


"""simple docstring"""


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


a_       							=    logging.get_logger(__name__)  # pylint: disable=invalid-name






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

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

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






def      a__ (				__lowercase   )	->	Optional[int]:


				if isinstance(__lowercase     ,			torch.Tensor   ):
								return mask
				elif isinstance(__lowercase     ,			PIL.Image.Image   ):
								_A					  =			[mask]

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




class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42







		def __init__(					self      :       Optional[int]    ,   a__      :       Tuple    ,   a__      :       Optional[int]		)       ->						Union[str, Any]:





						'''simple docstring'''







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







		@torch.no_grad()
		def __call__(					self      :       Tuple    ,   a__      :       int    ,   a__      :       Union[str, Any]    ,   a__      :       List[str] = 2_50    ,   a__      :       Any = 0.0    ,   a__      :       Optional[Any] = 10    ,   a__      :       List[str] = 10    ,   a__      :       Union[str, Any] = None    ,   a__      :       Dict = "pil"    ,   a__      :       Optional[Any] = True    ,   )       ->						Union[ImagePipelineOutput, Tuple]:





						'''simple docstring'''







						_A					  =			image

						_A					  =			_preprocess_image(a__		)
						_A					  =			original_image.to(device=self.device    ,   dtype=self.unet.dtype		)
						_A					  =			_preprocess_mask(a__		)
						_A					  =			mask_image.to(device=self.device    ,   dtype=self.unet.dtype		)

						_A					  =			original_image.shape[0]

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

						_A					  =			original_image.shape
						_A					  =			randn_tensor(a__    ,   generator=a__    ,   device=self.device    ,   dtype=self.unet.dtype		)

						# set step values
						self.scheduler.set_timesteps(a__    ,   a__    ,   a__    ,   self.device		)
						_A					  =			eta

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

										else:
														# compute the reverse: x_t-1 -> x_t
														_A					  =			self.scheduler.undo_step(a__    ,   a__    ,   a__		)
										_A					  =			t

						_A					  =			(image / 2 + 0.5).clamp(0    ,   1		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		).numpy()
						if output_type == "pil":
										_A					  =			self.numpy_to_pil(a__		)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=a__		)



"""simple docstring"""


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


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





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







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_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=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

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

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   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  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_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=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

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

						assert image.shape == (1, 5_12, 5_12, 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=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

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

						assert image.shape == (1, 5_12, 5_12, 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(a__    ,   generator=a__    ,   output_type="numpy"		).images

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

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