





"""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   __SCREAMING_SNAKE_CASE     :







    '''simple docstring'''


    @staticmethod
    def  __SCREAMING_SNAKE_CASE					(		*__a :							Dict       , **__a :							int						)		->					str:
          pass




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							hashlib.mda(image.tobytes()      )
      return m.hexdigest()



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







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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

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

  @slow
  @require_torch
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Dict:
        _UpperCamelCase   :						Optional[Any]    =							"Intel/dpt-large"
        _UpperCamelCase   :						Dict    =							pipeline("depth-estimation"       , model=__a						)
        _UpperCamelCase   :						Optional[int]    =							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()						)       , 29.3_04						)
        self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()						)       , 2.6_62						)


  @require_torch
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					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"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
