
'''simple docstring'''

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  _UpperCAmelCase ( lowercase_    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	str      =							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  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=32							,		lowerCAmelCase_=2							,		lowerCAmelCase_=3							,		lowerCAmelCase_=6_40							,		lowerCAmelCase_=4							,		lowerCAmelCase_="silu"							,		lowerCAmelCase_=3							,		lowerCAmelCase_=32							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=10							,		lowerCAmelCase_=None							,		):

           '''simple docstring'''

           a_      :	Tuple      =							parent
           a_      :	List[str]      =							batch_size
           a_      :	Any      =							image_size
           a_      :	Optional[int]      =							patch_size
           a_      :	Dict      =							num_channels
           a_      :	Optional[Any]      =							last_hidden_size
           a_      :	Optional[Any]      =							num_attention_heads
           a_      :	str      =							hidden_act
           a_      :	Union[str, Any]      =							conv_kernel_size
           a_      :	List[Any]      =							output_stride
           a_      :	int      =							hidden_dropout_prob
           a_      :	int      =							attention_probs_dropout_prob
           a_      :	str      =							classifier_dropout_prob
           a_      :	Any      =							use_labels
           a_      :	Tuple      =							is_training
           a_      :	Any      =							num_labels
           a_      :	Optional[int]      =							initializer_range
           a_      :	int      =							scope


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Optional[Any]      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

           a_      :	Union[str, Any]      =							self.get_config()

           return config, pixel_values, labels, pixel_labels


     def 							_lowerCAmelCase  (      self ):

           '''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							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

           '''simple docstring'''

           a_      :	Any      =							MobileViTModel(config=lowerCAmelCase_ )
           model.to(lowerCAmelCase_ )
           model.eval()
           a_      :	List[Any]      =							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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

           '''simple docstring'''

           a_      :	str      =							self.num_labels
           a_      :	List[str]      =							MobileViTForImageClassification(lowerCAmelCase_ )
           model.to(lowerCAmelCase_ )
           model.eval()
           a_      :	Optional[Any]      =							model(lowerCAmelCase_							,		labels=lowerCAmelCase_ )
           self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

           '''simple docstring'''

           a_      :	Any      =							self.num_labels
           a_      :	List[str]      =							MobileViTForSemanticSegmentation(lowerCAmelCase_ )
           model.to(lowerCAmelCase_ )
           model.eval()
           a_      :	List[str]      =							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,
               )							,		)
           a_      :	Union[str, Any]      =							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 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Optional[int]      =							self.prepare_config_and_inputs()
           a_      , a_      , a_      , a_      :	Union[str, Any]      =							config_and_inputs
           a_      :	Dict      =							{"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowercase_	,lowercase_	,unittest.TestCase    ):

     """simple docstring"""

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

     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Optional[Any]      =							MobileViTModelTester(self )
           a_      :	int      =							MobileViTConfigTester(self							,		config_class=lowerCAmelCase_							,		has_text_modality=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           self.config_tester.run_common_tests()


     @unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           pass


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

           '''simple docstring'''

           pass


     @unittest.skip(reason="""MobileViT does not output attentions""" )
     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           pass


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      , a_      :	int      =							self.model_tester.prepare_config_and_inputs_for_common()

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

                 a_      :	List[str]      =							["""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 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           pass


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Dict      =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           def check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):
                 a_      :	Union[str, Any]      =							model_class(lowerCAmelCase_ )
                 model.to(lowerCAmelCase_ )
                 model.eval()

                 with torch.no_grad():
                       a_      :	Union[str, Any]      =							model(**self._prepare_for_class(lowerCAmelCase_							,		lowerCAmelCase_ ) )

                 a_      :	List[Any]      =							outputs.hidden_states

                 a_      :	Optional[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.
                 a_      :	Union[str, Any]      =							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 )

           a_      , a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 a_      :	Tuple      =							True
                 check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

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

                 check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Tuple      =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_ )






     @slow
     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_      :	List[Any]      =							MobileViTModel.from_pretrained(lowerCAmelCase_ )
                 self.assertIsNotNone(lowerCAmelCase_ )







def 	_snake_case						(      ):
      """simple docstring"""







      a_      :	List[Any]      =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
      return image




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

     """simple docstring"""

     @cached_property
     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

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


     @slow
     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Any      =							MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" ).to(lowerCAmelCase_ )

           a_      :	Tuple      =							self.default_image_processor
           a_      :	List[Any]      =							prepare_img()
           a_      :	Union[str, Any]      =							image_processor(images=lowerCAmelCase_							,		return_tensors="""pt""" ).to(lowerCAmelCase_ )

           # forward pass
           with torch.no_grad():
                 a_      :	List[str]      =							model(**lowerCAmelCase_ )

           # verify the logits
           a_      :	Any      =							torch.Size((1, 10_00) )
           self.assertEqual(outputs.logits.shape							,		lowerCAmelCase_ )

           a_      :	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 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Optional[int]      =							MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
           a_      :	List[str]      =							model.to(lowerCAmelCase_ )

           a_      :	int      =							MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

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

           # forward pass
           with torch.no_grad():
                 a_      :	int      =							model(**lowerCAmelCase_ )
           a_      :	int      =							outputs.logits

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

           a_      :	Dict      =							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 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           a_      :	Tuple      =							MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
           a_      :	str      =							model.to(lowerCAmelCase_ )

           a_      :	List[str]      =							MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

           a_      :	Tuple      =							prepare_img()
           a_      :	List[str]      =							image_processor(images=lowerCAmelCase_							,		return_tensors="""pt""" ).to(lowerCAmelCase_ )

           # forward pass
           with torch.no_grad():
                 a_      :	int      =							model(**lowerCAmelCase_ )

           a_      :	Optional[Any]      =							outputs.logits.detach().cpu()

           a_      :	Optional[int]      =							image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_							,		target_sizes=[(50, 60)] )
           a_      :	Union[str, Any]      =							torch.Size((50, 60) )
           self.assertEqual(segmentation[0].shape							,		lowerCAmelCase_ )

           a_      :	List[Any]      =							image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_ )
           a_      :	Dict      =							torch.Size((32, 32) )
           self.assertEqual(segmentation[0].shape							,		lowerCAmelCase_ )




'''simple docstring'''

def 	_snake_case						(      A_       :    list   ):
  """simple docstring"""







  if len(A_   ) <= 1:
    return lst

  a_      :	Any      =							1

  while i < len(A_   ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      a_      , a_      :	int      =							lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        a_      :	List[str]      =							1

  return lst


if __name__ == "__main__":
    __snake_case:  List[Any]								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[int]								=			[int(item) for item in user_input.split(",")]
    print(gnome_sort(unsorted))


