



"""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


lowerCamelCase       	=						logging.get_logger(__name__)  # pylint: disable=invalid-name
def        a__     (				lowerCAmelCase__				):
      warnings.warn(
          "The preprocess method is deprecated and will be removed in a future version. Please"
          " use VaeImageProcessor.preprocess instead"     ,      UpperCAmelCase_     ,      )
      if isinstance(UpperCAmelCase_     ,      torch.Tensor				):
            return image
      elif isinstance(UpperCAmelCase_     ,      PIL.Image.Image				):
            UpperCAmelCase_      			=							[image]

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

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





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

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


class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					42



       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




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







       @torch.no_grad()
       def __call__(							self      :  List[str]		,    _UpperCAmelCase      :  Union[torch.Tensor, PIL.Image.Image]		,    _UpperCAmelCase      :  Union[torch.Tensor, PIL.Image.Image]		,    _UpperCAmelCase      :  int = 250		,    _UpperCAmelCase      :  float = 0.0		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  Optional[Union[torch.Generator, List[torch.Generator]]] = None		,    _UpperCAmelCase      :  Optional[str] = "pil"		,    _UpperCAmelCase      :  bool = True		,    )			->						Union[ImagePipelineOutput, Tuple]:


             '''simple docstring'''




             UpperCAmelCase_      			=							image

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

             UpperCAmelCase_      			=							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."""   )

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

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

             UpperCAmelCase_      			=							self.scheduler.timesteps[0] + 1
             UpperCAmelCase_      			=							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
                         UpperCAmelCase_      			=							self.unet(_A		,    _A   ).sample
                         # compute previous image: x_t -> x_t-1
                         UpperCAmelCase_      			=							self.scheduler.step(_A		,    _A		,    _A		,    _A		,    _A		,    _A   ).prev_sample

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

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=_A   )









"""simple docstring"""





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"   )

             # using the PNDM scheduler by default
             UpperCAmelCase_      			=							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                 "CompVis/stable-diffusion-v1-4"		,    revision="onnx"		,    safety_checker=_UpperCAmelCase		,    feature_extractor=_UpperCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image   ).max() < 1e-2




