



from statistics import mean

import numpy as np





def 							A     (					_UpperCAmelCase    :       list							,       _UpperCAmelCase    :       list							,       _UpperCAmelCase    :       list							,       _UpperCAmelCase    :       int   )    ->			list:







			'''simple docstring'''
			_UpperCAmelCase			=       0
			# Number of processes finished
			_UpperCAmelCase			=       0
			# Displays the finished process.
			# If it is 0, the performance is completed if it is 1, before the performance.
			_UpperCAmelCase			=       [0] * no_of_process
			# List to include calculation results
			_UpperCAmelCase			=       [0] * no_of_process

			# Sort by arrival time.
			_UpperCAmelCase			=       [burst_time[i] for i in np.argsort(_UpperCAmelCase   )]
			_UpperCAmelCase			=       [process_name[i] for i in np.argsort(_UpperCAmelCase   )]
			arrival_time.sort()

			while no_of_process > finished_process_count:
						_UpperCAmelCase			=       0
						while finished_process[i] == 1:
									i += 1
						if current_time < arrival_time[i]:
									_UpperCAmelCase			=       arrival_time[i]

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

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

			return turn_around_time





def 							A     (					_UpperCAmelCase    :       list							,       _UpperCAmelCase    :       list							,       _UpperCAmelCase    :       list							,       _UpperCAmelCase    :       int   )    ->			list:







			'''simple docstring'''
			_UpperCAmelCase			=       [0] * no_of_process
			for i in range(0							,       _UpperCAmelCase   ):
						_UpperCAmelCase			=       turn_around_time[i] - burst_time[i]
			return waiting_time


if __name__ == "__main__":
							UpperCAmelCase__       =	5
							UpperCAmelCase__       =	["A", "B", "C", "D", "E"]
							UpperCAmelCase__       =	[1, 2, 3, 4, 5]
							UpperCAmelCase__       =	[1, 2, 3, 4, 5]

							UpperCAmelCase__       =	calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							UpperCAmelCase__       =	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 inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch

							from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
							from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor







class       __lowerCAmelCase   (      A						):



			def    _lowerCamelCase	(						self       :  List[str])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.config_class(**self.inputs_dict)
						self.parent.assertTrue(hasattr(A			,		'hidden_sizes'))
						self.parent.assertTrue(hasattr(A			,		'neck_hidden_sizes'))
						self.parent.assertTrue(hasattr(A			,		'num_attention_heads'))







class       __lowerCAmelCase   :



			def __init__(						self       :  int			,		A       :  Tuple			,		A       :  List[str]=13			,		A       :  List[str]=32			,		A       :  List[str]=2			,		A       :  List[str]=3			,		A       :  List[Any]=6_40			,		A       :  Any=4			,		A       :  int="silu"			,		A       :  int=3			,		A       :  Dict=32			,		A       :  List[Any]=0.1			,		A       :  Optional[Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  List[str]=0.0_2			,		A       :  int=True			,		A       :  Any=True			,		A       :  List[str]=10			,		A       :  Tuple=None			,		)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       image_size
						_UpperCAmelCase			=       patch_size
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       last_hidden_size
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       conv_kernel_size
						_UpperCAmelCase			=       output_stride
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       classifier_dropout_prob
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scope



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



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.num_labels)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.image_size, self.image_size]			,		self.num_labels)

						_UpperCAmelCase			=       self.get_config()

						return config, pixel_values, labels, pixel_labels



			def    _lowerCamelCase	(						self       :  str)       ->   int:



						"""simple docstring"""






						return MobileViTConfig(
						    image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		num_attention_heads=self.num_attention_heads			,		hidden_act=self.hidden_act			,		conv_kernel_size=self.conv_kernel_size			,		output_stride=self.output_stride			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		classifier_dropout_prob=self.classifier_dropout_prob			,		initializer_range=self.initializer_range			,		)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Dict			,		A       :  Tuple			,		A       :  int			,		A       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTModel(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,		(
						        self.batch_size,
						        self.last_hidden_size,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )			,		)



