





'''simple docstring'''







class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__    ,   A__    ) ->	int:
      snake_case				=			name
      snake_case				=			value
      snake_case				=			weight







     def __repr__( self    ) ->	str:
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







     def     UpperCamelCase			( self    ) ->	Any:
      return self.value







     def     UpperCamelCase			( self    ) ->	List[str]:
      return self.name







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      return self.weight







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return self.value / self.weight




def    __UpperCamelCase	(	a    : int					,   a    : Optional[int]					,   a    : int     ) ->Dict:
 snake_case				=			[]
 for i in range(len(a     )     ):
  menu.append(Things(name[i]					,   value[i]					,   weight[i]     )     )
 return menu

def    __UpperCamelCase	(	a    : List[str]					,   a    : Optional[int]					,   a    : Dict     ) ->Any:
 snake_case				=			sorted(a					,   key=a					,   reverse=a     )
 snake_case				=			[]
 snake_case				,		snake_case				=			0.0, 0.0
 for i in range(len(a     )     ):
  if (total_cost + items_copy[i].get_weight()) <= max_cost:
   result.append(items_copy[i]     )
   total_cost += items_copy[i].get_weight()
   total_value += items_copy[i].get_value()
 return (result, total_value)

def    __UpperCamelCase	(	) ->Union[str, Any]:
 pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

