"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               lowercase_	:				List[Any]													=	mf_knapsack(i - 1     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          else:
               lowercase_	:				Optional[Any]													=	max(
                   mf_knapsack(i - 1     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )     ,			mf_knapsack(i - 1     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			j - wt[i - 1]      ) + val[i - 1]     ,			)
          lowercase_	:				Any													=	val
     return f[i][j]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Dict													=	[[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_:
                    lowercase_	:				List[Any]													=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]     ,			dp[i - 1][w_]      )
               else:
                    lowercase_	:				Union[str, Any]													=	dp[i - 1][w_]

     return dp[n][w_], dp



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     list      ):
     if not (isinstance(__SCREAMING_SNAKE_CASE     ,			(list, tuple)      ) and isinstance(__SCREAMING_SNAKE_CASE     ,			(list, tuple)      )):
          raise ValueError(
              'Both the weights and values vectors must be either lists or tuples'      )

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

     lowercase_							,  lowercase_	:				Tuple													=	knapsack(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				set													=	set()
     _construct_solution(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     return optimal_val, example_optional_set



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     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(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			i - 1     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          else:
               optimal_set.add(__SCREAMING_SNAKE_CASE      )
               _construct_solution(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			i - 1     ,			j - wt[i - 1]     ,			__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=[3, 2, 4, 4]
       __SCREAMING_SNAKE_CASE    	=[4, 3, 2, 3]
       __SCREAMING_SNAKE_CASE    	=4
       __SCREAMING_SNAKE_CASE    	=6
       __SCREAMING_SNAKE_CASE    	=[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE    	=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
       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE    	=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)


"""simple docstring"""







import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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 torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,__UpperCamelCase=None     ,__UpperCamelCase=2     ,)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	parent
        lowercase_	:				List[Any]													=	batch_size
        lowercase_	:				Optional[int]													=	image_size
        lowercase_	:				List[str]													=	patch_size
        lowercase_	:				int													=	num_channels
        lowercase_	:				List[str]													=	is_training
        lowercase_	:				Union[str, Any]													=	use_labels
        lowercase_	:				str													=	hidden_size
        lowercase_	:				List[str]													=	num_hidden_layers
        lowercase_	:				List[str]													=	num_attention_heads
        lowercase_	:				Dict													=	intermediate_size
        lowercase_	:				Optional[int]													=	hidden_act
        lowercase_	:				Any													=	hidden_dropout_prob
        lowercase_	:				Any													=	attention_probs_dropout_prob
        lowercase_	:				List[str]													=	type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	initializer_range
        lowercase_	:				int													=	scope
        lowercase_	:				Dict													=	encoder_stride

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Any													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Tuple													=	None
        if self.use_labels:
             lowercase_	:				Dict													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)

        lowercase_	:				Any													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return ViTConfig(
            image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=__UpperCamelCase     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	ViTModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTForMaskedImageModeling(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(
            result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase_	:				Any													=	1
        lowercase_	:				List[Any]													=	ViTForMaskedImageModeling(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.type_sequence_label_size
        lowercase_	:				Dict													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Dict													=	1
        lowercase_	:				Any													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)







   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				Optional[Any]													=	config_and_inputs
        lowercase_	:				str													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   lowercase							=  True

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModelTester(self			)
        lowercase_	:				Dict													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViT does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)			)
             lowercase_	:				List[Any]													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_	:				Tuple													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				List[Any]													=	ViTModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)






def 			lowercase__(       ):
     lowercase_	:				Any													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
   @slow
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			).to(__UpperCamelCase			)

        lowercase_	:				Any													=	self.default_image_processor
        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			).to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				Any													=	model(**__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Optional[Any]													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	torch.tensor([-0.2744, 0.8215, -0.0836]			).to(__UpperCamelCase			)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	ViTModel.from_pretrained('facebook/dino-vits8'			).to(__UpperCamelCase			)

        lowercase_	:				int													=	ViTImageProcessor.from_pretrained('facebook/dino-vits8'     ,size=480			)
        lowercase_	:				int													=	prepare_img()
        lowercase_	:				Dict													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				int													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				int													=	model(__UpperCamelCase     ,interpolate_pos_encoding=__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Any													=	torch.Size((1, 3601, 384)			)
        self.assertEqual(outputs.last_hidden_state.shape     ,__UpperCamelCase			)

        lowercase_	:				Any													=	torch.tensor(
            [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]			).to(__UpperCamelCase			)

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







   @slow
   @require_accelerate
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModel.from_pretrained('facebook/dino-vits8'     ,torch_dtype=torch.floataa     ,device_map='auto'			)
        lowercase_	:				int													=	self.default_image_processor

        lowercase_	:				Optional[int]													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				Union[str, Any]													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
             lowercase_	:				Dict													=	model(__UpperCamelCase			)

