


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      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[str]				    = self.config_class(**self.inputs_dict)
            self.parent.assertTrue(hasattr(__lowerCamelCase ,						"width_multiplier"))







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=6_4 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						__lowerCamelCase="swish" ,						__lowerCamelCase=3 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=1_0 ,						__lowerCamelCase=None ,						__lowerCamelCase=0.2_5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						)			->				int:
            _A      :       Optional[Any]				    = parent
            _A      :       List[str]				    = batch_size
            _A      :       Dict				    = image_size
            _A      :       Dict				    = patch_size
            _A      :       Optional[int]				    = num_channels
            _A      :       List[str]				    = make_divisible(5_1_2 * width_multiplier ,						divisor=8)
            _A      :       Any				    = hidden_act
            _A      :       Union[str, Any]				    = conv_kernel_size
            _A      :       Tuple				    = output_stride
            _A      :       List[Any]				    = classifier_dropout_prob
            _A      :       Any				    = use_labels
            _A      :       Union[str, Any]				    = is_training
            _A      :       Union[str, Any]				    = num_labels
            _A      :       str				    = initializer_range
            _A      :       Dict				    = scope
            _A      :       List[str]				    = width_multiplier
            _A      :       str				    = ffn_dropout
            _A      :       List[str]				    = attn_dropout


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

            return config, pixel_values, labels, pixel_labels


     def  _lowerCamelCase (						self)			->				int:
            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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Union[str, Any]				    = MobileViTVaModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       List[Any]				    = self.num_labels
            _A      :       Union[str, Any]				    = MobileViTVaForImageClassification(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[Any]				    = model(__lowerCamelCase ,						labels=__lowerCamelCase)
            self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels))


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Tuple				    = self.num_labels
            _A      :       Tuple				    = MobileViTVaForSemanticSegmentation(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       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      :       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)			->				Tuple:
            _A      :       str				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A    ,			_A      :       List[Any]				    = config_and_inputs
            _A      :       List[str]				    = {"pixel_values": pixel_values}
            return config, inputs_dict







@require_torch
class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (
         (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )

     __SCREAMING_SNAKE_CASE  			= (
         {
             "feature-extraction": MobileViTVaModel,
             "image-classification": MobileViTVaForImageClassification,
             "image-segmentation": MobileViTVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = MobileViTVaModelTester(self)
            _A      :       int				    = MobileViTVaConfigTester(self ,						config_class=__lowerCamelCase ,						has_text_modality=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            self.config_tester.run_common_tests()


     @unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


     @unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
     def  _lowerCamelCase (						self)			->				Tuple:
            pass


     @unittest.skip(reason="MobileViTV2 does not output attentions")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


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


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


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A    ,			_A      :       Tuple				    = self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A      :       Optional[Any]				    = ["pixel_values"]
                   self.assertListEqual(arg_names[:1] ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            def check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase):
                   _A      :       str				    = model_class(__lowerCamelCase)
                   model.to(__lowerCamelCase)
                   model.eval()

                   with torch.no_grad():
                          _A      :       int				    = model(**self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase))

                   _A      :       Any				    = outputs.hidden_states

                   _A      :       str				    = 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.
                   _A      :       List[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      :       Dict				    = self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A      :       str				    = True
                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

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

                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       int				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase)


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A      :       Optional[int]				    = MobileViTVaModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)




def 							_UpperCAmelCase				():
       _A      :       List[str]				    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       return image







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




     '''simple docstring'''


     @cached_property
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return (
                MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
                if is_vision_available()
                else None
            )


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       str				    = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
                __lowerCamelCase)

            _A      :       int				    = self.default_image_processor
            _A      :       Any				    = prepare_img()
            _A      :       Dict				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

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

            # verify the logits
            _A      :       Dict				    = torch.Size((1, 1_0_0_0))
            self.assertEqual(outputs.logits.shape ,						__lowerCamelCase)

            _A      :       str				    = 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)			->				Optional[int]:
            _A      :       Union[str, Any]				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       List[str]				    = model.to(__lowerCamelCase)

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

            _A      :       Union[str, Any]				    = prepare_img()
            _A      :       Tuple				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Any				    = model(**__lowerCamelCase)
            _A      :       Tuple				    = outputs.logits

