
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							@property
							def 			A__				(    self):
												lowercase						     =						1
												lowercase						     =						3
												lowercase						     =						(3_2, 3_2)

												lowercase						     =						floats_tensor((batch_size, num_channels) + sizes       ,rng=random.Random(0)).to(A__)
												return image




							@property
							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						UNetaDConditionModel(
												    block_out_channels=(3_2, 3_2, 6_4)       ,layers_per_block=2       ,sample_size=3_2       ,in_channels=7       ,out_channels=4       ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')       ,up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,cross_attention_dim=3_2       ,attention_head_dim=8       ,use_linear_projection=A__       ,only_cross_attention=(True, True, False)       ,num_class_embeds=1_0_0       ,)
												return model




							@property
							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						AutoencoderKL(
												    block_out_channels=[3_2, 3_2, 6_4]       ,in_channels=3       ,out_channels=3       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=4       ,)
												return model




							@property
							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						CLIPTextConfig(
												    bos_token_id=0       ,eos_token_id=2       ,hidden_size=3_2       ,intermediate_size=3_7       ,layer_norm_eps=1E-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1_0_0_0       ,hidden_act='''gelu'''       ,projection_dim=5_1_2       ,)
												return CLIPTextModel(A__)




							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase						     =						self.dummy_cond_unet_upscale
												lowercase						     =						DDPMScheduler()
												lowercase						     =						DDIMScheduler(prediction_type='''v_prediction''')
												lowercase						     =						self.dummy_vae
												lowercase						     =						self.dummy_text_encoder
												lowercase						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase						     =						self.dummy_image.cpu().permute(0       ,2       ,3       ,1)[0]
												lowercase						     =						Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((6_4, 6_4))

												# make sure here that pndm scheduler skips prk
												lowercase						     =						StableDiffusionUpscalePipeline(
												    unet=A__       ,low_res_scheduler=A__       ,scheduler=A__       ,vae=A__       ,text_encoder=A__       ,tokenizer=A__       ,max_noise_level=3_5_0       ,)
												lowercase						     =						sd_pipe.to(A__)
												sd_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						'''A painting of a squirrel eating a burger'''
												lowercase						     =						torch.Generator(device=A__).manual_seed(0)
												lowercase						     =						sd_pipe(
												    [prompt]       ,image=A__       ,generator=A__       ,guidance_scale=6.0       ,noise_level=2_0       ,num_inference_steps=2       ,output_type='''np'''       ,)

												lowercase						     =						output.images

												lowercase						     =						torch.Generator(device=A__).manual_seed(0)
												lowercase						     =						sd_pipe(
												    [prompt]       ,image=A__       ,generator=A__       ,guidance_scale=6.0       ,noise_level=2_0       ,num_inference_steps=2       ,output_type='''np'''       ,return_dict=A__       ,)[0]

												lowercase						     =						image[0, -3:, -3:, -1]
												lowercase						     =						image_from_tuple[0, -3:, -3:, -1]

												lowercase						     =						low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												lowercase						     =						np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

												assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2




							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase						     =						self.dummy_cond_unet_upscale
												lowercase						     =						DDPMScheduler()
												lowercase						     =						DDIMScheduler(prediction_type='''v_prediction''')
												lowercase						     =						self.dummy_vae
												lowercase						     =						self.dummy_text_encoder
												lowercase						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase						     =						self.dummy_image.cpu().permute(0       ,2       ,3       ,1)[0]
												lowercase						     =						Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((6_4, 6_4))

												# make sure here that pndm scheduler skips prk
												lowercase						     =						StableDiffusionUpscalePipeline(
												    unet=A__       ,low_res_scheduler=A__       ,scheduler=A__       ,vae=A__       ,text_encoder=A__       ,tokenizer=A__       ,max_noise_level=3_5_0       ,)
												lowercase						     =						sd_pipe.to(A__)
												sd_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						'''A painting of a squirrel eating a burger'''
												lowercase						     =						sd_pipe(
												    2 * [prompt]       ,image=2 * [low_res_image]       ,guidance_scale=6.0       ,noise_level=2_0       ,num_inference_steps=2       ,output_type='''np'''       ,)
												lowercase						     =						output.images
												assert image.shape[0] == 2

												lowercase						     =						torch.Generator(device=A__).manual_seed(0)
												lowercase						     =						sd_pipe(
												    [prompt]       ,image=A__       ,generator=A__       ,num_images_per_prompt=2       ,guidance_scale=6.0       ,noise_level=2_0       ,num_inference_steps=2       ,output_type='''np'''       ,)
												lowercase						     =						output.images
												assert image.shape[0] == 2





