





"""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 							lowercase__			:

					"""simple docstring"""







					@staticmethod
					def   _a      (      *_A						,   **_A    ):






											'''simple docstring'''







											pass

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			hashlib.mda(image.tobytes()     )
						return m.hexdigest()






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

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[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   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Dict				   =			datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,   """image"""						,   split="""test"""    )
									UpperCamelCase				:		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    )},
									    ]						,   _A						,   )







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






									'''simple docstring'''







									pass







			@slow
			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			"""Intel/dpt-large"""
									UpperCamelCase				:		Union[str, Any]				   =			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()    )						,   29.3_04    )
									self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()    )						,   2.6_62    )







			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







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






"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






