import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
     from transformers.models.mobilevitva.modeling_mobilevitva import (
         MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
         make_divisible,
     )


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor



class     _A   (    __UpperCamelCase							):






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





       '''simple docstring'''





       UpperCamelCase__										=      self.config_class(**self.inputs_dict	)
       self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,				'''width_multiplier'''	)	)



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=64   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_="swish"   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=10   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=0.25   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      make_divisible(512 * width_multiplier   ,				divisor=8	)
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      conv_kernel_size
       UpperCamelCase__										=      output_stride
       UpperCamelCase__										=      classifier_dropout_prob
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      num_labels
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      scope
       UpperCamelCase__										=      width_multiplier
       UpperCamelCase__										=      ffn_dropout
       UpperCamelCase__										=      attn_dropout






  def 		_a					(self	)		->      Tuple:





       '''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 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       return MobileViTVaConfig(
           image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				hidden_act=self.hidden_act   ,				conv_kernel_size=self.conv_kernel_size   ,				output_stride=self.output_stride   ,				classifier_dropout_prob=self.classifier_dropout_prob   ,				initializer_range=self.initializer_range   ,				width_multiplier=self.width_multiplier   ,				ffn_dropout=self.ffn_dropout_prob   ,				attn_dropout=self.attn_dropout_prob   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTVaModel(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       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(SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       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 		_a					(self	)		->      str:





       '''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     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						=(
      (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )

  SCREAMING_SNAKE_CASE_	:	List[str]						=(
      {
          "feature-extraction": MobileViTVaModel,
          "image-classification": MobileViTVaForImageClassification,
          "image-segmentation": MobileViTVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE_	:	Tuple						=False
  SCREAMING_SNAKE_CASE_	:	str						=False
  SCREAMING_SNAKE_CASE_	:	Any						=False
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=False






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





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTVaModelTester(self	)
       UpperCamelCase__										=      MobileViTVaConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				has_text_modality=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''	)
  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       pass






  @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''	)
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






  @unittest.skip(reason='''MobileViTV2 does not output attentions'''	)
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass






  @require_torch_multi_gpu
  @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''	)
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            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]   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       def check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            model.to(SCREAMING_SNAKE_CASE_	)
            model.eval()

            with torch.no_grad():
                 UpperCamelCase__										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)

            UpperCamelCase__										=      outputs.hidden_states

            UpperCamelCase__										=      5
            self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)

            # MobileViTV2'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(SCREAMING_SNAKE_CASE_	)	):
                 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(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

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

            check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__										=      MobileViTVaModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image



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






  @cached_property
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       return (
           MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''	)
           if is_vision_available()
           else None
       )






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





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''	).to(
           SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 1000)	)
       self.assertEqual(outputs.logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]	).to(SCREAMING_SNAKE_CASE_	)

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






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





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''	)
       UpperCamelCase__										=      model.to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''	)

       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs.logits

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

       UpperCamelCase__										=      torch.tensor(
           [
               [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
               [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
               [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
           ]   ,				device=SCREAMING_SNAKE_CASE_   ,				)

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






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





       '''simple docstring'''





       UpperCamelCase__										=      MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''	)
       UpperCamelCase__										=      model.to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''	)

       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

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

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

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




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A							):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         UpperCamelCase__										=      remaining_time[j]
                         UpperCamelCase__										=      j
                         UpperCamelCase__										=      True

          if not check:
               increment_time += 1
               continue
          remaining_time[short] -= 1

          UpperCamelCase__										=      remaining_time[short]
          if minm == 0:
               UpperCamelCase__										=      999999999

          if remaining_time[short] == 0:
               complete += 1
               UpperCamelCase__										=      False

               # Find finish time of current process
               UpperCamelCase__										=      increment_time + 1

               # Calculate waiting time
               UpperCamelCase__										=      finish_time - arrival_time[short]
               UpperCamelCase__										=      finar - burst_time[short]

               if waiting_time[short] < 0:
                    UpperCamelCase__										=      0

        # Increment time
          increment_time += 1
     return waiting_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          UpperCamelCase__										=      total_waiting_time + waiting_time[i]
          UpperCamelCase__										=      total_turn_around_time + turn_around_time[i]
     print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}"							)
     print('''Average turn around time =''' ,							total_turn_around_time / no_of_processes							)


if __name__ == "__main__":
     print('''Enter how many process you want to analyze''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=list(range(1, no_of_processes + 1))

     for i in range(no_of_processes):
          print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     __magic_name__								=burst_time
     __magic_name__								=no_of_processes
     __magic_name__								=waiting_time
     __magic_name__								=calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=pd.DataFrame(
         list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
         columns=[
             '''Process''',
             '''BurstTime''',
             '''ArrivalTime''',
             '''WaitingTime''',
             '''TurnAroundTime''',
         ],
     )

     # Printing the dataFrame
     pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
     print(fcfs)



