
"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


UpperCAmelCase__      :    Any    							= logging.get_logger(__name__)  # pylint: disable=invalid-name



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       )					->		Optional[int]:



											"""simple docstring"""




											super().__init__()
											self.register_modules(
											    vae=SCREAMING_SNAKE_CASE__   ,       text_encoder=SCREAMING_SNAKE_CASE__   ,       tokenizer=SCREAMING_SNAKE_CASE__   ,       unet=SCREAMING_SNAKE_CASE__   ,       scheduler=SCREAMING_SNAKE_CASE__   ,       safety_checker=SCREAMING_SNAKE_CASE__   ,       feature_extractor=SCREAMING_SNAKE_CASE__   ,       )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__ = "auto"    )					->		List[str]:



											"""simple docstring"""




											if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											self.enable_attention_slicing(SCREAMING_SNAKE_CASE__    )



				@torch.no_grad()
				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = 5_12   ,       SCREAMING_SNAKE_CASE__ = 5_12   ,       SCREAMING_SNAKE_CASE__ = 50   ,       SCREAMING_SNAKE_CASE__ = 7.5   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 1   ,       SCREAMING_SNAKE_CASE__ = 0.0   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = "pil"   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 1   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       str           =   1
											elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   len(SCREAMING_SNAKE_CASE__    )
											else:
																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE__    )}'''    )

											if height % 8 != 0 or width % 8 != 0:
																		raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''    )

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																		    F''' {type(SCREAMING_SNAKE_CASE__    )}.'''    )

											# get prompt text embeddings
											SCREAMING_SNAKE_CASE__						:       int           =   self.tokenizer(
											    SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors="""pt"""   ,       )
											SCREAMING_SNAKE_CASE__						:       str           =   text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
																		logger.warning(
																		    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																		    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''    )
																		SCREAMING_SNAKE_CASE__						:       str           =   text_input_ids[:, : self.tokenizer.model_max_length]

											if text_embeddings is None:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.text_encoder(text_input_ids.to(self.device    )    )[0]

											# duplicate text embeddings for each generation per prompt, using mps friendly method
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   text_embeddings.shape
											SCREAMING_SNAKE_CASE__						:       int           =   text_embeddings.repeat(1   ,       SCREAMING_SNAKE_CASE__   ,       1    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   text_embeddings.view(bs_embed * num_images_per_prompt   ,       SCREAMING_SNAKE_CASE__   ,       -1    )

											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																		SCREAMING_SNAKE_CASE__						:       List[str]
																		if negative_prompt is None:
																									SCREAMING_SNAKE_CASE__						:       str           =   [""""""]
																		elif type(SCREAMING_SNAKE_CASE__    ) is not type(SCREAMING_SNAKE_CASE__    ):
																									raise TypeError(
																									    F'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE__    )} !='''
																									    F''' {type(SCREAMING_SNAKE_CASE__    )}.'''    )
																		elif isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [negative_prompt]
																		elif batch_size != len(SCREAMING_SNAKE_CASE__    ):
																									raise ValueError(
																									    F'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE__    )}, but `prompt`:'''
																									    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																									    """ the batch size of `prompt`."""    )
																		else:
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   negative_prompt

																		SCREAMING_SNAKE_CASE__						:       Tuple           =   text_input_ids.shape[-1]
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.tokenizer(
																		    SCREAMING_SNAKE_CASE__   ,       padding="""max_length"""   ,       max_length=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""   ,       )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		SCREAMING_SNAKE_CASE__						:       Any           =   uncond_embeddings.shape[1]
																		SCREAMING_SNAKE_CASE__						:       Any           =   uncond_embeddings.repeat(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       1    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   uncond_embeddings.view(batch_size * num_images_per_prompt   ,       SCREAMING_SNAKE_CASE__   ,       -1    )

																		# For classifier free guidance, we need to do two forward passes.
																		# Here we concatenate the unconditional and text embeddings into a single batch
																		# to avoid doing two forward passes
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.cat([uncond_embeddings, text_embeddings]    )

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											SCREAMING_SNAKE_CASE__						:       List[str]           =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											SCREAMING_SNAKE_CASE__						:       str           =   (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not exist on mps
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.randn(
																									    SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       device="""cpu"""   ,       dtype=SCREAMING_SNAKE_CASE__    ).to(self.device    )
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.randn(SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       device="""cpu"""   ,       dtype=SCREAMING_SNAKE_CASE__    ).to(
																									    self.device    )
																		else:
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.randn(
																									    SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       device=self.device   ,       dtype=SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.randn(SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       device=self.device   ,       dtype=SCREAMING_SNAKE_CASE__    )
											else:
																		if latents_reference.shape != latents_shape:
																									raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   latents_reference.to(self.device    )
																		SCREAMING_SNAKE_CASE__						:       Any           =   latents.to(self.device    )

