





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{
    '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:
							_SCREAMING_SNAKE_CASE      	=		[
							    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecAudioForAudioFrameClassification',
							    'Data2VecAudioForCTC',
							    'Data2VecAudioForSequenceClassification',
							    'Data2VecAudioForXVector',
							    'Data2VecAudioModel',
							    'Data2VecAudioPreTrainedModel',
							]
							_SCREAMING_SNAKE_CASE      	=		[
							    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecTextForCausalLM',
							    'Data2VecTextForMaskedLM',
							    'Data2VecTextForMultipleChoice',
							    'Data2VecTextForQuestionAnswering',
							    'Data2VecTextForSequenceClassification',
							    'Data2VecTextForTokenClassification',
							    'Data2VecTextModel',
							    'Data2VecTextPreTrainedModel',
							]
							_SCREAMING_SNAKE_CASE      	=		[
							    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecVisionForImageClassification',
							    'Data2VecVisionForMaskedImageModeling',
							    'Data2VecVisionForSemanticSegmentation',
							    'Data2VecVisionModel',
							    'Data2VecVisionPreTrainedModel',
							]

if is_tf_available():
							_SCREAMING_SNAKE_CASE      	=		[
							    '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

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








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

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

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

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

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		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}''')