			def    _lowerCamelCase	(						self       :  int			,		A       :  Any			,		A       :  List[Any]			,		A       :  List[Any]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       MobileViTForImageClassification(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  int			,		A       :  Tuple			,		A       :  Optional[Any]			,		A       :  Union[str, Any]			,		A       :  List[Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       MobileViTForSemanticSegmentation(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(
						    result.logits.shape			,		(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )			,		)
						_UpperCAmelCase			=       model(A			,		labels=A)
						self.parent.assertEqual(
						    result.logits.shape			,		(
						        self.batch_size,
						        self.num_labels,
						        self.image_size // self.output_stride,
						        self.image_size // self.output_stride,
						    )			,		)



			def    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       config_and_inputs
						_UpperCAmelCase			=       {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': MobileViTModel,
			        '''image-classification''': MobileViTForImageClassification,
			        '''image-segmentation''': MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  str)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTModelTester(self)
						_UpperCAmelCase			=       MobileViTConfigTester(self			,		config_class=A			,		has_text_modality=A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			@unittest.skip(reason='MobileViT does not use inputs_embeds')
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						pass



			@unittest.skip(reason='MobileViT does not support input and output embeddings')
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						pass



			@unittest.skip(reason='MobileViT does not output attentions')
			def    _lowerCamelCase	(						self       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       model_class(A)
									_UpperCAmelCase			=       inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_UpperCAmelCase			=       [*signature.parameters.keys()]

									_UpperCAmelCase			=       ['pixel_values']
									self.assertListEqual(arg_names[:1]			,		A)



			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Any:



						"""simple docstring"""






						def check_hidden_states_output(A       :  List[str]			,		A       :  Union[str, Any]			,		A       :  int):
									_UpperCAmelCase			=       model_class(A)
									model.to(A)
									model.eval()

									with torch.no_grad():
												_UpperCAmelCase			=       model(**self._prepare_for_class(A			,		A))

									_UpperCAmelCase			=       outputs.hidden_states

									_UpperCAmelCase			=       5
									self.assertEqual(len(A)			,		A)

									# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									_UpperCAmelCase			=       2
									for i in range(len(A)):
												self.assertListEqual(
												    list(hidden_states[i].shape[-2:])			,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,		)
												divisor *= 2

									self.assertEqual(self.model_tester.output_stride			,		divisor // 2)

						_UpperCAmelCase	,					_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase			=       True
									check_hidden_states_output(A			,		A			,		A)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									_UpperCAmelCase			=       True

									check_hidden_states_output(A			,		A			,		A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A)



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase			=       MobileViTModel.from_pretrained(A)
									self.assertIsNotNone(A)





def 							A     (					)    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
			return image







@require_torch
@require_vision
class       __lowerCAmelCase   (      unittest.TestCase						):



			@cached_property
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None



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



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(A)

						_UpperCAmelCase			=       self.default_image_processor
						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)

						# verify the logits
						_UpperCAmelCase			=       torch.Size((1, 10_00))
						self.assertEqual(outputs.logits.shape			,		A)

						_UpperCAmelCase			=       torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]).to(A)

						self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		A			,		atol=1E-4))



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



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
						_UpperCAmelCase			=       model.to(A)

						_UpperCAmelCase			=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)
						_UpperCAmelCase			=       outputs.logits

						# verify the logits
						_UpperCAmelCase			=       torch.Size((1, 21, 32, 32))
						self.assertEqual(logits.shape			,		A)

						_UpperCAmelCase			=       torch.tensor(
						    [
						        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
						        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
						        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
						    ]			,		device=A			,		)

						self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,		A			,		atol=1E-4))



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



						"""simple docstring"""






						_UpperCAmelCase			=       MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
						_UpperCAmelCase			=       model.to(A)

						_UpperCAmelCase			=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

						_UpperCAmelCase			=       prepare_img()
						_UpperCAmelCase			=       image_processor(images=A			,		return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase			=       model(**A)

						_UpperCAmelCase			=       outputs.logits.detach().cpu()

						_UpperCAmelCase			=       image_processor.post_process_semantic_segmentation(outputs=A			,		target_sizes=[(50, 60)])
						_UpperCAmelCase			=       torch.Size((50, 60))
						self.assertEqual(segmentation[0].shape			,		A)

						_UpperCAmelCase			=       image_processor.post_process_semantic_segmentation(outputs=A)
						_UpperCAmelCase			=       torch.Size((32, 32))
						self.assertEqual(segmentation[0].shape			,		A)
