


"""simple docstring"""






import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    LayoutLMForMaskedLM,
					    LayoutLMForQuestionAnswering,
					    LayoutLMForSequenceClassification,
					    LayoutLMForTokenClassification,
					    LayoutLMModel,
					)


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=13	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=99	,					lowerCamelCase__=32	,					lowerCamelCase__=5	,					lowerCamelCase__=4	,					lowerCamelCase__=37	,					lowerCamelCase__="gelu"	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=16	,					lowerCamelCase__=2	,					lowerCamelCase__=0.02	,					lowerCamelCase__=3	,					lowerCamelCase__=4	,					lowerCamelCase__=None	,					lowerCamelCase__=1_000	,					)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Dict		=      parent
								snake_case__  :						List[Any]		=      batch_size
								snake_case__  :						List[Any]		=      seq_length
								snake_case__  :						Dict		=      is_training
								snake_case__  :						Optional[int]		=      use_input_mask
								snake_case__  :						Any		=      use_token_type_ids
								snake_case__  :						Optional[int]		=      use_labels
								snake_case__  :						List[Any]		=      vocab_size
								snake_case__  :						int		=      hidden_size
								snake_case__  :						Any		=      num_hidden_layers
								snake_case__  :						Optional[Any]		=      num_attention_heads
								snake_case__  :						Optional[Any]		=      intermediate_size
								snake_case__  :						Optional[int]		=      hidden_act
								snake_case__  :						Dict		=      hidden_dropout_prob
								snake_case__  :						Dict		=      attention_probs_dropout_prob
								snake_case__  :						List[str]		=      max_position_embeddings
								snake_case__  :						Union[str, Any]		=      type_vocab_size
								snake_case__  :						Optional[Any]		=      type_sequence_label_size
								snake_case__  :						List[str]		=      initializer_range
								snake_case__  :						Any		=      num_labels
								snake_case__  :						Dict		=      num_choices
								snake_case__  :						str		=      scope
								snake_case__  :						Optional[Any]		=      range_bbox




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[str]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[Any]		=      ids_tensor([self.batch_size, self.seq_length, 4]	,					self.range_bbox)
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]):
									for j in range(bbox.shape[1]):
										if bbox[i, j, 3] < bbox[i, j, 1]:
											snake_case__  :						Tuple		=      bbox[i, j, 3]
											snake_case__  :						Tuple		=      bbox[i, j, 1]
											snake_case__  :						Union[str, Any]		=      t
										if bbox[i, j, 2] < bbox[i, j, 0]:
											snake_case__  :						List[str]		=      bbox[i, j, 2]
											snake_case__  :						Union[str, Any]		=      bbox[i, j, 0]
											snake_case__  :						Union[str, Any]		=      t

								snake_case__  :						Optional[int]		=      None
								if self.use_input_mask:
									snake_case__  :						Optional[Any]		=      ids_tensor([self.batch_size, self.seq_length]	,					vocab_size=2)

								snake_case__  :						Optional[int]		=      None
								if self.use_token_type_ids:
									snake_case__  :						Any		=      ids_tensor([self.batch_size, self.seq_length]	,					self.type_vocab_size)

								snake_case__  :						Union[str, Any]		=      None
								snake_case__  :						List[str]		=      None
								snake_case__  :						Union[str, Any]		=      None
								if self.use_labels:
									snake_case__  :						Tuple		=      ids_tensor([self.batch_size]	,					self.type_sequence_label_size)
									snake_case__  :						Optional[Any]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.num_labels)
									snake_case__  :						Optional[Any]		=      ids_tensor([self.batch_size]	,					self.num_choices)

								snake_case__  :						Union[str, Any]		=      self.get_config()

								return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								return LayoutLMConfig(
								    vocab_size=self.vocab_size	,					hidden_size=self.hidden_size	,					num_hidden_layers=self.num_hidden_layers	,					num_attention_heads=self.num_attention_heads	,					intermediate_size=self.intermediate_size	,					hidden_act=self.hidden_act	,					hidden_dropout_prob=self.hidden_dropout_prob	,					attention_probs_dropout_prob=self.attention_probs_dropout_prob	,					max_position_embeddings=self.max_position_embeddings	,					type_vocab_size=self.type_vocab_size	,					initializer_range=self.initializer_range	,					)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      LayoutLMModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Any		=      model(lowerCamelCase__	,					lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__)
								snake_case__  :						Optional[int]		=      model(lowerCamelCase__	,					lowerCamelCase__	,					token_type_ids=lowerCamelCase__)
								snake_case__  :						int		=      model(lowerCamelCase__	,					lowerCamelCase__)
								self.parent.assertEqual(result.last_hidden_state.shape	,					(self.batch_size, self.seq_length, self.hidden_size))
								self.parent.assertEqual(result.pooler_output.shape	,					(self.batch_size, self.hidden_size))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						str		=      LayoutLMForMaskedLM(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Any		=      model(lowerCamelCase__	,					lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					labels=lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.vocab_size))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						str		=      self.num_labels
								snake_case__  :						Any		=      LayoutLMForSequenceClassification(lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Union[str, Any]		=      model(
								    lowerCamelCase__	,					lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					labels=lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_labels))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						int		=      self.num_labels
								snake_case__  :						Optional[int]		=      LayoutLMForTokenClassification(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Any		=      model(lowerCamelCase__	,					lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					labels=lowerCamelCase__)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.num_labels))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Any		=      LayoutLMForQuestionAnswering(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Tuple		=      model(
								    lowerCamelCase__	,					bbox=lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					start_positions=lowerCamelCase__	,					end_positions=lowerCamelCase__	,					)
								self.parent.assertEqual(result.start_logits.shape	,					(self.batch_size, self.seq_length))
								self.parent.assertEqual(result.end_logits.shape	,					(self.batch_size, self.seq_length))







