         UpperCAmelCase_											=scheduler
         UpperCAmelCase_											=env
         UpperCAmelCase_											=env.get_dataset()
         UpperCAmelCase_											={}
         for key in self.data.keys():
            try:
               UpperCAmelCase_											=self.data[key].mean()
            except:  # noqa: E722
               pass
         UpperCAmelCase_											={}
         for key in self.data.keys():
            try:
               UpperCAmelCase_											=self.data[key].std()
            except:  # noqa: E722
               pass
         UpperCAmelCase_											=env.observation_space.shape[0]
         UpperCAmelCase_											=env.action_space.shape[0]



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[int]       )						->    Dict:
         '''simple docstring'''

         return (x_in - self.means[key]) / self.stds[key]



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         return x_in * self.stds[key] + self.means[key]



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[Any]       )						->    Dict:
         '''simple docstring'''

         if type(_lowerCAmelCase       ) is dict:
            return {k: self.to_torch(_lowerCAmelCase       ) for k, v in x_in.items()}
         elif torch.is_tensor(_lowerCAmelCase       ):
            return x_in.to(self.unet.device       )
         return torch.tensor(_lowerCAmelCase		,  device=self.unet.device       )



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Any       )						->    Any:
         '''simple docstring'''

         for key, val in cond.items():
            UpperCAmelCase_											=val.clone()
         return x_in



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=x.shape[0]
         UpperCAmelCase_											=None
         for i in tqdm.tqdm(self.scheduler.timesteps       ):
            # create batch of timesteps to pass into model
            UpperCAmelCase_											=torch.full((batch_size,)		,  _lowerCAmelCase		,  device=self.unet.device		,  dtype=torch.long       )
            for _ in range(_lowerCAmelCase       ):
               with torch.enable_grad():
                  x.requires_grad_()

                  # permute to match dimension for pre-trained models
                  UpperCAmelCase_											=self.value_function(x.permute(0		,  2		,  1       )		,  _lowerCAmelCase       ).sample
                  UpperCAmelCase_											=torch.autograd.grad([y.sum()]		,  [x]       )[0]

                  UpperCAmelCase_											=self.scheduler._get_variance(_lowerCAmelCase       )
                  UpperCAmelCase_											=torch.exp(0.5 * posterior_variance       )
                  UpperCAmelCase_											=model_std * grad

               UpperCAmelCase_											=0
               UpperCAmelCase_											=x.detach()
               UpperCAmelCase_											=x + scale * grad
               UpperCAmelCase_											=self.reset_xa(_lowerCAmelCase		,  _lowerCAmelCase		,  self.action_dim       )

            UpperCAmelCase_											=self.unet(x.permute(0		,  2		,  1       )		,  _lowerCAmelCase       ).sample.permute(0		,  2		,  1       )

            # TODO: verify deprecation of this kwarg
            UpperCAmelCase_											=self.scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  predict_epsilon=_lowerCAmelCase       )["prev_sample"]

            # apply conditions to the trajectory (set the initial state)
            UpperCAmelCase_											=self.reset_xa(_lowerCAmelCase		,  _lowerCAmelCase		,  self.action_dim       )
            UpperCAmelCase_											=self.to_torch(_lowerCAmelCase       )
         return x, y






      def __call__(	self:						int		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[Any]=64		,  _lowerCAmelCase:						int=32		,  _lowerCAmelCase:						Dict=2		,  _lowerCAmelCase:						Tuple=0.1       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.normalize(_lowerCAmelCase		,  "observations"       )
         UpperCAmelCase_											=obs[None].repeat(_lowerCAmelCase		,  axis=0       )

         UpperCAmelCase_											={0: self.to_torch(_lowerCAmelCase       )}
         UpperCAmelCase_											=(batch_size, planning_horizon, self.state_dim + self.action_dim)

         # generate initial noise and apply our conditions (to make the trajectories start at current state)
         UpperCAmelCase_											=randn_tensor(_lowerCAmelCase		,  device=self.unet.device       )
         UpperCAmelCase_											=self.reset_xa(_lowerCAmelCase		,  _lowerCAmelCase		,  self.action_dim       )
         UpperCAmelCase_											=self.to_torch(_lowerCAmelCase       )

         # run the diffusion process
         UpperCAmelCase_						,       UpperCAmelCase_											=self.run_diffusion(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         # sort output trajectories by value
         UpperCAmelCase_											=y.argsort(0		,  descending=_lowerCAmelCase       ).squeeze()
         UpperCAmelCase_											=x[sorted_idx]
         UpperCAmelCase_											=sorted_values[:, :, : self.action_dim]
         UpperCAmelCase_											=actions.detach().cpu().numpy()
         UpperCAmelCase_											=self.de_normalize(_lowerCAmelCase		,  key="actions"       )

