




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     image.size
  lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  lowerCAmelCase__				:  Dict		       =     image.resize((w, h)	,      resample=PIL_INTERPOLATION['''lanczos''']      )
  lowerCAmelCase__				:  Tuple		       =     np.array(__snake_case      ).astype(np.floataa      ) / 255.0
  lowerCAmelCase__				:  int		       =     image[None].transpose(0	,      3	,      1	,      2      )
  lowerCAmelCase__				:  Any		       =     torch.from_numpy(__snake_case      )
  return 2.0 * image - 1.0






class  _lowerCAmelCase     (  __lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				):
        super().__init__()
        self.register_modules(vqvae=_A      ,				unet=_A      ,				scheduler=_A		)







      @torch.no_grad()
      def __call__(   self      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = 1      ,				__UpperCAmelCase = 100      ,				__UpperCAmelCase = 0.0      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = "pil"      ,				__UpperCAmelCase = True      ,				):
        if isinstance(_A      ,				PIL.Image.Image		):
          lowerCAmelCase__				:  List[Any]		       =     1
        elif isinstance(_A      ,				torch.Tensor		):
          lowerCAmelCase__				:  Union[str, Any]		       =     image.shape[0]
        else:
          raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_A		)}"""		)

        if isinstance(_A      ,				PIL.Image.Image		):
          lowerCAmelCase__				:  Optional[int]		       =     preprocess(_A		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     image.shape[-2:]

        # in_channels should be 6: 3 for latents, 3 for low resolution image
        lowerCAmelCase__				:  int		       =     (batch_size, self.unet.config.in_channels // 2, height, width)
        lowerCAmelCase__				:  int		       =     next(self.unet.parameters()		).dtype

        lowerCAmelCase__				:  str		       =     randn_tensor(_A      ,				generator=_A      ,				device=self.device      ,				dtype=_A		)

        lowerCAmelCase__				:  str		       =     image.to(device=self.device      ,				dtype=_A		)

        # set timesteps and move to the correct device
        self.scheduler.set_timesteps(_A      ,				device=self.device		)
        lowerCAmelCase__				:  Optional[int]		       =     self.scheduler.timesteps

        # scale the initial noise by the standard deviation required by the scheduler
        lowerCAmelCase__				:  Optional[int]		       =     latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowerCAmelCase__				:  Optional[int]		       =     '''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
        lowerCAmelCase__				:  Union[str, Any]		       =     {}
        if accepts_eta:
          lowerCAmelCase__				:  List[Any]		       =     eta

        for t in self.progress_bar(_A		):
          # concat latents and low resolution image in the channel dimension.
          lowerCAmelCase__				:  str		       =     torch.cat([latents, image]      ,				dim=1		)
          lowerCAmelCase__				:  Optional[Any]		       =     self.scheduler.scale_model_input(_A      ,				_A		)
          # predict the noise residual
          lowerCAmelCase__				:  Union[str, Any]		       =     self.unet(_A      ,				_A		).sample
          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase__				:  str		       =     self.scheduler.step(_A      ,				_A      ,				_A      ,				**_A		).prev_sample

        # decode the image latents with the VQVAE
        lowerCAmelCase__				:  int		       =     self.vqvae.decode(_A		).sample
        lowerCAmelCase__				:  Optional[Any]		       =     torch.clamp(_A      ,				-1.0      ,				1.0		)
        lowerCAmelCase__				:  str		       =     image / 2 + 0.5
        lowerCAmelCase__				:  Dict		       =     image.cpu().permute(0      ,				2      ,				3      ,				1		).numpy()

        if output_type == "pil":
          lowerCAmelCase__				:  Dict		       =     self.numpy_to_pil(_A		)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=_A		)







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort






class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






@nightly
@require_onnxruntime
@require_torch_gpu
class  _lowerCAmelCase     (  unittest.TestCase	):

      @property
      def 	__magic_name__(   self		):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				scheduler=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3

