



"""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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

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