
"""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 							__magic_name__     :





							@staticmethod
							def 							lowerCAmelCase      (				*snake_case_   :							Optional[int]							,						**snake_case_   :							Tuple    ):
														pass
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       hashlib.mda(image.tobytes() )
							return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							__magic_name__     (		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING





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





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

												__snake_case     			=       datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,						"image"							,						split="test"    )
												__snake_case     			=       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    )},
												    ]							,						snake_case_							,						)





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





					@slow
					@require_torch
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       "Intel/dpt-large"
												__snake_case     			=       pipeline("depth-estimation"							,						model=snake_case_    )
												__snake_case     			=       depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Any    ):
												# 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 __future__ import annotations

from collections.abc import Callable
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1_00							,    )  ->     float:







							"""simple docstring"""


							__snake_case     			=       x_start
							__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
							__snake_case     			=       0.0
							for _ in range(SCREAMING_SNAKE_CASE ):
														# Approximates small segments of curve as linear and solve
														# for trapezoidal area
														__snake_case     			=       (x_end - x_start) / steps + xa
														__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
														area += abs(fxa + fxa ) * (xa - xa) / 2
														# Increment step
														__snake_case     			=       xa
														__snake_case     			=       fxa
							return area


if __name__ == "__main__":
		def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







									"""simple docstring"""


									return x**3 + x**2

		print("""f(x) = x^3 + x^2""")
		print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
		_SCREAMING_SNAKE_CASE	       =      10
		while i <= 100_000:
				print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
				i *= 10
