



"""simple docstring"""
from statistics import mean

import numpy as np



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

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

		while no_of_process > finished_process_count:
				a_     :      Union[str, Any] 						=     0
				while finished_process[i] == 1:
						i += 1
				if current_time < arrival_time[i]:
						a_     :      Dict 						=     arrival_time[i]

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

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

		return turn_around_time



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      list:
		a_     :      Optional[Any] 						=     [0] * no_of_process
		for i in range(0, SCREAMING_SNAKE_CASE__	):
				a_     :      List[str] 						=     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}""")




"""simple docstring"""
import warnings
from typing import List, Optional, Union

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




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor