





from statistics import mean

import numpy as np







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   list:
  __snake_case:						Union[str, Any]			  =						0
  # Number of processes finished
  __snake_case:						Tuple			  =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						str			  =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						str			  =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						str			  =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						List[Any]			  =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

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

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

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

  return turn_around_time







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   list:
  __snake_case:						int			  =						[0] * no_of_process
  for i in range(0 , SCREAMING_SNAKE_CASE__):
    __snake_case:						List[Any]			  =						turn_around_time[i] - burst_time[i]
  return waiting_time


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

      __UpperCAmelCase	:       Dict        =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase	:       str        =							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    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Optional[int]			  =						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    __snake_case	:





       '''simple docstring'''







       def __init__(  self			:   int							,    A			:   str							,    A			:   Dict=13							,    A			:   str=32							,    A			:   Any=2							,    A			:   Optional[Any]=3							,    A			:   str=640							,    A			:   Tuple=4							,    A			:   Dict="silu"							,    A			:   List[Any]=3							,    A			:   Any=32							,    A			:   Any=0.1							,    A			:   int=0.1							,    A			:   Dict=0.1							,    A			:   Optional[Any]=0.02							,    A			:   List[Any]=True							,    A			:   Tuple=True							,    A			:   Any=10							,    A			:   Optional[int]=None							,    ):
         __snake_case:						List[Any]			  =						parent
         __snake_case:						Dict			  =						batch_size
         __snake_case:						int			  =						image_size
         __snake_case:						Tuple			  =						patch_size
         __snake_case:						Tuple			  =						num_channels
         __snake_case:						str			  =						last_hidden_size
         __snake_case:						Dict			  =						num_attention_heads
         __snake_case:						Dict			  =						hidden_act
         __snake_case:						Tuple			  =						conv_kernel_size
         __snake_case:						List[str]			  =						output_stride
         __snake_case:						List[str]			  =						hidden_dropout_prob
         __snake_case:						Optional[Any]			  =						attention_probs_dropout_prob
         __snake_case:						int			  =						classifier_dropout_prob
         __snake_case:						List[Any]			  =						use_labels
         __snake_case:						Union[str, Any]			  =						is_training
         __snake_case:						Union[str, Any]			  =						num_labels
         __snake_case:						str			  =						initializer_range
         __snake_case:						List[Any]			  =						scope




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Union[str, Any]			  =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         __snake_case:						Tuple			  =						None
         __snake_case:						Any			  =						None
         if self.use_labels:
           __snake_case:						Union[str, Any]			  =						ids_tensor([self.batch_size]							,    self.num_labels		)
           __snake_case:						str			  =						ids_tensor([self.batch_size, self.image_size, self.image_size]							,    self.num_labels		)

         __snake_case:						Any			  =						self.get_config()

         return config, pixel_values, labels, pixel_labels




       def 							UpperCAmelCase__			(  self			:   int		):
         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 							UpperCAmelCase__			(  self			:   str							,    A			:   Optional[Any]							,    A			:   Any							,    A			:   Any							,    A			:   Union[str, Any]		):
         __snake_case:						List[Any]			  =						MobileViTModel(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						int			  =						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 							UpperCAmelCase__			(  self			:   str							,    A			:   List[Any]							,    A			:   Any							,    A			:   Any							,    A			:   int		):
         __snake_case:						str			  =						self.num_labels
         __snake_case:						Optional[int]			  =						MobileViTForImageClassification(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Union[str, Any]			  =						model(A							,    labels=A		)
         self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   str							,    A			:   Optional[Any]							,    A			:   int							,    A			:   str		):
         __snake_case:						List[Any]			  =						self.num_labels
         __snake_case:						Dict			  =						MobileViTForSemanticSegmentation(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Union[str, Any]			  =						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,
             )							,    )
         __snake_case:						Tuple			  =						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 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Tuple			  =						self.prepare_config_and_inputs()
         __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case:						Any			  =						config_and_inputs
         __snake_case:						Optional[int]			  =						{"""pixel_values""": pixel_values}
         return config, inputs_dict







