"""simple docstring"""


import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import UperNetForSemanticSegmentation
    from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=13,							lowerCAmelCase__=32,							lowerCAmelCase__=3,							lowerCAmelCase__=4,							lowerCAmelCase__=[10, 20, 30, 40],							lowerCAmelCase__=[2, 2, 3, 2],							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=37,							lowerCAmelCase__="gelu",							lowerCAmelCase__=10,							lowerCAmelCase__=0.02,							lowerCAmelCase__=["stage2", "stage3", "stage4"],							lowerCAmelCase__=3,							lowerCAmelCase__=None,							)      ->      str:
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					image_size
            snake_case_					       =					num_channels
            snake_case_					       =					num_stages
            snake_case_					       =					hidden_sizes
            snake_case_					       =					depths
            snake_case_					       =					is_training
            snake_case_					       =					use_labels
            snake_case_					       =					intermediate_size
            snake_case_					       =					hidden_act
            snake_case_					       =					type_sequence_label_size
            snake_case_					       =					initializer_range
            snake_case_					       =					out_features
            snake_case_					       =					num_labels
            snake_case_					       =					scope
            snake_case_					       =					num_stages







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

            snake_case_					       =					None
            if self.use_labels:
                 snake_case_					       =					ids_tensor([self.batch_size],							self.type_sequence_label_size)

            snake_case_					       =					self.get_config()

            return config, pixel_values, labels







       def 		a_						( self)      ->      Dict:
            return ConvNextConfig(
                num_channels=self.num_channels,							num_stages=self.num_stages,							hidden_sizes=self.hidden_sizes,							depths=self.depths,							is_training=self.is_training,							intermediate_size=self.intermediate_size,							hidden_act=self.hidden_act,							out_features=self.out_features,							)







       def 		a_						( self)      ->      Dict:
            return UperNetConfig(
                backbone_config=self.get_backbone_config(),							hidden_size=512,							pool_scales=[1, 2, 3, 6],							use_auxiliary_head=lowerCAmelCase__,							auxiliary_loss_weight=0.4,							auxiliary_in_channels=40,							auxiliary_channels=256,							auxiliary_num_convs=1,							auxiliary_concat_input=lowerCAmelCase__,							loss_ignore_index=255,							num_labels=self.num_labels,							)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					UperNetForSemanticSegmentation(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(
                result.logits.shape,							(self.batch_size, self.num_labels, self.image_size, self.image_size))







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.prepare_config_and_inputs()
            (
                (
                snake_case_
            )       ,	(
                snake_case_
            )       ,	(
                snake_case_
            )       ,	
            )					       =					config_and_inputs
            snake_case_					       =					{'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      (UperNetForSemanticSegmentation,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_			=      {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					UperNetModelTester(self)
            snake_case_					       =					ConfigTester(self,							config_class=lowerCAmelCase__,							has_text_modality=lowerCAmelCase__,							hidden_size=37)







       def 		a_						( self)      ->      Dict:
            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()







       def 		a_						( self)      ->      Optional[Any]:
            return







       def 		a_						( self)      ->      Tuple:
            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case_					       =					['pixel_values']
                 self.assertListEqual(arg_names[:1],							lowerCAmelCase__)







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__)







       @unittest.skip(reason='UperNet does not use inputs_embeds')
       def 		a_						( self)      ->      List[str]:
            pass







       @unittest.skip(reason='UperNet does not support input and output embeddings')
       def 		a_						( self)      ->      List[Any]:
            pass







       @unittest.skip(reason='UperNet does not have a base model')
       def 		a_						( self)      ->      List[Any]:
            pass







       @unittest.skip(reason='UperNet does not have a base model')
       def 		a_						( self)      ->      Any:
            pass







       @require_torch_multi_gpu
       @unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
       def 		a_						( self)      ->      Tuple:
            pass







       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
       def 		a_						( self)      ->      Optional[Any]:
            pass







       def 		a_						( self)      ->      Any:
            def check_hidden_states_output(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_					       =					model_class(lowerCAmelCase__)
                 model.to(lowerCAmelCase__)
                 model.eval()

                 with torch.no_grad():
                      snake_case_					       =					model(**self._prepare_for_class(lowerCAmelCase__,							lowerCAmelCase__))

                 snake_case_					       =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case_					       =					self.model_tester.num_stages
                 self.assertEqual(len(lowerCAmelCase__),							expected_num_stages + 1)

                 # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]),							[self.model_tester.image_size // 4, self.model_tester.image_size // 4],							)

            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 snake_case_					       =					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_					       =					True

                 check_hidden_states_output(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Dict:
            snake_case_       ,	snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()

            snake_case_					       =					_config_zero_init(lowerCAmelCase__)
            snake_case_					       =					_config_zero_init(configs_no_init.backbone_config)
            for model_class in self.all_model_classes:
                 snake_case_					       =					model_class(config=lowerCAmelCase__)
                 for name, param in model.named_parameters():
                      if param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1e9).round() / 1e9).item(),							[0.0, 1.0],							msg=f'Parameter {name} of model {model_class} seems not properly initialized',							)







       @unittest.skip(reason='UperNet does not have tied weights')
       def 		a_						( self)      ->      Any:
            pass







       @slow
       def 		a_						( self)      ->      List[str]:
            for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case_					       =					UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase__)
                 self.assertIsNotNone(lowerCAmelCase__)







def 					UpperCAmelCase    (		)				->      str:
     snake_case_					       =					hf_hub_download(
         repo_id='hf-internal-testing/fixtures_ade20k'						,					repo_type='dataset'						,					filename='ADE_val_00000001.jpg'					)
     snake_case_					       =					Image.open(UpperCAmelCase					).convert('RGB'					)
     return image




@require_torch
@require_vision
@slow
class  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      int:
            snake_case_					       =					AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny')
            snake_case_					       =					UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny').to(lowerCAmelCase__)

            snake_case_					       =					prepare_img()
            snake_case_					       =					processor(images=lowerCAmelCase__,							return_tensors='pt').to(lowerCAmelCase__)

            with torch.no_grad():
                 snake_case_					       =					model(**lowerCAmelCase__)

            snake_case_					       =					torch.Size((1, model.config.num_labels, 512, 512))
            self.assertEqual(outputs.logits.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor(
                [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(lowerCAmelCase__)
            self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],							lowerCAmelCase__,							atol=1e-4))







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny')
            snake_case_					       =					UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny').to(lowerCAmelCase__)

            snake_case_					       =					prepare_img()
            snake_case_					       =					processor(images=lowerCAmelCase__,							return_tensors='pt').to(lowerCAmelCase__)

            with torch.no_grad():
                 snake_case_					       =					model(**lowerCAmelCase__)

            snake_case_					       =					torch.Size((1, model.config.num_labels, 512, 512))
            self.assertEqual(outputs.logits.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor(
                [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(lowerCAmelCase__)
            self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],							lowerCAmelCase__,							atol=1e-4))



def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    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       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[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       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	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       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    '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       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# 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__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = 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
	UpperCAmelCase      , UpperCAmelCase	      = 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)

