"""simple docstring"""





import gc
import threading
import time

import psutil
import torch


class      UpperCAmelCase_	:


      def __init__(   self							)       ->					Union[str, Any]:
             lowercase__			:		Dict       =	psutil.Process()
             lowercase__			:		int       =	False


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		List[str]       =	-1

             while True:
                    lowercase__			:		str       =	max(self.process.memory_info().rss		,			self.cpu_memory_peak							)

                    # can't sleep or will not catch the peak right (this comment is here on purpose)
                    if not self.peak_monitoring:
                           break


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		List[Any]       =	True
             lowercase__			:		Optional[int]       =	threading.Thread(target=self.peak_monitor							)
             lowercase__			:		List[str]       =	True
             self.thread.start()


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	False
             self.thread.join()
             return self.cpu_memory_peak





_UpperCamelCase   :						Optional[Any]      =    PeakCPUMemory()





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Any       =	{'time': time.time()}

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

       # CPU mem
       lowercase__			:		Tuple       =	psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count()     ):
              lowercase__			:		Union[str, Any]       =	torch.cuda.memory_allocated(_lowerCAmelCase     )
       torch.cuda.reset_peak_memory_stats()

       return measures





def 				a_     (      _lowerCAmelCase  : Tuple     ):





       '''simple docstring'''

       lowercase__			:		int       =	{'time': time.time() - start_measures['time']}

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

       # CPU mem
       lowercase__			:		Union[str, Any]       =	(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
       lowercase__			:		Any       =	(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

       # GPU mem
       for i in range(torch.cuda.device_count()     ):
              lowercase__			:		Optional[int]       =	(torch.cuda.memory_allocated(_lowerCAmelCase     ) - start_measures[str(_lowerCAmelCase     )]) / 2**20
              lowercase__			:		Union[str, Any]       =	(torch.cuda.max_memory_allocated(_lowerCAmelCase     ) - start_measures[str(_lowerCAmelCase     )]) / 2**20

       return measures






def 				a_     (      _lowerCAmelCase  : Optional[Any]   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       print(f"""{description}:"""     )
       print(f"""- Time: {measures["time"]:.2f}s"""     )
       for i in range(torch.cuda.device_count()     ):
              print(f"""- GPU {i} allocated: {measures[str(_lowerCAmelCase     )]:.2f}MiB"""     )
              lowercase__			:		List[Any]       =	measures[f"""{i}-peak"""]
              print(f"""- GPU {i} peak: {peak:.2f}MiB"""     )
       print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""     )
       print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""     )





'''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 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  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
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

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



