


"""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 			lowerCamelCase__						:







							@staticmethod
							def __a						(							*_lowercase   :    Dict			,			**_lowercase   :    str		):
									pass







def  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





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



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING







						def __a						(							self   :    Any			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Tuple		):
								A					       = DepthEstimationPipeline(model=_lowercase			,			image_processor=_lowercase		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







						def __a						(							self   :    List[str]			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[Any]		):
								A					       = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
								self.assertEqual({'predicted_depth': ANY(torch.Tensor		), 'depth': ANY(Image.Image		)}			,			_lowercase		)
								import datasets

								A					       = datasets.load_dataset('hf-internal-testing/fixtures_image_utils'			,			'image'			,			split='test'		)
								A					       = 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 __a						(							self   :    Union[str, Any]		):
								pass







						@slow
						@require_torch
						def __a						(							self   :    str		):
								A					       = 'Intel/dpt-large'
								A					       = pipeline('depth-estimation'			,			model=_lowercase		)
								A					       = depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'		)
								A					       = 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 __a						(							self   :    Optional[int]		):
								# 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







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		if not nums:
				return 0
		A					       = nums[0]
		A					       = 0
		for num in nums[1:]:
				A       , A					       = (
				    max_excluding + num,
				    max(UpperCamelCase__ ,			UpperCamelCase__	),
				)
		return max(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





