



from statistics import mean

import numpy as np






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		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 :		Tuple			    =       [0] * no_of_process

						# Sort by arrival time.
						_UpperCamelCase :		Union[str, Any]			    =       [burst_time[i] for i in np.argsort(_UpperCAmelCase							)]
						_UpperCamelCase :		int			    =       [process_name[i] for i in np.argsort(_UpperCAmelCase							)]
						arrival_time.sort()

						while no_of_process > finished_process_count:
												_UpperCamelCase :		List[Any]			    =       0
												while finished_process[i] == 1:
																		i += 1
												if current_time < arrival_time[i]:
																		_UpperCamelCase :		Optional[Any]			    =       arrival_time[i]

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

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

						return turn_around_time






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       [0] * no_of_process
						for i in range(0  ,  _UpperCAmelCase							):
												_UpperCamelCase :		Dict			    =       turn_around_time[i] - burst_time[i]
						return waiting_time


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  5
					snake_case_			:     Tuple  	=  ["A", "B", "C", "D", "E"]
					snake_case_			:     Union[str, Any]  	=  [1, 2, 3, 4, 5]
					snake_case_			:     str  	=  [1, 2, 3, 4, 5]

					snake_case_			:     int  	=  calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					snake_case_			:     Tuple  	=  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_			:     Optional[Any]  	=  {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPFeatureExtractor']
					snake_case_			:     Dict  	=  ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Tuple  	=  [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     str  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


