
'''simple docstring'''
import unittest

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


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel






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





	@slow
	def        snake_case__		(    self):
					snake_case_ :    int     			=						XLMRobertaModel.from_pretrained("xlm-roberta-base")
					snake_case_ :    Dict     			=						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]])
					# The dog is cute and lives in the garden house

					snake_case_ :    List[Any]     			=						torch.Size((1, 12, 7_68))  # batch_size, sequence_length, embedding_vector_dim
					snake_case_ :    int     			=						torch.tensor(
					    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]])
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
									snake_case_ :    int     			=						model(_SCREAMING_SNAKE_CASE)["last_hidden_state"].detach()
					self.assertEqual(output.shape ,       _SCREAMING_SNAKE_CASE)
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1] ,       _SCREAMING_SNAKE_CASE ,       atol=1E-3))





	@slow
	def        snake_case__		(    self):
					snake_case_ :    int     			=						XLMRobertaModel.from_pretrained("xlm-roberta-large")
					snake_case_ :    Optional[Any]     			=						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]])
					# The dog is cute and lives in the garden house

					snake_case_ :    int     			=						torch.Size((1, 12, 10_24))  # batch_size, sequence_length, embedding_vector_dim
					snake_case_ :    int     			=						torch.tensor(
					    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]])
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
									snake_case_ :    Union[str, Any]     			=						model(_SCREAMING_SNAKE_CASE)["last_hidden_state"].detach()
					self.assertEqual(output.shape ,       _SCREAMING_SNAKE_CASE)
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1] ,       _SCREAMING_SNAKE_CASE ,       atol=1E-3))






'''simple docstring'''
from __future__ import annotations

from statistics import mean

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						[0] * no_of_processes
    snake_case_ :    Optional[int]     			=						[0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    str     			=						burst_time[i]
    snake_case_ :    list[int]     			=						[]

    snake_case_ :    Tuple     			=						0
    snake_case_ :    Optional[int]     			=						0

    # When processes are not completed,
    # A process whose arrival time has passed \
    # and has remaining execution time is put into the ready_process.
    # The shortest process in the ready_process, target_process is executed.

    while completed != no_of_processes:
        snake_case_ :    Any     			=						[]
        snake_case_ :    Dict     			=						-1

        for i in range(__SCREAMING_SNAKE_CASE       ):
            if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                ready_process.append(__SCREAMING_SNAKE_CASE       )

        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Dict     			=						ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    snake_case_ :    Optional[int]     			=						i
            total_time += burst_time[target_process]
            completed += 1
            snake_case_ :    Tuple     			=						0
            snake_case_ :    int     			=						(
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						[0] * no_of_processes
    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Dict     			=						burst_time[i] + waiting_time[i]
    return turn_around_time


if __name__ == "__main__":
  print("[TEST CASE 01]")

  a_											=						4
  a_											=						[2, 5, 3, 7]
  a_											=						[0, 0, 0, 0]
  a_											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  a_											=						calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
        f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
    )
  print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
  print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")




