






from statistics import mean

import numpy as np

def    UpperCamelCase						(	_A,   _A,   _A,   _A			):






						"""simple docstring"""







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

						# Sort by arrival time.
						__magic_name__      :       List[str]       						= [burst_time[i] for i in np.argsort(_A			)]
						__magic_name__      :       Optional[int]       						= [process_name[i] for i in np.argsort(_A			)]
						arrival_time.sort()

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

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

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

						return turn_around_time

def    UpperCamelCase						(	_A,   _A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       Optional[Any]       						= [0] * no_of_process
						for i in range(0,   _A			):
												__magic_name__      :       List[Any]       						= turn_around_time[i] - burst_time[i]
						return waiting_time


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

		__magic_name__:					int							=     calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		__magic_name__:					Union[str, Any]							=     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}""")








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
