





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def   a_   (			*lowerCAmelCase_				:      Union[str, Any]     ):
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      __lowerCAmelCase							     =     list(lowerCAmelCase_     )
   for i in range(len(lowerCAmelCase_     )     ):
      __lowerCAmelCase							     =     None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects

def   a_   (			lowerCAmelCase_				:      Exception     ):
   __lowerCAmelCase							     =     [
       'CUDA out of memory.',  # CUDA OOM
       'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
       'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
   ]
   if isinstance(lowerCAmelCase_,     lowerCAmelCase_     ) and len(exception.args     ) == 1:
      return any(err in exception.args[0] for err in _statements     )
   return False




def   a_   (			lowerCAmelCase_				:      callable = None,     lowerCAmelCase_				:      int = 128     ):
   if function is None:
      return functools.partial(lowerCAmelCase_,     starting_batch_size=lowerCAmelCase_     )

   __lowerCAmelCase							     =     starting_batch_size

   def decorator(*lowerCAmelCase_				:      Dict,     **lowerCAmelCase_				:      Dict     ):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      __lowerCAmelCase							     =     list(inspect.signature(lowerCAmelCase_     ).parameters.keys()     )
      # Guard against user error
      if len(lowerCAmelCase_     ) < (len(lowerCAmelCase_     ) + 1):
         __lowerCAmelCase							     =     ', '.join([F"""{arg}={value}""" for arg, value in zip(params[1:],     args[1:]     )]     )
         raise TypeError(
             F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
             F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`"""     )
      while True:
         if batch_size == 0:
            raise RuntimeError('No executable batch size found, reached zero.'     )
         try:
            return function(lowerCAmelCase_,     *lowerCAmelCase_,     **lowerCAmelCase_     )
         except Exception as e:
            if should_reduce_batch_size(lowerCAmelCase_     ):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator








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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     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
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

