



from statistics import mean

import numpy as np



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					UpperCamelCase_						     =      0
					# Number of processes finished
					UpperCamelCase_						     =      0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					UpperCamelCase_						     =      [0] * no_of_process
					# List to include calculation results
					UpperCamelCase_						     =      [0] * no_of_process

					# Sort by arrival time.
					UpperCamelCase_						     =      [burst_time[i] for i in np.argsort(_SCREAMING_SNAKE_CASE)]
					UpperCamelCase_						     =      [process_name[i] for i in np.argsort(_SCREAMING_SNAKE_CASE)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										UpperCamelCase_						     =      0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															UpperCamelCase_						     =      arrival_time[i]

										UpperCamelCase_						     =      0
										# Index showing the location of the process being performed
										UpperCamelCase_						     =      0
										# Saves the current response ratio.
										UpperCamelCase_						     =      0
										for i in range(0      ,		_SCREAMING_SNAKE_CASE):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				UpperCamelCase_						     =      (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				UpperCamelCase_						     =      temp
																				UpperCamelCase_						     =      i

																				# Calculate the turn around time
										UpperCamelCase_						     =      current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										UpperCamelCase_						     =      1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					UpperCamelCase_						     =      [0] * no_of_process
					for i in range(0      ,		_SCREAMING_SNAKE_CASE):
										UpperCamelCase_						     =      turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
	UpperCAmelCase      :							Union[str, Any]							       =5
	UpperCAmelCase      :							Union[str, Any]							       =["""A""", """B""", """C""", """D""", """E"""]
	UpperCAmelCase      :							Dict							       =[1, 2, 3, 4, 5]
	UpperCAmelCase      :							Optional[Any]							       =[1, 2, 3, 4, 5]

	UpperCAmelCase      :							List[Any]							       =calculate_turn_around_time(
	    process_name, arrival_time, burst_time, no_of_process
	)
	UpperCAmelCase      :							List[str]							       =calculate_waiting_time(
	    process_name, turn_around_time, burst_time, no_of_process
	)

	print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
	for i in range(0, no_of_process):
		print(
		    F"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
		    F"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
		)

	print(F"average waiting time : {mean(waiting_time):.5f}")
	print(F"average turn around time : {mean(turn_around_time):.5f}")







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A											=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  __A											=		[
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
