





"""simple docstring"""




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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


a		:					Optional[int]	      =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								super().__init__()

								if safety_checker is None:
									logger.warning(
									    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
									    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
									    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
									    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
									    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
									    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." )

								self.register_modules(
								    speech_model=lowerCAmelCase__ ,   speech_processor=lowerCAmelCase__ ,   vae=lowerCAmelCase__ ,   text_encoder=lowerCAmelCase__ ,   tokenizer=lowerCAmelCase__ ,   unet=lowerCAmelCase__ ,   scheduler=lowerCAmelCase__ ,   feature_extractor=lowerCAmelCase__ ,   )






							def        __a  (  self ,   lowerCAmelCase__ = "auto" )      ->					List[Any]:
								if slice_size == "auto":
									a :						List[str]      =							self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								self.enable_attention_slicing(lowerCAmelCase__ )






							@torch.no_grad()
							def __call__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=1_6000 ,   lowerCAmelCase__ = 512 ,   lowerCAmelCase__ = 512 ,   lowerCAmelCase__ = 50 ,   lowerCAmelCase__ = 7.5 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 ,   lowerCAmelCase__ = 0.0 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = "pil" ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 ,   **lowerCAmelCase__ ,   )      ->					List[Any]:
								a :						Optional[Any]      =							self.speech_processor.feature_extractor(
								    lowerCAmelCase__ ,   return_tensors="pt" ,   sampling_rate=lowerCAmelCase__ ).input_features.to(self.device )
								a :						Optional[int]      =							self.speech_model.generate(lowerCAmelCase__ ,   max_length=48_0000 )

								a :						int      =							self.speech_processor.tokenizer.batch_decode(lowerCAmelCase__ ,   skip_special_tokens=lowerCAmelCase__ ,   normalize=lowerCAmelCase__ )[
								    0
								]

								if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									a :						List[Any]      =							1
								elif isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									a :						List[str]      =							len(lowerCAmelCase__ )
								else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__ )}""" )

								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(lowerCAmelCase__ ,   lowerCAmelCase__ ) or callback_steps <= 0)
								):
									raise ValueError(
									    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    f""" {type(lowerCAmelCase__ )}.""" )

								# get prompt text embeddings
								a :						Tuple      =							self.tokenizer(
								    lowerCAmelCase__ ,   padding="max_length" ,   max_length=self.tokenizer.model_max_length ,   return_tensors="pt" ,   )
								a :						Union[str, Any]      =							text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									a :						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}""" )
									a :						Union[str, Any]      =							text_input_ids[:, : self.tokenizer.model_max_length]
								a :						int      =							self.text_encoder(text_input_ids.to(self.device ) )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								a,			a,			a :						Optional[Any]      =							text_embeddings.shape
								a :						Tuple      =							text_embeddings.repeat(1 ,   lowerCAmelCase__ ,   1 )
								a :						Dict      =							text_embeddings.view(bs_embed * num_images_per_prompt ,   lowerCAmelCase__ ,   -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.
								a :						List[str]      =							guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									a :						List[str]
									if negative_prompt is None:
										a :						Dict      =							[""] * batch_size
									elif type(lowerCAmelCase__ ) is not type(lowerCAmelCase__ ):
										raise TypeError(
										    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__ )} !="""
										    f""" {type(lowerCAmelCase__ )}.""" )
									elif isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
										a :						List[str]      =							[negative_prompt]
									elif batch_size != len(lowerCAmelCase__ ):
										raise ValueError(
										    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__ )}, but `prompt`:"""
										    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
										    " the batch size of `prompt`." )
									else:
										a :						Union[str, Any]      =							negative_prompt

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

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									a :						Dict      =							uncond_embeddings.shape[1]
									a :						Optional[Any]      =							uncond_embeddings.repeat(1 ,   lowerCAmelCase__ ,   1 )
									a :						Optional[int]      =							uncond_embeddings.view(batch_size * num_images_per_prompt ,   lowerCAmelCase__ ,   -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
									a :						Tuple      =							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`.
								a :						List[str]      =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								a :						Any      =							text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not exist on mps
										a :						Union[str, Any]      =							torch.randn(lowerCAmelCase__ ,   generator=lowerCAmelCase__ ,   device="cpu" ,   dtype=lowerCAmelCase__ ).to(
										    self.device )
									else:
										a :						int      =							torch.randn(lowerCAmelCase__ ,   generator=lowerCAmelCase__ ,   device=self.device ,   dtype=lowerCAmelCase__ )
								else:
									if latents.shape != latents_shape:
										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
									a :						Dict      =							latents.to(self.device )

								# set timesteps
								self.scheduler.set_timesteps(lowerCAmelCase__ )

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

								# scale the initial noise by the standard deviation required by the scheduler
								a :						str      =							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]
								a :						List[Any]      =							"eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
								a :						Dict      =							{}
								if accepts_eta:
									a :						List[Any]      =							eta

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

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

									# perform guidance
									if do_classifier_free_guidance:
										a,			a :						Any      =							noise_pred.chunk(2 )
										a :						int      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									a :						Union[str, Any]      =							self.scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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

								a :						str      =							1 / 0.18_215 * latents
								a :						List[Any]      =							self.vae.decode(lowerCAmelCase__ ).sample

								a :						Union[str, Any]      =							(image / 2 + 0.5).clamp(0 ,   1 )

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

								if output_type == "pil":
									a :						Union[str, Any]      =							self.numpy_to_pil(lowerCAmelCase__ )

								if not return_dict:
									return image

								return StableDiffusionPipelineOutput(images=lowerCAmelCase__ ,   nsfw_content_detected=lowerCAmelCase__ )









"""simple docstring"""




from __future__ import annotations

from statistics import mean






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       list[int]       ,			_lowercase		:       int   )					->list[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						Any      =							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:
		a :						str      =							[]
		a :						Tuple      =							-1

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

		if len(_lowercase   ) > 0:
			a :						List[Any]      =							ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					a :						Optional[Any]      =							i
			total_time += burst_time[target_process]
			completed += 1
			a :						str      =							0
			a :						Union[str, Any]      =							(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       int       ,			_lowercase		:       list[int]   )					->list[int]:






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


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

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[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}''')