							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						str		=      self.prepare_config_and_inputs()
								(
								    (
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					
								)  :						Optional[int]		=      config_and_inputs
								snake_case__  :						Tuple		=      {
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     str				  =  (
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							__magic_name__					:     int				  =  (
							    {
							        '''document-question-answering''': LayoutLMForQuestionAnswering,
							        '''feature-extraction''': LayoutLMModel,
							        '''fill-mask''': LayoutLMForMaskedLM,
							        '''text-classification''': LayoutLMForSequenceClassification,
							        '''token-classification''': LayoutLMForTokenClassification,
							        '''zero-shot''': LayoutLMForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__magic_name__					:     List[Any]				  =  True




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      LayoutLMModelTester(self)
								snake_case__  :						List[Any]		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					hidden_size=37)




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Any		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									snake_case__  :						Dict		=      type
									self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__)







							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								pass



def 			A__		(					)							->	str:

	'''simple docstring'''


	snake_case__  :						List[str]		=      torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]				,    device=_UpperCAmelCase	)  # noqa: E231
	snake_case__  :						Dict		=      torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]				,    device=_UpperCAmelCase	)  # noqa: E231
	snake_case__  :						List[Any]		=      torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]				,    device=_UpperCAmelCase	)  # noqa: E231
	snake_case__  :						Any		=      torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]				,    device=_UpperCAmelCase	)  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	snake_case__  :						int		=      torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]				,    device=_UpperCAmelCase	)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels



@require_torch
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@slow
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(lowerCamelCase__)

								snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						Any		=      prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case__  :						Any		=      model(input_ids=lowerCamelCase__	,					bbox=lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__)

								# test the sequence output on [0, :3, :3]
								snake_case__  :						Optional[Any]		=      torch.tensor(
								    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]	,					device=lowerCamelCase__	,					)

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,					lowerCamelCase__	,					atol=1E-3))

								# test the pooled output on [1, :3]
								snake_case__  :						Union[str, Any]		=      torch.tensor([-0.65_80, -0.02_14, 0.85_52]	,					device=lowerCamelCase__)

								self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]	,					lowerCamelCase__	,					atol=1E-3))




							@slow
							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,					num_labels=2).to(
								    lowerCamelCase__)

								snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						int		=      prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case__  :						List[Any]		=      model(
								    input_ids=lowerCamelCase__	,					bbox=lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					labels=torch.tensor([1, 1]	,					device=lowerCamelCase__)	,					)

								# test whether we get a loss as a scalar
								snake_case__  :						Optional[int]		=      outputs.loss
								snake_case__  :						int		=      torch.Size([])
								self.assertEqual(loss.shape	,					lowerCamelCase__)

								# test the shape of the logits
								snake_case__  :						int		=      outputs.logits
								snake_case__  :						str		=      torch.Size((2, 2))
								self.assertEqual(logits.shape	,					lowerCamelCase__)




							@slow
							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						str		=      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	,					num_labels=13).to(
								    lowerCamelCase__)

								snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						Union[str, Any]		=      prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case__  :						Tuple		=      model(
								    input_ids=lowerCamelCase__	,					bbox=lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__	,					labels=lowerCamelCase__)

								# test the loss calculation to be around 2.65
								# expected_loss = torch.tensor(2.65, device=torch_device)

								# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
								# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

								# test the shape of the logits
								snake_case__  :						Tuple		=      outputs.logits
								snake_case__  :						Dict		=      torch.Size((2, 25, 13))
								self.assertEqual(logits.shape	,					lowerCamelCase__)







							@slow
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Any		=      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(lowerCamelCase__)

								snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						str		=      prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case__  :						int		=      model(input_ids=lowerCamelCase__	,					bbox=lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__)

								# test the shape of the logits
								snake_case__  :						Any		=      torch.Size((2, 25))
								self.assertEqual(outputs.start_logits.shape	,					lowerCamelCase__)
								self.assertEqual(outputs.end_logits.shape	,					lowerCamelCase__)






from __future__ import annotations

from statistics import mean


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]					):
       _UpperCamelCase  :							str	    =	2
       _UpperCamelCase  :							Optional[int]	    =	list(lowercase_					)
       _UpperCamelCase  :							Union[str, Any]	    =	[0] * len(lowercase_					)
       _UpperCamelCase  :							List[str]	    =	0
       while True:
              _UpperCamelCase  :							List[str]	    =	True
              for i, burst_time in enumerate(lowercase_					):
                     if rem_burst_times[i] > 0:
                            _UpperCamelCase  :							List[str]	    =	False
                            if rem_burst_times[i] > quantum:
                                   t += quantum
                                   rem_burst_times[i] -= quantum
                            else:
                                   t += rem_burst_times[i]
                                   _UpperCamelCase  :							str	    =	t - burst_time
                                   _UpperCamelCase  :							Union[str, Any]	    =	0
              if done is True:
                     return waiting_times


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]		,    lowercase_:						list[int]					):
       return [burst + waiting for burst, waiting in zip(lowercase_		,    lowercase_					)]


if __name__ == "__main__":
 UpperCAmelCase      		=					[3, 5, 7]
 UpperCAmelCase      		=					calculate_waiting_times(burst_times)
 UpperCAmelCase      		=					calculate_turn_around_times(burst_times, waiting_times)
 print('''Process ID \tBurst Time \tWaiting Time \tTurnaround Time''')
 for i, burst_time in enumerate(burst_times):
  print(
      f'''  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '''
      f'''{turn_around_times[i]}'''
  )
 print(f'''\nAverage waiting time = {mean(waiting_times):.5f}''')
 print(f'''Average turn around time = {mean(turn_around_times):.5f}''')



