






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  _lowercase   (   _lowerCAmelCase						):
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.config_class(**self.inputs_dict   )
       self.parent.assertTrue(hasattr(_lowerCAmelCase				,			"""hidden_sizes"""   )   )
       self.parent.assertTrue(hasattr(_lowerCAmelCase				,			"""neck_hidden_sizes"""   )   )
       self.parent.assertTrue(hasattr(_lowerCAmelCase				,			"""num_attention_heads"""   )   )







class  _lowercase   :
      def __init__(     self				,			a				,			a=1_3				,			a=3_2				,			a=2				,			a=3				,			a=6_4_0				,			a=4				,			a="silu"				,			a=3				,			a=3_2				,			a=0.1				,			a=0.1				,			a=0.1				,			a=0.02				,			a=True				,			a=True				,			a=1_0				,			a=None				,			):
       snake_case__  :    Optional[int]							       =parent
       snake_case__  :    List[Any]							       =batch_size
       snake_case__  :    Optional[Any]							       =image_size
       snake_case__  :    Union[str, Any]							       =patch_size
       snake_case__  :    List[Any]							       =num_channels
       snake_case__  :    Union[str, Any]							       =last_hidden_size
       snake_case__  :    int							       =num_attention_heads
       snake_case__  :    int							       =hidden_act
       snake_case__  :    List[Any]							       =conv_kernel_size
       snake_case__  :    int							       =output_stride
       snake_case__  :    Dict							       =hidden_dropout_prob
       snake_case__  :    int							       =attention_probs_dropout_prob
       snake_case__  :    int							       =classifier_dropout_prob
       snake_case__  :    List[str]							       =use_labels
       snake_case__  :    Optional[int]							       =is_training
       snake_case__  :    Union[str, Any]							       =num_labels
       snake_case__  :    Dict							       =initializer_range
       snake_case__  :    int							       =scope
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

       snake_case__  :    int							       =self.get_config()

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







@require_torch
class  _lowercase   (   _lowerCAmelCase						,				_lowerCAmelCase						,				unittest.TestCase						):
      _a							:						List[Any]										=							(
          (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      _a							:						List[Any]										=							(
          {
              'feature-extraction': MobileViTModel,
              'image-classification': MobileViTForImageClassification,
              'image-segmentation': MobileViTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _a							:						List[str]										=							False
      _a							:						Tuple										=							False
      _a							:						Union[str, Any]										=							False
      _a							:						int										=							False
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =MobileViTModelTester(self   )
       snake_case__  :    int							       =MobileViTConfigTester(self				,			config_class=_lowerCAmelCase				,			has_text_modality=_lowerCAmelCase   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      @unittest.skip(reason="""MobileViT does not use inputs_embeds"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""MobileViT does not support input and output embeddings"""   )
      def 						lowercase__					(     self   ):
       pass
      @unittest.skip(reason="""MobileViT does not output attentions"""   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       snake_case__       ,   snake_case__  :    Dict							       =self.model_tester.prepare_config_and_inputs_for_common()

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

        snake_case__  :    Optional[int]							       =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,			_lowerCAmelCase   )
      @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__  :    Optional[int]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase   )
      def 						lowercase__					(     self   ):







       def check_hidden_states_output(a				,			a				,			a   ):
        snake_case__  :    Optional[int]							       =model_class(_lowerCAmelCase   )
        model.to(_lowerCAmelCase   )
        model.eval()

        with torch.no_grad():
         snake_case__  :    Tuple							       =model(**self._prepare_for_class(_lowerCAmelCase				,			_lowerCAmelCase   )   )

        snake_case__  :    Tuple							       =outputs.hidden_states

        snake_case__  :    Any							       =5
        self.assertEqual(len(_lowerCAmelCase   )				,			_lowerCAmelCase   )

        # 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__  :    Tuple							       =2
        for i in range(len(_lowerCAmelCase   )   ):
         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__  :    Optional[Any]							       =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(_lowerCAmelCase				,			_lowerCAmelCase				,			_lowerCAmelCase   )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        snake_case__  :    Union[str, Any]							       =True

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



      @slow
      def 						lowercase__					(     self   ):







       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    List[str]							       =MobileViTModel.from_pretrained(_lowerCAmelCase   )
        self.assertIsNotNone(_lowerCAmelCase   )







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =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/mobilevit-xx-small"""   ) if is_vision_available() else None
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""   ).to(_lowerCAmelCase   )

       snake_case__  :    Optional[int]							       =self.default_image_processor
       snake_case__  :    str							       =prepare_img()
       snake_case__  :    Dict							       =image_processor(images=_lowerCAmelCase				,			return_tensors="""pt"""   ).to(_lowerCAmelCase   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    Union[str, Any]							       =model(**_lowerCAmelCase   )

       # verify the logits
       snake_case__  :    Optional[int]							       =torch.Size((1, 1_0_0_0)   )
       self.assertEqual(outputs.logits.shape				,			_lowerCAmelCase   )

       snake_case__  :    Optional[int]							       =torch.tensor([-1.9364, -1.2327, -0.4653]   ).to(_lowerCAmelCase   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			_lowerCAmelCase				,			atol=1e-4   )   )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )
       snake_case__  :    Optional[Any]							       =model.to(_lowerCAmelCase   )

       snake_case__  :    Union[str, Any]							       =MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

       snake_case__  :    Union[str, Any]							       =prepare_img()
       snake_case__  :    List[Any]							       =image_processor(images=_lowerCAmelCase				,			return_tensors="""pt"""   ).to(_lowerCAmelCase   )

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

       # verify the logits
       snake_case__  :    Any							       =torch.Size((1, 2_1, 3_2, 3_2)   )
       self.assertEqual(logits.shape				,			_lowerCAmelCase   )

       snake_case__  :    List[Any]							       =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=_lowerCAmelCase				,			)

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



      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )
       snake_case__  :    Dict							       =model.to(_lowerCAmelCase   )

       snake_case__  :    Tuple							       =MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

       snake_case__  :    List[str]							       =prepare_img()
       snake_case__  :    int							       =image_processor(images=_lowerCAmelCase				,			return_tensors="""pt"""   ).to(_lowerCAmelCase   )

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

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

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

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













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))





