




'''simple docstring'''





from __future__ import annotations


def   UpperCamelCase_(			snake_case							: list[int]  ):

							'''simple docstring'''






							snake_case_			      =   [0] * len(snake_case  )
							for i in range(1							,       len(snake_case  )  ):
														snake_case_			      =   duration_times[i - 1] + waiting_times[i - 1]
							return waiting_times


def   UpperCamelCase_(			snake_case							: list[int]							,       snake_case							: list[int]  ):

							'''simple docstring'''






							return [
							    duration_time + waiting_times[i]
							    for i, duration_time in enumerate(snake_case  )
							]


def   UpperCamelCase_(			snake_case							: list[int]  ):

							'''simple docstring'''






							return sum(snake_case  ) / len(snake_case  )


def   UpperCamelCase_(			snake_case							: list[int]  ):

							'''simple docstring'''






							return sum(snake_case  ) / len(snake_case  )


if __name__ == "__main__":
		# process id's
		_SCREAMING_SNAKE_CASE							:     str   	=				[1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print("Zero amount of processes")
				raise SystemExit(0)

		# duration time of all processes
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				[19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print("Unable to match all id's with their duration time")
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		_SCREAMING_SNAKE_CASE							:     Dict   	=				calculate_waiting_times(duration_times)
		_SCREAMING_SNAKE_CASE							:     Any   	=				calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				calculate_average_waiting_time(waiting_times)
		_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
		for i, process in enumerate(processes):
				print(
				    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
				    F"{turnaround_times[i]}"
				)
		print(F"Average waiting time = {average_waiting_time}")
		print(F"Average turn around time = {average_turnaround_time}")










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