											# This is the key part of the pipeline where we
											# try to ensure that the generated images w/ the same seed
											# but different sizes actually result in similar images
											SCREAMING_SNAKE_CASE__						:       List[str]           =   (latents_shape[3] - latents_shape_reference[3]) // 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   (latents_shape[2] - latents_shape_reference[2]) // 2
											SCREAMING_SNAKE_CASE__						:       Dict           =   latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
											SCREAMING_SNAKE_CASE__						:       Dict           =   latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
											SCREAMING_SNAKE_CASE__						:       int           =   0 if dx < 0 else dx
											SCREAMING_SNAKE_CASE__						:       Tuple           =   0 if dy < 0 else dy
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   max(-dx   ,       0    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   max(-dy   ,       0    )
											# import pdb
											# pdb.set_trace()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   latents_reference[:, :, dy : dy + h, dx : dx + w]

											# set timesteps
											self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__    )

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.scheduler.timesteps.to(self.device    )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE__						:       int           =   latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   """eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   eta

											for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__    )    ):
																		# expand the latents if we are doing classifier free guidance
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

																		# predict the noise residual
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.unet(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__    ).sample

																		# perform guidance
																		if do_classifier_free_guidance:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   noise_pred.chunk(2    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE__						:       int           =   self.scheduler.step(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    ).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																									callback(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   1 / 0.18215 * latents
											SCREAMING_SNAKE_CASE__						:       Any           =   self.vae.decode(SCREAMING_SNAKE_CASE__    ).sample

											SCREAMING_SNAKE_CASE__						:       Dict           =   (image / 2 + 0.5).clamp(0   ,       1    )

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   image.cpu().permute(0   ,       2   ,       3   ,       1    ).float().numpy()

											if self.safety_checker is not None:
																		SCREAMING_SNAKE_CASE__						:       str           =   self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE__    )   ,       return_tensors="""pt"""    ).to(
																		    self.device    )
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   self.safety_checker(
																		    images=SCREAMING_SNAKE_CASE__   ,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   None

											if output_type == "pil":
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.numpy_to_pil(SCREAMING_SNAKE_CASE__    )

											if not return_dict:
																		return (image, has_nsfw_concept)

											return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE__   ,       nsfw_content_detected=SCREAMING_SNAKE_CASE__    )






from __future__ import annotations

from statistics import mean




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				__lowerCamelCase    					=	[0] * no_of_processes

				# Initialize remaining_time to waiting_time.

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i]
				__lowerCamelCase    					=	[]

				__lowerCamelCase    					=	0
				__lowerCamelCase    					=	0

				# When processes are not completed,
				# A process whose arrival time has passed \
				# and has remaining execution time is put into the ready_process.
				# The shortest process in the ready_process, target_process is executed.

				while completed != no_of_processes:
								__lowerCamelCase    					=	[]
								__lowerCamelCase    					=	-1

								for i in range(__lowerCAmelCase							):
												if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
																ready_process.append(__lowerCAmelCase							)

								if len(__lowerCAmelCase							) > 0:
												__lowerCamelCase    					=	ready_process[0]
												for i in ready_process:
																if remaining_time[i] < remaining_time[target_process]:
																				__lowerCamelCase    					=	i
												total_time += burst_time[target_process]
												completed += 1
												__lowerCamelCase    					=	0
												__lowerCamelCase    					=	(
												    total_time - arrival_time[target_process] - burst_time[target_process]
												)
								else:
												total_time += 1

				return waiting_time




def  __magic_name__ (   __lowerCAmelCase	:						list[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						list[int]							)			->   list[int]:
				__lowerCamelCase    					=	[0] * no_of_processes
				for i in range(__lowerCAmelCase							):
								__lowerCamelCase    					=	burst_time[i] + waiting_time[i]
				return turn_around_time


if __name__ == "__main__":
					print("[TEST CASE 01]")

					SCREAMING_SNAKE_CASE__   :			Tuple												=    4
					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    [2, 5, 3, 7]
					SCREAMING_SNAKE_CASE__   :			List[str]												=    [0, 0, 0, 0]
					SCREAMING_SNAKE_CASE__   :			str												=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
										    F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
										)
					print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
					print(F'Average turnaround time = {mean(turn_around_time):.5f}')