            # verify the logits
            _A      :       str				    = torch.Size((1, 2_1, 3_2, 3_2))
            self.assertEqual(logits.shape ,						__lowerCamelCase)

            _A      :       Any				    = torch.tensor(
                [
                    [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                    [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                    [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
                ] ,						device=__lowerCamelCase ,						)

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


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       str				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       Optional[Any]				    = model.to(__lowerCamelCase)

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

            _A      :       Dict				    = prepare_img()
            _A      :       int				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Tuple				    = model(**__lowerCamelCase)

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

            _A      :       List[Any]				    = image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase ,						target_sizes=[(5_0, 6_0)])
            _A      :       Dict				    = torch.Size((5_0, 6_0))
            self.assertEqual(segmentation[0].shape ,						__lowerCamelCase)

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





def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : int		,					A : Optional[int]      ):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     snake_case__							:	Union[str, Any]   	=	mf_knapsack(i - 1		,					A		,					A		,					A      )
              else:
                     snake_case__							:	Any   	=	max(
                         mf_knapsack(i - 1		,					A		,					A		,					A      )		,					mf_knapsack(i - 1		,					A		,					A		,					j - wt[i - 1]      ) + val[i - 1]		,					)
              snake_case__							:	Optional[int]   	=	val
       return f[i][j]
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]		,					A : str		,					A : Dict      ):
       snake_case__							:	int   	=	[[0] * (w + 1) for _ in range(n + 1      )]

       for i in range(1		,					n + 1      ):
              for w_ in range(1		,					w + 1      ):
                     if wt[i - 1] <= w_:
                            snake_case__							:	Union[str, Any]   	=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]		,					dp[i - 1][w_]      )
                     else:
                            snake_case__							:	str   	=	dp[i - 1][w_]

       return dp[n][w_], dp
def        lowercase_							(A : int		,					A : list		,					A : list      ):
       if not (isinstance(A		,					(list, tuple)      ) and isinstance(A		,					(list, tuple)      )):
              raise ValueError(
                  'Both the weights and values vectors must be either lists or tuples'      )

       snake_case__							:	Dict   	=	len(A      )
       if num_items != len(A      ):
              snake_case__							:	str   	=	(
                  'The number of weights must be the same as the number of values.\n'
                  F'''But got {num_items} weights and {len(A      )} values'''
              )
              raise ValueError(A      )
       for i in range(A      ):
              if not isinstance(wt[i]		,					A      ):
                     snake_case__							:	Optional[int]   	=	(
                         'All weights must be integers but got weight of '
                         F'''type {type(wt[i]      )} at index {i}'''
                     )
                     raise TypeError(A      )

       snake_case__	,				snake_case__							:	Optional[int]   	=	knapsack(A		,					A		,					A		,					A      )
       snake_case__							:	set   	=	set()
       _construct_solution(A		,					A		,					A		,					A		,					A      )

       return optimal_val, example_optional_set
def        lowercase_							(A : list		,					A : list		,					A : int		,					A : int		,					A : set      ):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(A		,					A		,					i - 1		,					A		,					A      )
              else:
                     optimal_set.add(A      )
                     _construct_solution(A		,					A		,					i - 1		,					j - wt[i - 1]		,					A      )


if __name__ == "__main__":
 a_   :Any        =      [3, 2, 4, 4]
 a_   :List[Any]        =      [4, 3, 2, 3]
 a_   :Union[str, Any]        =      4
 a_   :List[str]        =      6
 a_   :Union[str, Any]        =      [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 a_  ,						a_   :List[Any]        =      knapsack(w, wt, val, n)
 print(optimal_solution)
 print(mf_knapsack(n, wt, val, w))  # switched the n and w

 # testing the dynamic programming problem with example
 # the optimal subset for the above example are items 3 and 4
 a_  ,						a_   :Any        =      knapsack_with_example_solution(w, wt, val)
 assert optimal_solution == 8
 assert optimal_subset == {3, 4}
 print("optimal_value = ", optimal_solution)
 print("An optimal subset corresponding to the optimal value", optimal_subset)






