'''simple docstring'''






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__							(       lowerCamelCase__   ):





   """simple docstring"""





   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   self.config_class(**self.inputs_dict			)
      self.parent.assertTrue(hasattr(lowerCAmelCase__						,     "width_multiplier"			)			)





class     A__							:





   """simple docstring"""





   def __init__( self     :		Any						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		List[str]=1_3						,     lowerCAmelCase__     :		List[Any]=6_4						,     lowerCAmelCase__     :		List[Any]=2						,     lowerCAmelCase__     :		Dict=3						,     lowerCAmelCase__     :		Any="swish"						,     lowerCAmelCase__     :		List[str]=3						,     lowerCAmelCase__     :		List[Any]=3_2						,     lowerCAmelCase__     :		Dict=0.1						,     lowerCAmelCase__     :		Optional[int]=0.02						,     lowerCAmelCase__     :		Optional[Any]=True						,     lowerCAmelCase__     :		Union[str, Any]=True						,     lowerCAmelCase__     :		Any=1_0						,     lowerCAmelCase__     :		Optional[int]=None						,     lowerCAmelCase__     :		Optional[Any]=0.25						,     lowerCAmelCase__     :		Optional[int]=0.0						,     lowerCAmelCase__     :		Dict=0.0						,     )       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   parent
      _UpperCAmelCase							:	Tuple							=   batch_size
      _UpperCAmelCase							:	Tuple							=   image_size
      _UpperCAmelCase							:	List[Any]							=   patch_size
      _UpperCAmelCase							:	Union[str, Any]							=   num_channels
      _UpperCAmelCase							:	Tuple							=   make_divisible(5_1_2 * width_multiplier						,     divisor=8			)
      _UpperCAmelCase							:	int							=   hidden_act
      _UpperCAmelCase							:	List[str]							=   conv_kernel_size
      _UpperCAmelCase							:	str							=   output_stride
      _UpperCAmelCase							:	Tuple							=   classifier_dropout_prob
      _UpperCAmelCase							:	Dict							=   use_labels
      _UpperCAmelCase							:	Optional[int]							=   is_training
      _UpperCAmelCase							:	List[Any]							=   num_labels
      _UpperCAmelCase							:	int							=   initializer_range
      _UpperCAmelCase							:	Any							=   scope
      _UpperCAmelCase							:	List[Any]							=   width_multiplier
      _UpperCAmelCase							:	Any							=   ffn_dropout
      _UpperCAmelCase							:	Optional[Any]							=   attn_dropout




   def 			_lowerCAmelCase		( self     :		str			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      _UpperCAmelCase							:	Any							=   None
      _UpperCAmelCase							:	Any							=   None
      if self.use_labels:
         _UpperCAmelCase							:	Any							=   ids_tensor([self.batch_size]						,     self.num_labels			)
         _UpperCAmelCase							:	List[Any]							=   ids_tensor([self.batch_size, self.image_size, self.image_size]						,     self.num_labels			)

      _UpperCAmelCase							:	Optional[int]							=   self.get_config()

      return config, pixel_values, labels, pixel_labels




   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Tuple:







      """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 			_lowerCAmelCase		( self     :		int						,     lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Union[str, Any]			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   MobileViTVaModel(config=lowerCAmelCase__			)
      model.to(lowerCAmelCase__			)
      model.eval()
      _UpperCAmelCase							:	str							=   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     :		List[Any]						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Optional[Any]			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   self.num_labels
      _UpperCAmelCase							:	Union[str, Any]							=   MobileViTVaForImageClassification(lowerCAmelCase__			)
      model.to(lowerCAmelCase__			)
      model.eval()
      _UpperCAmelCase							:	int							=   model(lowerCAmelCase__						,     labels=lowerCAmelCase__			)
      self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)			)




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		List[Any]						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Any			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   self.num_labels
      _UpperCAmelCase							:	Optional[Any]							=   MobileViTVaForSemanticSegmentation(lowerCAmelCase__			)
      model.to(lowerCAmelCase__			)
      model.eval()
      _UpperCAmelCase							:	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,
          )						,     )
      _UpperCAmelCase							:	Optional[int]							=   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     :		Any			)       ->    Dict:







      """simple docstring"""




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





@require_torch
class     A__							(       lowerCamelCase__					,		lowerCamelCase__					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Dict    				=     (
       (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )

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

   UpperCamelCase_       :						Optional[Any]    				=     False
   UpperCamelCase_       :						Tuple    				=     False
   UpperCamelCase_       :						str    				=     False
   UpperCamelCase_       :						Union[str, Any]    				=     False




   def 			_lowerCAmelCase		( self     :		int			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   MobileViTVaModelTester(self			)
      _UpperCAmelCase							:	List[Any]							=   MobileViTVaConfigTester(self						,     config_class=lowerCAmelCase__						,     has_text_modality=lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Dict:







