






from statistics import mean

import numpy as np

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__		)							->  str:






						"""simple docstring"""





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

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

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

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

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

						return turn_around_time

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__		)							->  Any:






						"""simple docstring"""





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


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

		UpperCamelCase 				=	calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		UpperCamelCase 				=	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 List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





