



"""simple docstring"""
from __future__ import annotations

from statistics import mean

def    _snake_case		(		UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      List[str]			=			[0] * no_of_processes
 UpperCAmelCase			:      List[Any]			=			[0] * no_of_processes

 # Initialize remaining_time to waiting_time.

 for i in range(UpperCamelCase		):
  UpperCAmelCase			:      Optional[Any]			=			burst_time[i]
 UpperCAmelCase			:      list[int]			=			[]

 UpperCAmelCase			:      Optional[int]			=			0
 UpperCAmelCase			:      List[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:
  UpperCAmelCase			:      Union[str, Any]			=			[]
  UpperCAmelCase			:      Optional[int]			=			-1

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

  if len(UpperCamelCase		) > 0:
   UpperCAmelCase			:      str			=			ready_process[0]
   for i in ready_process:
    if remaining_time[i] < remaining_time[target_process]:
     UpperCAmelCase			:      List[str]			=			i
   total_time += burst_time[target_process]
   completed += 1
   UpperCAmelCase			:      List[Any]			=			0
   UpperCAmelCase			:      int			=			(
       total_time - arrival_time[target_process] - burst_time[target_process]
   )
  else:
   total_time += 1

 return waiting_time

def    _snake_case		(		UpperCamelCase		:	list[int]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	list[int]		):
 UpperCAmelCase			:      List[str]			=			[0] * no_of_processes
 for i in range(UpperCamelCase		):
  UpperCAmelCase			:      str			=			burst_time[i] + waiting_time[i]
 return turn_around_time


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

      A:     Optional[int]										=	4
      A:     Optional[int]										=	[2, 5, 3, 7]
      A:     Any										=	[0, 0, 0, 0]
      A:     str										=	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
      A:     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}""")





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A:     Union[str, Any]										=	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SpeechT5ForSpeechToText",
          "SpeechT5ForSpeechToSpeech",
          "SpeechT5ForTextToSpeech",
          "SpeechT5Model",
          "SpeechT5PreTrainedModel",
          "SpeechT5HifiGan",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
