


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     _lowercase				:


											"""simple docstring"""




											@staticmethod
											def 		lowerCAmelCase  (     *__lowerCamelCase							:    str      ,   **__lowerCamelCase							:    Optional[int] ):


																	'''simple docstring'''



																	pass

def    lowercase_	(	_A    :					Image		):

						"""simple docstring"""




						lowerCamelCase__	:       Any					=						hashlib.mda(image.tobytes()		)
						return m.hexdigest()







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


							"""simple docstring"""




							A__      		=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING





							def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    List[str] ):


													'''simple docstring'''



													lowerCamelCase__	:       Union[str, Any]					=						DepthEstimationPipeline(model=lowercase__      ,   image_processor=lowercase__ )
													return depth_estimator, [
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													    "./tests/fixtures/tests_samples/COCO/000000039769.png",
													]





							def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Any ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
													self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )}      ,   lowercase__ )
													import datasets

													lowerCamelCase__	:       Any					=						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"      ,   "image"      ,   split="test" )
													lowerCamelCase__	:       Optional[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 )},
													    ]      ,   lowercase__      ,   )





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


													'''simple docstring'''



													pass





							@slow
							@require_torch
							def 		lowerCAmelCase  (     self							:    List[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						"Intel/dpt-large"
													lowerCamelCase__	:       int					=						pipeline("depth-estimation"      ,   model=lowercase__ )
													lowerCamelCase__	:       Tuple					=						depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
													lowerCamelCase__	:       Union[str, Any]					=						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 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



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






def    lowercase_	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"		)

      lowerCamelCase__	:       List[str]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"
      lowerCamelCase__	:       List[Any]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"

      lowerCamelCase__	:       List[Any]					=						max(len(_A		)					,      len(_A		)		)

      return "0b" + "".join(
          str(int(char_a != char_b		)		)
          for char_a, char_b in zip(a_binary.zfill(_A		)					,      b_binary.zfill(_A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