							@unittest.skipIf(torch_device != '''cuda'''       ,'''This test requires a GPU''')
							def 			A__				(    self):
												lowercase						     =						self.dummy_cond_unet_upscale
												lowercase						     =						DDPMScheduler()
												lowercase						     =						DDIMScheduler(prediction_type='''v_prediction''')
												lowercase						     =						self.dummy_vae
												lowercase						     =						self.dummy_text_encoder
												lowercase						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase						     =						self.dummy_image.cpu().permute(0       ,2       ,3       ,1)[0]
												lowercase						     =						Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((6_4, 6_4))

												# put models in fp16, except vae as it overflows in fp16
												lowercase						     =						unet.half()
												lowercase						     =						text_encoder.half()

												# make sure here that pndm scheduler skips prk
												lowercase						     =						StableDiffusionUpscalePipeline(
												    unet=A__       ,low_res_scheduler=A__       ,scheduler=A__       ,vae=A__       ,text_encoder=A__       ,tokenizer=A__       ,max_noise_level=3_5_0       ,)
												lowercase						     =						sd_pipe.to(A__)
												sd_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						'''A painting of a squirrel eating a burger'''
												lowercase						     =						torch.manual_seed(0)
												lowercase						     =						sd_pipe(
												    [prompt]       ,image=A__       ,generator=A__       ,num_inference_steps=2       ,output_type='''np'''       ,).images

												lowercase						     =						low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 			A__				(    self):
												lowercase						     =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png''')
												lowercase						     =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
												    '''/upsampled_cat.npy''')

												lowercase						     =						'''stabilityai/stable-diffusion-x4-upscaler'''
												lowercase						     =						StableDiffusionUpscalePipeline.from_pretrained(A__)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase						     =						'''a cat sitting on a park bench'''

												lowercase						     =						torch.manual_seed(0)
												lowercase						     =						pipe(
												    prompt=A__       ,image=A__       ,generator=A__       ,output_type='''np'''       ,)
												lowercase						     =						output.images[0]

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




							def 			A__				(    self):
												lowercase						     =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png''')
												lowercase						     =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
												    '''/upsampled_cat_fp16.npy''')

												lowercase						     =						'''stabilityai/stable-diffusion-x4-upscaler'''
												lowercase						     =						StableDiffusionUpscalePipeline.from_pretrained(
												    A__       ,torch_dtype=torch.floataa       ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing()

												lowercase						     =						'''a cat sitting on a park bench'''

												lowercase						     =						torch.manual_seed(0)
												lowercase						     =						pipe(
												    prompt=A__       ,image=A__       ,generator=A__       ,output_type='''np'''       ,)
												lowercase						     =						output.images[0]

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





							def 			A__				(    self):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowercase						     =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-upscale/low_res_cat.png''')

												lowercase						     =						'''stabilityai/stable-diffusion-x4-upscaler'''
												lowercase						     =						StableDiffusionUpscalePipeline.from_pretrained(
												    A__       ,torch_dtype=torch.floataa       ,)
												pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)
												pipe.enable_attention_slicing(1)
												pipe.enable_sequential_cpu_offload()

												lowercase						     =						'''a cat sitting on a park bench'''

												lowercase						     =						torch.manual_seed(0)
												lowercase						     =						pipe(
												    prompt=A__       ,image=A__       ,generator=A__       ,num_inference_steps=5       ,output_type='''np'''       ,)

												lowercase						     =						torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 1_0**9






from statistics import mean

import numpy as np



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						0
					# Number of processes finished
					lowercase						     =						0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					lowercase						     =						[0] * no_of_process
					# List to include calculation results
					lowercase						     =						[0] * no_of_process

					# Sort by arrival time.
					lowercase						     =						[burst_time[i] for i in np.argsort(lowerCAmelCase__		)]
					lowercase						     =						[process_name[i] for i in np.argsort(lowerCAmelCase__		)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										lowercase						     =						0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															lowercase						     =						arrival_time[i]

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

        # Calculate the turn around time
										lowercase						     =						current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										lowercase						     =						1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[0] * no_of_process
					for i in range(0			,      lowerCAmelCase__		):
										lowercase						     =						turn_around_time[i] - burst_time[i]
					return waiting_time


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

							lowercase__		:List[str]			  =   calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							lowercase__		:List[Any]			  =   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}')




