


"""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 			A__	:


         """simple docstring"""

         @staticmethod
         def 		a__						(	*__a:						Optional[int]     ,		**__a:						Optional[int]	)->    Optional[Any]:
               pass




def   snake_case		(     UpperCamelCase__       :						Image    )   ->      str:
      lowerCamelCase					:					List[Any]        =	hashlib.mda(image.tobytes()    )
      return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[int]													=MODEL_FOR_DEPTH_ESTIMATION_MAPPING

     def 		a__						(	self:						Optional[Any]     ,		__a:						Tuple     ,		__a:						Optional[int]     ,		__a:						Tuple	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	DepthEstimationPipeline(model=__a     ,		image_processor=__a	)
           return depth_estimator, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]

     def 		a__						(	self:						Any     ,		__a:						List[str]     ,		__a:						List[str]	)->    Optional[int]:
           lowerCamelCase					:					Union[str, Any]        =	depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
           self.assertEqual({"""predicted_depth""": ANY(torch.Tensor	), """depth""": ANY(Image.Image	)}     ,		__a	)
           import datasets

           lowerCamelCase					:					Union[str, Any]        =	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""     ,		"""image"""     ,		split="""test"""	)
           lowerCamelCase					:					Optional[int]        =	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 		a__						(	self:						Optional[Any]	)->    List[str]:
           pass

     @slow
     @require_torch
     def 		a__						(	self:						Union[str, Any]	)->    Optional[Any]:
           lowerCamelCase					:					str        =	"""Intel/dpt-large"""
           lowerCamelCase					:					List[Any]        =	pipeline("""depth-estimation"""     ,		model=__a	)
           lowerCamelCase					:					List[str]        =	depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""	)
           lowerCamelCase					:					Dict        =	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 		a__						(	self:						Optional[Any]	)->    List[str]:
           # This is highly irregular to have no small tests.
           self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""	)








"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




