


"""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 	__snake_case							(					*__UpperCAmelCase				:    str				, **__UpperCAmelCase				:    Union[str, Any]):
       pass


def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







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







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







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       MODEL_FOR_DEPTH_ESTIMATION_MAPPING






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Optional[int]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Tuple):
      a						:		List[Any]      							=			DepthEstimationPipeline(model=__UpperCAmelCase				, image_processor=__UpperCAmelCase)
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    str):
      a						:		List[str]      							=			depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png")
      self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}				, __UpperCAmelCase)
      import datasets

      a						:		Any      							=			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"				, "image"				, split="test")
      a						:		Tuple      							=			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)},
          ]				, __UpperCAmelCase				, )






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






    @slow
    @require_torch
    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Any      							=			"Intel/dpt-large"
      a						:		Union[str, Any]      							=			pipeline("depth-estimation"				, model=__UpperCAmelCase)
      a						:		Tuple      							=			depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
      a						:		List[str]      							=			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 	__snake_case							(					self				:    Optional[int]):
      self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT")










'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						float		):
 return math.pow(UpperCAmelCase_					,				2		) - a





def __snake_case					(      UpperCAmelCase_						:						float		):
 return 2 * x





def __snake_case					(      UpperCAmelCase_						:						float		):
 lowerCamelCase_      =  2.0

 while start <= a:
  lowerCamelCase_      =  math.pow(UpperCAmelCase_					,				2		)

 return start





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						int = 9999					,				UpperCAmelCase_						:						float = 0.00_0000_0000_0001		):

 if a < 0:
  raise ValueError("math domain error"		)

 lowerCamelCase_      =  get_initial_point(UpperCAmelCase_		)

 for _ in range(UpperCAmelCase_		):
  lowerCamelCase_      =  value
  lowerCamelCase_      =  value - fx(UpperCAmelCase_					,				UpperCAmelCase_		) / fx_derivative(UpperCAmelCase_		)
  if abs(prev_value - value		) < tolerance:
   return value

 return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()



