"""simple docstring"""






from __future__ import annotations

import pandas as pd


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]					,		lowerCAmelCase__     :list[int]					,		lowerCAmelCase__     :int		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	[0] * no_of_processes
      lowercase														=	[0] * no_of_processes
      # Copy the burst time into remaining_time[]
      for i in range(__lowerCamelCase		):
            lowercase														=	burst_time[i]

      lowercase														=	0
      lowercase														=	0
      lowercase														=	9_9_9_9_9_9_9_9_9
      lowercase														=	0
      lowercase														=	False

      # Process until all processes are completed
      while complete != no_of_processes:
            for j in range(__lowerCamelCase		):
                  if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                        if remaining_time[j] < minm:
                              lowercase														=	remaining_time[j]
                              lowercase														=	j
                              lowercase														=	True

            if not check:
                  increment_time += 1
                  continue
            remaining_time[short] -= 1

            lowercase														=	remaining_time[short]
            if minm == 0:
                  lowercase														=	9_9_9_9_9_9_9_9_9

            if remaining_time[short] == 0:
                  complete += 1
                  lowercase														=	False

                  # Find finish time of current process
                  lowercase														=	increment_time + 1

                  # Calculate waiting time
                  lowercase														=	finish_time - arrival_time[short]
                  lowercase														=	finar - burst_time[short]

                  if waiting_time[short] < 0:
                        lowercase														=	0

        # Increment time
            increment_time += 1
      return waiting_time


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :list[int]		) ->    str:
      '''simple docstring'''


      lowercase														=	[0] * no_of_processes
      for i in range(__lowerCamelCase		):
            lowercase														=	burst_time[i] + waiting_time[i]
      return turn_around_time


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]					,		lowerCAmelCase__     :list[int]					,		lowerCAmelCase__     :int		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	0
      lowercase														=	0
      for i in range(__lowerCamelCase		):
            lowercase														=	total_waiting_time + waiting_time[i]
            lowercase														=	total_turn_around_time + turn_around_time[i]
      print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}'		)
      print("""Average turn around time ="""					,		total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
       print("""Enter how many process you want to analyze""")
       __lowerCAmelCase    :       List[str]					     =int(input())
       __lowerCAmelCase    :       Union[str, Any]					     =[0] * no_of_processes
       __lowerCAmelCase    :       Dict					     =[0] * no_of_processes
       __lowerCAmelCase    :       Dict					     =list(range(1, no_of_processes + 1))

       for i in range(no_of_processes):
              print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
              __lowerCAmelCase    :       Optional[Any]					     =map(int, input().split())

       __lowerCAmelCase    :       int					     =calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       __lowerCAmelCase    :       Dict					     =burst_time
       __lowerCAmelCase    :       Tuple					     =no_of_processes
       __lowerCAmelCase    :       Tuple					     =waiting_time
       __lowerCAmelCase    :       Tuple					     =calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       __lowerCAmelCase    :       Any					     =pd.DataFrame(
           list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
           columns=[
               """Process""",
               """BurstTime""",
               """ArrivalTime""",
               """WaitingTime""",
               """TurnAroundTime""",
           ],
       )

       # Printing the dataFrame
       pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
       print(fcfs)








"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




