




from statistics import mean

import numpy as np




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list		,						_lowerCamelCase				:    list		,						_lowerCamelCase				:    list		,						_lowerCamelCase				:    int)      ->					list:






							'''simple docstring'''





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

							# Sort by arrival time.
							__UpperCamelCase			:     int         =      [burst_time[i] for i in np.argsort(_lowerCamelCase)]
							__UpperCamelCase			:     Dict         =      [process_name[i] for i in np.argsort(_lowerCamelCase)]
							arrival_time.sort()

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

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

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

							return turn_around_time




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list		,						_lowerCamelCase				:    list		,						_lowerCamelCase				:    list		,						_lowerCamelCase				:    int)      ->					list:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [0] * no_of_process
							for i in range(0		,						_lowerCamelCase):
														__UpperCamelCase			:     Optional[Any]         =      turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
		lowercase						:      Any									= 5
		lowercase						:      List[str]									= ['A', 'B', 'C', 'D', 'E']
		lowercase						:      str									= [1, 2, 3, 4, 5]
		lowercase						:      Optional[int]									= [1, 2, 3, 4, 5]

		lowercase						:      Dict									= calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		lowercase						:      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}")

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)