





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 			lowerCAmelCase__:






              '''simple docstring'''


              @staticmethod
              def     UpperCamelCase_			(      *__lowerCamelCase    ,						**__lowerCamelCase							)			->     Optional[int]:
                   pass






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	str						    =							hashlib.mda(image.tobytes()	)
     return m.hexdigest()





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






       '''simple docstring'''


       __snake_case 			=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DepthEstimationPipeline(model=__lowerCamelCase    ,						image_processor=__lowerCamelCase							)
            return depth_estimator, [
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
            ]




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	str						    =							depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
            self.assertEqual({"predicted_depth": ANY(torch.Tensor							), "depth": ANY(Image.Image							)}    ,						__lowerCamelCase							)
            import datasets

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"    ,						"image"    ,						split="test"							)
            _SCREAMING_SNAKE_CASE       :	List[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							)},
                ]    ,						__lowerCamelCase    ,						)




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




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






import cmath
import math






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							math.radians(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							math.radians(__lowerCamelCase	)

     # Convert voltage and current to rectangular form
     _SCREAMING_SNAKE_CASE       :	str						    =							cmath.rect(__lowerCamelCase,       __lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	str						    =							cmath.rect(__lowerCamelCase,       __lowerCamelCase	)

     # Calculate apparent power
     return voltage_rect * current_rect


if __name__ == "__main__":
       import doctest

       doctest.testmod()