@require_torch
class    __snake_case	(							__lowerCamelCase       ,      __lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					(
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__						=					(
           {
               """feature-extraction""": MobileViTModel,
               """image-classification""": MobileViTForImageClassification,
               """image-segmentation""": MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						List[Any]			  =						MobileViTModelTester(self		)
         __snake_case:						str			  =						MobileViTConfigTester(self							,    config_class=A							,    has_text_modality=A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         self.config_tester.run_common_tests()




       @unittest.skip(reason="""MobileViT does not use inputs_embeds"""		)
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         pass




       @unittest.skip(reason="""MobileViT does not support input and output embeddings"""		)
       def 							UpperCAmelCase__			(  self			:   Dict		):
         pass




       @unittest.skip(reason="""MobileViT does not output attentions"""		)
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         pass




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case  ,       __snake_case:						Dict			  =						self.model_tester.prepare_config_and_inputs_for_common()

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

           __snake_case:						List[Any]			  =						["""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 							UpperCAmelCase__			(  self			:   Optional[int]		):
         pass




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[Any]			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         def check_hidden_states_output(A			:   List[Any]							,    A			:   int							,    A			:   Tuple		):
           __snake_case:						List[str]			  =						model_class(A		)
           model.to(A		)
           model.eval()

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

           __snake_case:						Optional[int]			  =						outputs.hidden_states

           __snake_case:						Any			  =						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.
           __snake_case:						Union[str, Any]			  =						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		)

         __snake_case  ,       __snake_case:						List[Any]			  =						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           __snake_case:						Optional[Any]			  =						True
           check_hidden_states_output(A							,    A							,    A		)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __snake_case:						Dict			  =						True

           check_hidden_states_output(A							,    A							,    A		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Optional[Any]			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A		)




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						Optional[int]			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A		)






       @slow
       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:						List[Any]			  =						MobileViTModel.from_pretrained(A		)
           self.assertIsNotNone(A		)







def       A__							(   )       ->   Optional[int]:
  __snake_case:						Optional[Any]			  =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
  return image







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





       '''simple docstring'''







       @cached_property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""		) if is_vision_available() else None




       @slow
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Tuple			  =						MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""		).to(A		)

         __snake_case:						str			  =						self.default_image_processor
         __snake_case:						Optional[Any]			  =						prepare_img()
         __snake_case:						List[Any]			  =						image_processor(images=A							,    return_tensors="""pt"""		).to(A		)

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

         # verify the logits
         __snake_case:						List[str]			  =						torch.Size((1, 1_000)		)
         self.assertEqual(outputs.logits.shape							,    A		)

         __snake_case:						Union[str, Any]			  =						torch.tensor([-1.9364, -1.2327, -0.4653]		).to(A		)

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




       @slow
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Tuple			  =						MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)
         __snake_case:						List[str]			  =						model.to(A		)

         __snake_case:						Dict			  =						MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)

         __snake_case:						List[Any]			  =						prepare_img()
         __snake_case:						List[str]			  =						image_processor(images=A							,    return_tensors="""pt"""		).to(A		)

         # forward pass
         with torch.no_grad():
           __snake_case:						List[Any]			  =						model(**A		)
         __snake_case:						Optional[int]			  =						outputs.logits

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

         __snake_case:						Optional[int]			  =						torch.tensor(
             [
                 [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                 [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                 [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
             ]							,    device=A							,    )

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






       @slow
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						int			  =						MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)
         __snake_case:						str			  =						model.to(A		)

         __snake_case:						Optional[Any]			  =						MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""		)

         __snake_case:						List[str]			  =						prepare_img()
         __snake_case:						Optional[int]			  =						image_processor(images=A							,    return_tensors="""pt"""		).to(A		)

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

         __snake_case:						List[Any]			  =						outputs.logits.detach().cpu()

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

         __snake_case:						int			  =						image_processor.post_process_semantic_segmentation(outputs=A		)
         __snake_case:						Tuple			  =						torch.Size((32, 32)		)
         self.assertEqual(segmentation[0].shape							,    A		)