      """simple docstring"""




      self.config_tester.run_common_tests()




   @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"			)
   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    str:







      """simple docstring"""




      pass




   @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"			)
   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    str:







      """simple docstring"""




      pass




   @unittest.skip(reason="MobileViTV2 does not output attentions"			)
   def 			_lowerCAmelCase		( self     :		Any			)       ->    int:







      """simple docstring"""




      pass




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







      """simple docstring"""




      pass




   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    str:







      """simple docstring"""




      pass




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







      """simple docstring"""




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

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

         _UpperCAmelCase							:	Union[str, Any]							=   ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]						,     lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		Dict			)       ->    List[str]:







      """simple docstring"""




      def check_hidden_states_output(lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		List[Any]			):
         _UpperCAmelCase							:	Optional[int]							=   model_class(lowerCAmelCase__			)
         model.to(lowerCAmelCase__			)
         model.eval()

         with torch.no_grad():
            _UpperCAmelCase							:	List[str]							=   model(**self._prepare_for_class(lowerCAmelCase__						,     lowerCAmelCase__			)			)

         _UpperCAmelCase							:	List[Any]							=   outputs.hidden_states

         _UpperCAmelCase							:	Union[str, Any]							=   5
         self.assertEqual(len(lowerCAmelCase__			)						,     lowerCAmelCase__			)

         # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         _UpperCAmelCase							:	int							=   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			)

      _UpperCAmelCase							:	Any							=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _UpperCAmelCase							:	str							=   True
         check_hidden_states_output(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			)

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

         check_hidden_states_output(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		str			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)




   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__			)




   @slow
   def 			_lowerCAmelCase		( self     :		str			)       ->    Dict:







      """simple docstring"""




      for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase							:	Tuple							=   MobileViTVaModel.from_pretrained(lowerCAmelCase__			)
         self.assertIsNotNone(lowerCAmelCase__			)






def    __UpperCAmelCase			(  ):
   _UpperCAmelCase							:	Tuple							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
   return image






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





   """simple docstring"""





   @cached_property
   def 			_lowerCAmelCase		( self     :		int			)       ->    int:







      """simple docstring"""




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




   @slow
   def 			_lowerCAmelCase		( self     :		List[str]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"			).to(
          lowerCAmelCase__			)

      _UpperCAmelCase							:	int							=   self.default_image_processor
      _UpperCAmelCase							:	int							=   prepare_img()
      _UpperCAmelCase							:	Any							=   image_processor(images=lowerCAmelCase__						,     return_tensors="pt"			).to(lowerCAmelCase__			)

      # forward pass
      with torch.no_grad():
         _UpperCAmelCase							:	str							=   model(**lowerCAmelCase__			)

      # verify the logits
      _UpperCAmelCase							:	Optional[Any]							=   torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape						,     lowerCAmelCase__			)

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

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




   @slow
   def 			_lowerCAmelCase		( self     :		Any			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)
      _UpperCAmelCase							:	Any							=   model.to(lowerCAmelCase__			)

      _UpperCAmelCase							:	List[Any]							=   MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)

      _UpperCAmelCase							:	Dict							=   prepare_img()
      _UpperCAmelCase							:	int							=   image_processor(images=lowerCAmelCase__						,     return_tensors="pt"			).to(lowerCAmelCase__			)

      # forward pass
      with torch.no_grad():
         _UpperCAmelCase							:	Dict							=   model(**lowerCAmelCase__			)
      _UpperCAmelCase							:	List[str]							=   outputs.logits

      # verify the logits
      _UpperCAmelCase							:	int							=   torch.Size((1, 2_1, 3_2, 3_2)			)
      self.assertEqual(logits.shape						,     lowerCAmelCase__			)

      _UpperCAmelCase							:	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=lowerCAmelCase__						,     )

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




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







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)
      _UpperCAmelCase							:	List[Any]							=   model.to(lowerCAmelCase__			)

      _UpperCAmelCase							:	str							=   MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"			)

      _UpperCAmelCase							:	Optional[int]							=   prepare_img()
      _UpperCAmelCase							:	List[Any]							=   image_processor(images=lowerCAmelCase__						,     return_tensors="pt"			).to(lowerCAmelCase__			)

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

      _UpperCAmelCase							:	int							=   outputs.logits.detach().cpu()

      _UpperCAmelCase							:	str							=   image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__						,     target_sizes=[(5_0, 6_0)]			)
      _UpperCAmelCase							:	int							=   torch.Size((5_0, 6_0)			)
      self.assertEqual(segmentation[0].shape						,     lowerCAmelCase__			)

      _UpperCAmelCase							:	Tuple							=   image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__			)
      _UpperCAmelCase							:	str							=   torch.Size((3_2, 3_2)			)
      self.assertEqual(segmentation[0].shape						,     lowerCAmelCase__			)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]