






'''simple docstring'''







import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _lowercase			(       _lowercase       ,   unittest.TestCase	):
							# TODO: is there an appropriate internal test set?
							a          = """ssube/stable-diffusion-x4-upscaler-onnx"""




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Optional[Any]=0  ):
														lowerCamelCase__      :			Optional[Any] 						= floats_tensor((1, 3, 128, 128)    ,							rng=random.Random(UpperCamelCase__  )  )
														lowerCamelCase__      :			Dict 						= torch.manual_seed(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= {
														    """prompt""": """A painting of a squirrel eating a burger""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 3,
														    """guidance_scale""": 7.5,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			int 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_dummy_inputs()
														lowerCamelCase__      :			Optional[Any] 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Union[str, Any] 						= image[0, -3:, -3:, -1].flatten()

														# started as 128, should now be 512
														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			Any 						= np.array(
														    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]  )
														assert np.abs(image_slice - expected_slice  ).max() < 1e-1




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
														lowerCamelCase__      :			Tuple 						= PNDMScheduler.from_config(pipe.scheduler.config    ,							skip_prk_steps=UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			int 						= self.get_dummy_inputs()
														lowerCamelCase__      :			int 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			List[str] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			Union[str, Any] 						= np.array(
														    [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]  )
														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Optional[Any] 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
														lowerCamelCase__      :			str 						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= self.get_dummy_inputs()
														lowerCamelCase__      :			Dict 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Optional[Any] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			List[Any] 						= np.array(
														    [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]  )

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




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Dict 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
														lowerCamelCase__      :			Tuple 						= EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= self.get_dummy_inputs()
														lowerCamelCase__      :			int 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			List[Any] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			Dict 						= np.array(
														    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]  )
														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Dict 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="""CPUExecutionProvider"""  )
														lowerCamelCase__      :			int 						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= self.get_dummy_inputs()
														lowerCamelCase__      :			Dict 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Any 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			int 						= np.array(
														    [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]  )

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



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




							@property
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														return (
														    "CUDAExecutionProvider",
														    {
														        "gpu_mem_limit": "15000000000",  # 15GB
														        "arena_extend_strategy": "kSameAsRequested",
														    },
														)




							@property
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= ort.SessionOptions()
														lowerCamelCase__      :			List[Any] 						= False
														return options




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Union[str, Any] 						= load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/img2img/sketch-mountains-input.jpg"""  )
														lowerCamelCase__      :			Union[str, Any] 						= init_image.resize((128, 128)  )
														# using the PNDM scheduler by default
														lowerCamelCase__      :			Optional[int] 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(
														    """ssube/stable-diffusion-x4-upscaler-onnx"""    ,							provider=self.gpu_provider    ,							sess_options=self.gpu_options    ,							)
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= """A fantasy landscape, trending on artstation"""

														lowerCamelCase__      :			int 						= torch.manual_seed(0  )
														lowerCamelCase__      :			Union[str, Any] 						= pipe(
														    prompt=UpperCamelCase__    ,							image=UpperCamelCase__    ,							guidance_scale=7.5    ,							num_inference_steps=10    ,							generator=UpperCamelCase__    ,							output_type="""np"""    ,							)
														lowerCamelCase__      :			int 						= output.images
														lowerCamelCase__      :			Union[str, Any] 						= images[0, 255:258, 383:386, -1]

														assert images.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			List[Any] 						= np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]  )
														# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2e-2




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			List[str] 						= load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/img2img/sketch-mountains-input.jpg"""  )
														lowerCamelCase__      :			Union[str, Any] 						= init_image.resize((128, 128)  )
														lowerCamelCase__      :			Optional[int] 						= LMSDiscreteScheduler.from_pretrained(
														    """ssube/stable-diffusion-x4-upscaler-onnx"""    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			Dict 						= OnnxStableDiffusionUpscalePipeline.from_pretrained(
														    """ssube/stable-diffusion-x4-upscaler-onnx"""    ,							scheduler=UpperCamelCase__    ,							provider=self.gpu_provider    ,							sess_options=self.gpu_options    ,							)
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= """A fantasy landscape, trending on artstation"""

														lowerCamelCase__      :			List[str] 						= torch.manual_seed(0  )
														lowerCamelCase__      :			int 						= pipe(
														    prompt=UpperCamelCase__    ,							image=UpperCamelCase__    ,							guidance_scale=7.5    ,							num_inference_steps=20    ,							generator=UpperCamelCase__    ,							output_type="""np"""    ,							)
														lowerCamelCase__      :			Optional[Any] 						= output.images
														lowerCamelCase__      :			Any 						= images[0, 255:258, 383:386, -1]

														assert images.shape == (1, 512, 512, 3)
														lowerCamelCase__      :			int 						= np.array(
														    [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]  )
														# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2e-2









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

														lowerCamelCase__      :			Any 						= 0
														# Index showing the location of the process being performed
														lowerCamelCase__      :			Union[str, Any] 						= 0
														# Saves the current response ratio.
														lowerCamelCase__      :			Any 						= 0
														for i in range(0       ,    UpperCamelCase   ):
																					if finished_process[i] == 0 and arrival_time[i] <= current_time:
																												lowerCamelCase__      :			Optional[int] 						= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																												    i
																												]
																					if response_ratio < temp:
																												lowerCamelCase__      :			int 						= temp
																												lowerCamelCase__      :			str 						= i

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
			_A			:    List[str]					  =5
			_A			:    Optional[Any]					  =['''A''', '''B''', '''C''', '''D''', '''E''']
			_A			:    Optional[int]					  =[1, 2, 3, 4, 5]
			_A			:    Dict					  =[1, 2, 3, 4, 5]

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[int]					  =calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
			for i in range(0, no_of_process):
						print(
						    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(F'average waiting time : {mean(waiting_time):.5f}')
			print(F'average turn around time : {mean(turn_around_time):.5f}')

