






"""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      (    *lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Dict:
													pass





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    hashlib.mda(image.tobytes()       )
				return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowerCAmelCase						(    unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				MODEL_FOR_DEPTH_ESTIMATION_MAPPING







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    DepthEstimationPipeline(model=lowerCAmelCase_   ,				image_processor=lowerCAmelCase_							)
										return depth_estimator, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
										self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}   ,				lowerCAmelCase_							)
										import datasets

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    datasets.load_dataset('hf-internal-testing/fixtures_image_utils'   ,				'image'   ,				split='test'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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							)},
										    ]   ,				lowerCAmelCase_   ,				)







						@require_tf
						@unittest.skip('Depth estimation is not implemented in TF'							)
						def 			A      (    self							)		->							Tuple:
										pass







						@slow
						@require_torch
						def 			A      (    self							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		Any       		=    'Intel/dpt-large'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pipeline('depth-estimation'   ,				model=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    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 			A      (    self							)		->							Union[str, 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"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    size
										_SCREAMING_SNAKE_CASE   :		str       		=    [0] * size
										_SCREAMING_SNAKE_CASE   :		Any       		=    [0] * size







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return index | (index + 1)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return (index & (index + 1)) - 1







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		str       		=    value
										while index < self.size:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_prev(lowerCAmelCase_							) + 1
														if current_left_border == index:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    value
														else:
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		str       		=    self.get_next(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										right -= 1  # Because of right is exclusive
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0
										while left <= right:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_prev(lowerCAmelCase_							)
														if left <= current_left:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    max(lowerCAmelCase_   ,				self.tree[right]							)
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current_left
														else:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    max(lowerCAmelCase_   ,				self.arr[right]							)
																		right -= 1
										return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()