         # select the action with the highest value
         if y is not None:
            UpperCAmelCase_											=0
         else:
            # if we didn't run value guiding, select a random action
            UpperCAmelCase_											=np.random.randint(0		,  _lowerCAmelCase       )

         UpperCAmelCase_											=denorm_actions[selected_index, 0]
         return denorm_actions











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 	lowerCAmelCase__    (	self:						str       )						->    Optional[Any]:
         '''simple docstring'''

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



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=1
         UpperCAmelCase_											=3
         UpperCAmelCase_											=(32, 32)

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



      @property
      def 	lowerCAmelCase__    (	self:						Any       )						->    Union[str, Any]:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[Any]       )						->    int:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Dict       )						->    str:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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=1000		,  )
         return CLIPTextModel(_lowerCAmelCase       )



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         def extract(*_lowerCAmelCase:						List[str]		,  **_lowerCAmelCase:						List[str]       ):

            class      A       :



                  def __init__(	self:						List[str]       )						->    Any:
                     '''simple docstring'''

                     UpperCAmelCase_											=torch.ones([0]       )






                  def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Dict       )						->    List[Any]:
                     '''simple docstring'''

                     self.pixel_values.to(_lowerCAmelCase       )
                     return self

            return Out()

         return extract



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_											=self.dummy_cond_unet
         UpperCAmelCase_											=DDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  clip_sample=_lowerCAmelCase		,  set_alpha_to_one=_lowerCAmelCase		,  )

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

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

         UpperCAmelCase_											="A painting of a squirrel eating a burger"

         UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(0       )
         UpperCAmelCase_											=sd_pipe([prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=6.0		,  num_inference_steps=2		,  output_type="np"       )
         UpperCAmelCase_											=output.images

         UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(0       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=6.0		,  num_inference_steps=2		,  output_type="np"		,  return_dict=_lowerCAmelCase		,  )[0]

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

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_											=np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]       )

         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 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[Any]:
         '''simple docstring'''

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

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

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

         UpperCAmelCase_											=output.images

         UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(0       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=6.0		,  num_inference_steps=2		,  output_type="np"		,  return_dict=_lowerCAmelCase		,  )[0]

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

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_											=np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]       )

         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 	lowerCAmelCase__    (	self:						List[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-lms-pipe"		,  safety_checker=_lowerCAmelCase       )
         assert isinstance(_lowerCAmelCase		,  _lowerCAmelCase       )
         assert isinstance(pipe.scheduler		,  _lowerCAmelCase       )
         assert pipe.safety_checker is None

         UpperCAmelCase_											=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(_lowerCAmelCase       )
            UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained(_lowerCAmelCase       )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.dummy_cond_unet
         UpperCAmelCase_											=PNDMScheduler(skip_prk_steps=_lowerCAmelCase       )
         UpperCAmelCase_											=self.dummy_vae
         UpperCAmelCase_											=self.dummy_text_encoder
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

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

         UpperCAmelCase_											="A painting of a squirrel eating a burger"
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[Any]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,  safety_checker=_lowerCAmelCase       )
         UpperCAmelCase_											=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=(
             "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 "
         )
         UpperCAmelCase_											=40_0366_0346
         UpperCAmelCase_											=7

         # without safety guidance (sld_guidance_scale = 0)
         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

         assert image.shape == (1, 512, 512, 3)

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

         # without safety guidance (strong configuration)
         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=2000		,  sld_warmup_steps=7		,  sld_threshold=0.0_25		,  sld_momentum_scale=0.5		,  sld_mom_beta=0.7		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

         assert image.shape == (1, 512, 512, 3)

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



      def 	lowerCAmelCase__    (	self:						Any       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,  safety_checker=_lowerCAmelCase       )
         UpperCAmelCase_											=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											="padme amidala taking a bath artwork, safe for work, no nudity"
         UpperCAmelCase_											=27_3497_1755
         UpperCAmelCase_											=7

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

         assert image.shape == (1, 512, 512, 3)

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

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=2000		,  sld_warmup_steps=7		,  sld_threshold=0.0_25		,  sld_momentum_scale=0.5		,  sld_mom_beta=0.7		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

         assert image.shape == (1, 512, 512, 3)

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






      def 	lowerCAmelCase__    (	self:						str       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=(
             "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
             " leyendecker"
         )
         UpperCAmelCase_											=10_4435_5234
         UpperCAmelCase_											=12

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

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

         assert image.shape == (1, 512, 512, 3)

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

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=2000		,  sld_warmup_steps=7		,  sld_threshold=0.0_25		,  sld_momentum_scale=0.5		,  sld_mom_beta=0.7		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]       )
         assert image.shape == (1, 512, 512, 3)

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




