"""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 							__UpperCAmelCase					( *lowerCamelCase_    :       Any			,					**lowerCamelCase_    :       Union[str, Any]						):

											"""simple docstring"""






											pass


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							hashlib.mda(image.tobytes()			)
	return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  _lowerCamelCase			(unittest.TestCase     ):
				_snake_case     =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							DepthEstimationPipeline(model=__A			,					image_processor=__A						)
					return depth_estimator, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
					self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}			,					__A						)
					import datasets

					_lowercase :    Any							=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'			,					'image'			,					split='test'						)
					_lowercase :    Optional[int]							=							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 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					pass







				@slow
				@require_torch
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    str							=							"Intel/dpt-large"
					_lowercase :    Dict							=							pipeline('depth-estimation'			,					model=__A						)
					_lowercase :    Optional[int]							=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
					_lowercase :    List[str]							=							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()						)			,					2.662						)







				@require_torch
				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






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


"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

	for i in range(3     ,int(limit**0.5 + 1			)     ,2			):
		_lowercase :    Optional[Any]							=							i * 2
		while index < limit:
			_lowercase :    int							=							False
			_lowercase :    Tuple							=							index + i

	_lowercase :    Optional[Any]							=							[2]

	for i in range(3     ,__UpperCAmelCase     ,2			):
		if is_prime[i]:
			primes.append(__UpperCAmelCase			)

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

	for i in range(len(__UpperCAmelCase			)			):
		for j in range(i + length     ,len(__UpperCAmelCase			)			):
			_lowercase :    List[Any]							=							sum(primes[i:j]			)
			if sol >= ceiling:
				break

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


if __name__ == "__main__":
						print(f"""{solution() = }""")



