






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class       __a  (   A__	):
 _lowerCAmelCase	:	UNetaDModel
 _lowerCAmelCase	:	ScoreSdeVeScheduler







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       UNetaDModel							,       SCREAMING_SNAKE_CASE :       ScoreSdeVeScheduler						):


      '''simple docstring'''






      super().__init__()
      self.register_modules(unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE						)







 @torch.no_grad()
 def __call__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       int = 1							,       SCREAMING_SNAKE_CASE :       int = 20_00							,       SCREAMING_SNAKE_CASE :       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,       SCREAMING_SNAKE_CASE :       Optional[str] = "pil"							,       SCREAMING_SNAKE_CASE :       bool = True							,       **SCREAMING_SNAKE_CASE :       Tuple							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  self.unet.config.sample_size
      UpperCamelCase__      :       List[str]	    =  (batch_size, 3, img_size, img_size)

      UpperCamelCase__      :       int	    =  self.unet

      UpperCamelCase__      :       int	    =  randn_tensor(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE						) * self.scheduler.init_noise_sigma
      UpperCamelCase__      :       List[Any]	    =  sample.to(self.device						)

      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
      self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
           UpperCamelCase__      :       Tuple	    =  self.scheduler.sigmas[i] * torch.ones(shape[0]							,       device=self.device						)

           # correction step
           for _ in range(self.scheduler.config.correct_steps						):
                UpperCamelCase__      :       Dict	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).sample
                UpperCamelCase__      :       str	    =  self.scheduler.step_correct(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE						).prev_sample

           # prediction step
           UpperCamelCase__      :       Tuple	    =  model(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						).sample
           UpperCamelCase__      :       str	    =  self.scheduler.step_pred(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE						)

           UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  output.prev_sample, output.prev_sample_mean

      UpperCamelCase__      :       Union[str, Any]	    =  sample_mean.clamp(0							,       1						)
      UpperCamelCase__      :       Optional[int]	    =  sample.cpu().permute(0							,       2							,       3							,       1						).numpy()
      if output_type == "pil":
           UpperCamelCase__      :       int	    =  self.numpy_to_pil(SCREAMING_SNAKE_CASE						)

      if not return_dict:
           return (sample,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE						)







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase     :							Optional[Any]    =False





class       __a  (   unittest.TestCase	):
 pass







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







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

      UpperCamelCase__      :       Dict	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       str	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Tuple	    =  VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  generator.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  "cyberpunk 2077"
      UpperCamelCase__      :       Any	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__      :       int	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  pipe.dual_guided(
          prompt=SCREAMING_SNAKE_CASE							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"							,       ).images

      UpperCamelCase__      :       Tuple	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Optional[int]	    =  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

      UpperCamelCase__      :       List[str]	    =  "A painting of a squirrel eating a burger "
      UpperCamelCase__      :       List[Any]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.text_to_image(
          prompt=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"						).images

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Dict	    =  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

      UpperCamelCase__      :       Any	    =  pipe.image_variation(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       output_type="numpy"						).images

      UpperCamelCase__      :       Optional[Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       int	    =  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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