
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()







class     lowercase		( unittest.TestCase		):
							lowercase_	:	List[Any]	 =StableDiffusionLDMaDPipeline
							lowercase_	:	Dict	 =TEXT_TO_IMAGE_PARAMS
							lowercase_	:	Dict	 =TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_	:	List[str]	 =TEXT_TO_IMAGE_IMAGE_PARAMS




							def 			A__				(    self):
												torch.manual_seed(0)
												lowercase						     =						UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)       ,layers_per_block=2       ,sample_size=3_2       ,in_channels=4       ,out_channels=4       ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,cross_attention_dim=3_2       ,)
												lowercase						     =						DDIMScheduler(
												    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule='''scaled_linear'''       ,clip_sample=A__       ,set_alpha_to_one=A__       ,)
												torch.manual_seed(0)
												lowercase						     =						AutoencoderKL(
												    block_out_channels=[3_2, 6_4]       ,in_channels=6       ,out_channels=6       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=4       ,)
												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       ,)
												lowercase						     =						CLIPTextModel(A__)
												lowercase						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase						     =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components




							def 			A__				(    self       ,A__       ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase						     =						torch.manual_seed(A__)
												else:
																	lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 6.0,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

												lowercase						     =						self.get_dummy_components()
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						rgb[0, -3:, -3:, -1]
												lowercase						     =						depth[0, -3:, -1]

												assert rgb.shape == (1, 6_4, 6_4, 3)
												assert depth.shape == (1, 6_4, 6_4)

												lowercase						     =						np.array(
												    [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262])
												lowercase						     =						np.array([103.46727, 85.812004, 87.849236])

												assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1E-2
												assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1E-2




							def 			A__				(    self):
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						3 * [inputs['''prompt''']]

												# forward
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb_slice_a[0, -3:, -3:, -1]
												lowercase						     =						depth_slice_a[0, -3:, -1]

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						3 * [inputs.pop('''prompt''')]

												lowercase						     =						ldmad_pipe.tokenizer(
												    A__       ,padding='''max_length'''       ,max_length=ldmad_pipe.tokenizer.model_max_length       ,truncation=A__       ,return_tensors='''pt'''       ,)
												lowercase						     =						text_inputs['''input_ids'''].to(A__)

												lowercase						     =						ldmad_pipe.text_encoder(A__)[0]

												lowercase						     =						prompt_embeds

												# forward
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb_slice_a[0, -3:, -3:, -1]
												lowercase						     =						depth_slice_a[0, -3:, -1]

												assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1E-4
												assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1E-4





							def 			A__				(    self):
												lowercase						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase						     =						self.get_dummy_components()
												lowercase						     =						PNDMScheduler(skip_prk_steps=A__)
												lowercase						     =						StableDiffusionLDMaDPipeline(**A__)
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_dummy_inputs(A__)
												lowercase						     =						'''french fries'''
												lowercase						     =						ldmad_pipe(**A__       ,negative_prompt=A__)

												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb[0, -3:, -3:, -1]
												lowercase						     =						depth[0, -3:, -1]

												assert rgb.shape == (1, 6_4, 6_4, 3)
												assert depth.shape == (1, 6_4, 6_4)

												lowercase						     =						np.array(
												    [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217])
												lowercase						     =						np.array([107.84738, 84.62802, 89.962135])
												assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1E-2
												assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1E-2







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




							def 			A__				(    self):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 			A__				(    self       ,A__       ,A__="cpu"       ,A__=torch.floataa       ,A__=0):
												lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						np.random.RandomState(A__).standard_normal((1, 4, 6_4, 6_4))
												lowercase						     =						torch.from_numpy(A__).to(device=A__       ,dtype=A__)
												lowercase						     =						{
												    '''prompt''': '''a photograph of an astronaut riding a horse''',
												    '''latents''': latents,
												    '''generator''': generator,
												    '''num_inference_steps''': 3,
												    '''guidance_scale''': 7.5,
												    '''output_type''': '''numpy''',
												}
												return inputs





							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''')
												lowercase						     =						ldmad_pipe.to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth
												lowercase						     =						rgb[0, -3:, -3:, -1].flatten()
												lowercase						     =						rgb[0, -3:, -1].flatten()

												assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
												assert depth.shape == (1, 5_1_2, 5_1_2)

												lowercase						     =						np.array(
												    [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706])
												lowercase						     =						np.array(
												    [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706])
												assert np.abs(rgb_slice - expected_slice_rgb).max() < 3E-3
												assert np.abs(depth_slice - expected_slice_depth).max() < 3E-3







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




							def 			A__				(    self):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 			A__				(    self       ,A__       ,A__="cpu"       ,A__=torch.floataa       ,A__=0):
												lowercase						     =						torch.Generator(device=A__).manual_seed(A__)
												lowercase						     =						np.random.RandomState(A__).standard_normal((1, 4, 6_4, 6_4))
												lowercase						     =						torch.from_numpy(A__).to(device=A__       ,dtype=A__)
												lowercase						     =						{
												    '''prompt''': '''a photograph of an astronaut riding a horse''',
												    '''latents''': latents,
												    '''generator''': generator,
												    '''num_inference_steps''': 5_0,
												    '''guidance_scale''': 7.5,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''').to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						0.495586
												lowercase						     =						0.33795515
												lowercase						     =						112.48518
												lowercase						     =						98.489746
												assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
												assert np.abs(expected_depth_std - depth.std()) < 1E-3





							def 			A__				(    self):
												lowercase						     =						StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''').to(A__)
												ldmad_pipe.set_progress_bar_config(disable=A__)

												lowercase						     =						self.get_inputs(A__)
												lowercase						     =						ldmad_pipe(**A__)
												lowercase     ,						lowercase						     =						output.rgb, output.depth

												lowercase						     =						0.4194127
												lowercase						     =						0.35375586
												lowercase						     =						0.5638502
												lowercase						     =						0.34686103

												assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
												assert depth.shape == (1, 5_1_2, 5_1_2, 1)
												assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
												assert np.abs(expected_depth_std - depth.std()) < 1E-3






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}')




