






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  _lowercase   (   _A						):
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =self.config_class(**self.inputs_dict   )
       self.parent.assertTrue(hasattr(a				,			"""width_multiplier"""   )   )







class  _lowercase   :
      def __init__(     self				,			a				,			a=1_3				,			a=6_4				,			a=2				,			a=3				,			a="swish"				,			a=3				,			a=3_2				,			a=0.1				,			a=0.02				,			a=True				,			a=True				,			a=1_0				,			a=None				,			a=0.25				,			a=0.0				,			a=0.0				,			):
       snake_case__  :    int							       =parent
       snake_case__  :    int							       =batch_size
       snake_case__  :    Any							       =image_size
       snake_case__  :    Dict							       =patch_size
       snake_case__  :    List[str]							       =num_channels
       snake_case__  :    str							       =make_divisible(5_1_2 * width_multiplier				,			divisor=8   )
       snake_case__  :    Dict							       =hidden_act
       snake_case__  :    List[str]							       =conv_kernel_size
       snake_case__  :    Tuple							       =output_stride
       snake_case__  :    Tuple							       =classifier_dropout_prob
       snake_case__  :    Any							       =use_labels
       snake_case__  :    List[str]							       =is_training
       snake_case__  :    Union[str, Any]							       =num_labels
       snake_case__  :    Optional[int]							       =initializer_range
       snake_case__  :    Optional[Any]							       =scope
       snake_case__  :    Optional[Any]							       =width_multiplier
       snake_case__  :    Any							       =ffn_dropout
       snake_case__  :    Dict							       =attn_dropout
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       snake_case__  :    int							       =None
       snake_case__  :    List[Any]							       =None
       if self.use_labels:
        snake_case__  :    Optional[Any]							       =ids_tensor([self.batch_size]				,			self.num_labels   )
        snake_case__  :    Tuple							       =ids_tensor([self.batch_size, self.image_size, self.image_size]				,			self.num_labels   )

       snake_case__  :    Optional[Any]							       =self.get_config()

       return config, pixel_values, labels, pixel_labels
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a				,			a				,			a   ):
       snake_case__  :    Any							       =MobileViTVaModel(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    Union[str, Any]							       =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 						lowercase__					(     self				,			a				,			a				,			a				,			a   ):
       snake_case__  :    Optional[int]							       =self.num_labels
       snake_case__  :    List[Any]							       =MobileViTVaForImageClassification(a   )
       model.to(a   )
       model.eval()
       snake_case__  :    List[Any]							       =model(a				,			labels=a   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a   ):
       snake_case__  :    Any							       =self.num_labels
       snake_case__  :    List[Any]							       =MobileViTVaForSemanticSegmentation(a   )
       model.to(a   )
       model.eval()
       snake_case__  :    Tuple							       =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__  :    str							       =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 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Any							       =config_and_inputs
       snake_case__  :    int							       ={"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class  _lowercase   (   _A						,				_A						,				unittest.TestCase						):
      _a							:						Tuple										=							(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      _a							:						Dict										=							(
          {
              'feature-extraction': MobileViTVaModel,
              'image-classification': MobileViTVaForImageClassification,
              'image-segmentation': MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _a							:						List[str]										=							False
      _a							:						str										=							False
      _a							:						Optional[int]										=							False
      _a							:						Any										=							False
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =MobileViTVaModelTester(self   )
       snake_case__  :    Optional[Any]							       =MobileViTVaConfigTester(self				,			config_class=a				,			has_text_modality=a   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      @unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""MobileViTV2 does not output attentions"""   )
      def 						lowercase__					(     self   ):
       pass
      @require_torch_multi_gpu
      @unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       snake_case__       ,   snake_case__  :    Union[str, Any]							       =self.model_tester.prepare_config_and_inputs_for_common()

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

        snake_case__  :    Tuple							       =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,			a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       def check_hidden_states_output(a				,			a				,			a   ):
        snake_case__  :    Any							       =model_class(a   )
        model.to(a   )
        model.eval()

        with torch.no_grad():
         snake_case__  :    Optional[Any]							       =model(**self._prepare_for_class(a				,			a   )   )

        snake_case__  :    Optional[Any]							       =outputs.hidden_states

        snake_case__  :    int							       =5
        self.assertEqual(len(a   )				,			a   )

        # MobileViTV2'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[str]							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    Union[str, 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__  :    str							       =True

        check_hidden_states_output(a				,			a				,			a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*a   )



      @slow
      def 						lowercase__					(     self   ):
       for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    List[str]							       =MobileViTVaModel.from_pretrained(a   )
        self.assertIsNotNone(a   )







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
 return image







@require_torch
@require_vision
class  _lowercase   (   unittest.TestCase						):
      @cached_property
      def 						lowercase__					(     self   ):
       return (
           MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""   )
           if is_vision_available()
           else None
       )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""   ).to(
           a   )

       snake_case__  :    Tuple							       =self.default_image_processor
       snake_case__  :    Tuple							       =prepare_img()
       snake_case__  :    Tuple							       =image_processor(images=a				,			return_tensors="""pt"""   ).to(a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    Any							       =model(**a   )

       # verify the logits
       snake_case__  :    Any							       =torch.Size((1, 1_0_0_0)   )
       self.assertEqual(outputs.logits.shape				,			a   )

       snake_case__  :    Optional[int]							       =torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]   ).to(a   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			a				,			atol=1e-4   )   )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""   )
       snake_case__  :    Union[str, Any]							       =model.to(a   )

       snake_case__  :    Dict							       =MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""   )

       snake_case__  :    Optional[Any]							       =prepare_img()
       snake_case__  :    str							       =image_processor(images=a				,			return_tensors="""pt"""   ).to(a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    List[str]							       =model(**a   )
       snake_case__  :    Tuple							       =outputs.logits

       # verify the logits
       snake_case__  :    List[str]							       =torch.Size((1, 2_1, 3_2, 3_2)   )
       self.assertEqual(logits.shape				,			a   )

       snake_case__  :    Optional[Any]							       =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=a				,			)

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



      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""   )
       snake_case__  :    List[str]							       =model.to(a   )

       snake_case__  :    int							       =MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""   )

       snake_case__  :    Dict							       =prepare_img()
       snake_case__  :    int							       =image_processor(images=a				,			return_tensors="""pt"""   ).to(a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    List[Any]							       =model(**a   )

       snake_case__  :    Tuple							       =outputs.logits.detach().cpu()

       snake_case__  :    Tuple							       =image_processor.post_process_semantic_segmentation(outputs=a				,			target_sizes=[(5_0, 6_0)]   )
       snake_case__  :    Union[str, Any]							       =torch.Size((5_0, 6_0)   )
       self.assertEqual(segmentation[0].shape				,			a   )

       snake_case__  :    Optional[Any]							       =image_processor.post_process_semantic_segmentation(outputs=a   )
       snake_case__  :    List[Any]							       =torch.Size((3_2, 3_2)   )
       self.assertEqual(segmentation[0].shape				,			a   )













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





