
from __future__ import annotations

from statistics import mean


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Tuple					=		[0] * no_of_processes
  __SCREAMING_SNAKE_CASE      :	List[str]					=		[0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(snake_case							):
    __SCREAMING_SNAKE_CASE      :	Dict					=		burst_time[i]
  __SCREAMING_SNAKE_CASE      :	list[int]					=		[]

  __SCREAMING_SNAKE_CASE      :	List[Any]					=		0
  __SCREAMING_SNAKE_CASE      :	Optional[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:
    __SCREAMING_SNAKE_CASE      :	Dict					=		[]
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		-1

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

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

  return waiting_time


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Any					=		[0] * no_of_processes
  for i in range(snake_case							):
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		burst_time[i] + waiting_time[i]
  return turn_around_time


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

   lowercase_ 				=			4
   lowercase_ 				=			[2, 5, 3, 7]
   lowercase_ 				=			[0, 0, 0, 0]
   lowercase_ 				=			calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowercase_ 				=			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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		[
							    '''MT5EncoderModel''',
							    '''MT5ForConditionalGeneration''',
							    '''MT5ForQuestionAnswering''',
							    '''MT5Model''',
							    '''MT5PreTrainedModel''',
							    '''MT5Stack''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				str									=		['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		_LazyModule(
							    __name__,
							    globals()['''__file__'''],
							    _import_structure,
							    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
							    module_spec=__spec__,
							)





