






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 							_UpperCamelCase			:


       @staticmethod
       def  __UpperCAmelCase		(	*__UpperCamelCase					,						**__UpperCamelCase	)->	Optional[int]:
            pass





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					hashlib.mda(image.tobytes()       )
     return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							_UpperCamelCase			(unittest.TestCase			):
      snake_case_  =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Any:
           __lowerCAmelCase								=					DepthEstimationPipeline(model=__UpperCamelCase					,						image_processor=__UpperCamelCase	)
           return depth_estimator, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
           self.assertEqual({"predicted_depth": ANY(torch.Tensor	), "depth": ANY(Image.Image	)}					,						__UpperCamelCase	)
           import datasets

           __lowerCAmelCase								=					datasets.load_dataset("hf-internal-testing/fixtures_image_utils"					,						"image"					,						split="test"	)
           __lowerCAmelCase								=					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	)},
               ]					,						__UpperCamelCase					,						)


      @require_tf
      @unittest.skip("Depth estimation is not implemented in TF"	)
      def  __UpperCAmelCase		(	self	)->	List[Any]:
           pass


      @slow
      @require_torch
      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					"Intel/dpt-large"
           __lowerCAmelCase								=					pipeline("depth-estimation"					,						model=__UpperCamelCase	)
           __lowerCAmelCase								=					depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"	)
           __lowerCAmelCase								=					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	)->	Tuple:
           # This is highly irregular to have no small tests.
           self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"	)














from __future__ import annotations





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






