




def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Optional[int]=False					):

     if isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE					) and isinstance(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE					):
          __lowerCAmelCase           =	len(set_a.intersection(__SCREAMING_SNAKE_CASE					)					)

          if alternative_union:
               __lowerCAmelCase           =	len(__SCREAMING_SNAKE_CASE					) + len(__SCREAMING_SNAKE_CASE					)
          else:
               __lowerCAmelCase           =	len(set_a.union(__SCREAMING_SNAKE_CASE					)					)

          return intersection / union

     if isinstance(__SCREAMING_SNAKE_CASE				,				(list, tuple)					) and isinstance(__SCREAMING_SNAKE_CASE				,				(list, tuple)					):
          __lowerCAmelCase           =	[element for element in set_a if element in set_b]

          if alternative_union:
               __lowerCAmelCase           =	len(__SCREAMING_SNAKE_CASE					) + len(__SCREAMING_SNAKE_CASE					)
               return len(__SCREAMING_SNAKE_CASE					) / union
          else:
               __lowerCAmelCase           =	set_a + [element for element in set_b if element not in set_a]
               return len(__SCREAMING_SNAKE_CASE					) / len(__SCREAMING_SNAKE_CASE					)

          return len(__SCREAMING_SNAKE_CASE					) / len(__SCREAMING_SNAKE_CASE					)
     return None


if __name__ == "__main__":
     UpperCamelCase__         =      {"""a""", """b""", """c""", """d""", """e"""}
     UpperCamelCase__         =      {"""c""", """d""", """e""", """f""", """h""", """i"""}
     print(jaccard_similarity(set_a, set_b))










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       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											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      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__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()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




