
"""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 		UpperCAmelCase_						:
        """simple docstring"""




        @staticmethod
        def 	a   (			*a_							:			List[Any]  , **a_							:			Dict			)->       str:



             """simple docstring"""

             pass




def       A_      (       lowercase   )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	hashlib.mda(image.tobytes()   )
     return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 UpperCamelCase_    :  Union[str, Any]					 =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING




 def 	a   (			self							:			str  , a_							:			str  , a_							:			List[str]  , a_							:			Union[str, Any]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	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_							:			Optional[int]			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      self.assertEqual({"""predicted_depth""": ANY(torch.Tensor			), """depth""": ANY(Image.Image			)}  , a_			)
      import datasets

      UpperCAmelCase_    :     Tuple            =	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""  , """image"""  , split="""test"""			)
      UpperCAmelCase_    :     Any            =	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							:			Dict			)->       Tuple:



      """simple docstring"""

      pass




 @slow
 @require_torch
 def 	a   (			self							:			Union[str, Any]			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	"""Intel/dpt-large"""
      UpperCAmelCase_    :     Optional[int]            =	pipeline("""depth-estimation"""  , model=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      UpperCAmelCase_    :     Union[str, Any]            =	hashimage(outputs["""depth"""]			)

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()			)  , 29.304			)
      self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()			)  , 2.662			)






 @require_torch
 def 	a   (			self							:			List[str]			)->       List[str]:



      """simple docstring"""

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



"""simple docstring"""







def       A_      (       lowercase = 1000   )  ->				int:


     """simple docstring"""







     return sum(e for e in range(3    ,			lowercase   ) if e % 3 == 0 or e % 5 == 0   )


if __name__ == "__main__":
       print(f"""{solution() = }""")

