

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowercase     : Optional[Any]       =						logging.get_logger(__name__)  # pylint: disable=invalid-name







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Any:





    '''simple docstring'''




    warnings.warn(
        """The preprocess method is deprecated and will be removed in a future version. Please"""
        """ use VaeImageProcessor.preprocess instead""" ,    __A ,    )
    if isinstance(__A ,    torch.Tensor		):
        return image
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		Optional[int]        =		[image]

    if isinstance(image[0] ,    PIL.Image.Image		):
        snake_case      ,		snake_case							:		Optional[Any]        =		image[0].size
        snake_case      ,		snake_case							:		Union[str, Any]        =		(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        snake_case							:		List[str]        =		[np.array(i.resize((w, h) ,    resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
        snake_case							:		Tuple        =		np.concatenate(__A ,    axis=0		)
        snake_case							:		str        =		np.array(__A		).astype(np.floataa		) / 255.0
        snake_case							:		Any        =		image.transpose(0 ,    3 ,    1 ,    2		)
        snake_case							:		List[str]        =		2.0 * image - 1.0
        snake_case							:		int        =		torch.from_numpy(__A		)
    elif isinstance(image[0] ,    torch.Tensor		):
        snake_case							:		Tuple        =		torch.cat(__A ,    dim=0		)
    return image







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Optional[int]:





    '''simple docstring'''




    if isinstance(__A ,    torch.Tensor		):
        return mask
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		int        =		[mask]

    if isinstance(mask[0] ,    PIL.Image.Image		):
        snake_case      ,		snake_case							:		List[str]        =		mask[0].size
        snake_case      ,		snake_case							:		Any        =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        snake_case							:		Optional[int]        =		[np.array(m.convert("""L"""		).resize((w, h) ,    resample=PIL_INTERPOLATION["""nearest"""]		)		)[None, :] for m in mask]
        snake_case							:		List[Any]        =		np.concatenate(__A ,    axis=0		)
        snake_case							:		Optional[Any]        =		mask.astype(np.floataa		) / 255.0
        snake_case							:		Dict        =		0
        snake_case							:		List[Any]        =		1
        snake_case							:		Dict        =		torch.from_numpy(__A		)
    elif isinstance(mask[0] ,    torch.Tensor		):
        snake_case							:		List[Any]        =		torch.cat(__A ,    dim=0		)
    return mask



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      UNetaDModel
  __lowerCamelCase			:      RePaintScheduler





  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      self.register_modules(unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_    )





  @torch.no_grad()
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = 250   ,SCREAMING_SNAKE_CASE_ = 0.0   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "pil"   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		image

      snake_case							:		int        =		_preprocess_image(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		original_image.to(device=self.device   ,dtype=self.unet.dtype    )
      snake_case							:		int        =		_preprocess_mask(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		mask_image.to(device=self.device   ,dtype=self.unet.dtype    )

      snake_case							:		Optional[int]        =		original_image.shape[0]

      # sample gaussian noise to begin the loop
      if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) and len(SCREAMING_SNAKE_CASE_    ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_    )}, but requested an effective batch"""
              F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

      snake_case							:		int        =		original_image.shape
      snake_case							:		str        =		randn_tensor(SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device=self.device   ,dtype=self.unet.dtype    )

      # set step values
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,self.device    )
      snake_case							:		int        =		eta

      snake_case							:		Any        =		self.scheduler.timesteps[0] + 1
      snake_case							:		int        =		generator[0] if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) else generator
      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
          if t < t_last:
              # predict the noise residual
              snake_case							:		List[Any]        =		self.unet(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).sample
              # compute previous image: x_t -> x_t-1
              snake_case							:		Tuple        =		self.scheduler.step(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).prev_sample

          else:
              # compute the reverse: x_t-1 -> x_t
              snake_case							:		Optional[Any]        =		self.scheduler.undo_step(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
          snake_case							:		Union[str, Any]        =		t

      snake_case							:		Any        =		(image / 2 + 0.5).clamp(0   ,1    )
      snake_case							:		Optional[Any]        =		image.cpu().permute(0   ,2   ,3   ,1    ).numpy()
      if output_type == "pil":
          snake_case							:		Optional[int]        =		self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

      if not return_dict:
          return (image,)

      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


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





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


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

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

											A      						=    generator.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, 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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

											A      						=    image[0, 253:256, 253:256, -1]

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

											A      						=    'A painting of a squirrel eating a burger '
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.text_to_image(
											    prompt=__UpperCamelCase						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

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

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

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