



'''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 							__lowercase      (							*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
																pass



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		_a					:				Tuple				=							hashlib.mda(image.tobytes()  )
		return m.hexdigest()

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   List[Any]           = MODEL_FOR_DEPTH_ESTIMATION_MAPPING







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







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

									_a					:				Optional[int]				=							datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,'image'     ,split='test'		)
									_a					:				Dict				=							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		)},
									    ]     ,_UpperCAmelCase     ,)







							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'		)
							def 							__lowercase      (							self :	List[str]		):
									pass







							@slow
							@require_torch
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							'Intel/dpt-large'
									_a					:				Optional[int]				=							pipeline('depth-estimation'     ,model=_UpperCAmelCase		)
									_a					:				Optional[int]				=							depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'		)
									_a					:				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 							__lowercase      (							self :	Tuple		):
									# 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'''

__lowerCAmelCase						      =       range(2, 20 + 1)
__lowerCAmelCase						      =       [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase						      =       {}



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Optional[Any]:
		_a					:				Optional[int]				=							sum(a_i[j] for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  )
		_a					:				List[str]				=							sum(a_i[j] * base[j] for j in range(min(len(lowerCAmelCase_  )						,      lowerCAmelCase_  )  )  )

		_a	, _a					:				Any				=							0, 0
		_a					:				Any				=							n - i

		_a					:				List[Any]				=							memo.get(lowerCAmelCase_  )

		if sub_memo is not None:
				_a					:				Tuple				=							sub_memo.get(lowerCAmelCase_  )

				if jumps is not None and len(lowerCAmelCase_  ) > 0:
						# find and make the largest jump without going over
						_a					:				Any				=							-1
						for _k in range(len(lowerCAmelCase_  ) - 1						,      -1						,      -1  ):
								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
										_a					:				Any				=							_k
										break

						if max_jump >= 0:
								_a	, _a	, _a					:				Tuple				=							jumps[max_jump]
								# since the difference between jumps is cached, add c
								_a					:				Union[str, Any]				=							diff + c
								for j in range(min(lowerCAmelCase_						,      len(lowerCAmelCase_  )  )  ):
										_a	, _a					:				Dict				=							divmod(lowerCAmelCase_						,      10  )
								if new_c > 0:
										add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

				else:
						_a					:				Tuple				=							[]
		else:
				_a					:				Any				=							{c: []}
				_a					:				Optional[Any]				=							sub_memo

		if dn >= max_dn or c + diff >= base[k]:
				return diff, dn

		if k > ks[0]:
				while True:
						# keep doing smaller jumps
						_a	, _a					:				Dict				=							next_term(lowerCAmelCase_						,      k - 1						,      i + dn						,      lowerCAmelCase_  )
						diff += _diff
						dn += terms_jumped

						if dn >= max_dn or c + diff >= base[k]:
								break
		else:
				# would be too small a jump, just compute sequential terms instead
				_a	, _a					:				Any				=							compute(lowerCAmelCase_						,      lowerCAmelCase_						,      i + dn						,      lowerCAmelCase_  )
				diff += _diff
				dn += terms_jumped

		_a					:				Tuple				=							sub_memo[c]

		# keep jumps sorted by # of terms skipped
		_a					:				Any				=							0
		while j < len(lowerCAmelCase_  ):
				if jumps[j][1] > dn:
						break
				j += 1

		# cache the jump for this value digitsum(b) and c
		sub_memo[c].insert(lowerCAmelCase_						,      (diff, dn, k)  )
		return (diff, dn)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		if i >= n:
				return 0, i
		if k > len(lowerCAmelCase_  ):
				a_i.extend([0 for _ in range(k - len(lowerCAmelCase_  )  )]  )

		# note: a_i -> b * 10^k + c
		# ds_b -> digitsum(b)
		# ds_c -> digitsum(c)
		_a					:				Any				=							i
		_a	, _a	, _a					:				Optional[int]				=							0, 0, 0
		for j in range(len(lowerCAmelCase_  )  ):
				if j >= k:
						ds_b += a_i[j]
				else:
						ds_c += a_i[j]

		while i < n:
				i += 1
				_a					:				Any				=							ds_c + ds_b
				diff += addend
				_a					:				int				=							0
				for j in range(lowerCAmelCase_  ):
						_a					:				Optional[Any]				=							a_i[j] + addend
						_a	, _a					:				Tuple				=							divmod(lowerCAmelCase_						,      10  )

						ds_c += a_i[j]

				if addend > 0:
						break

		if addend > 0:
				add(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		return diff, i - start_i



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		for j in range(lowerCAmelCase_						,      len(lowerCAmelCase_  )  ):
				_a					:				Optional[Any]				=							digits[j] + addend
				if s >= 10:
						_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
						_a					:				List[str]				=							addend // 10 + quotient
				else:
						_a					:				Optional[Any]				=							s
						_a					:				Optional[Any]				=							addend // 10

				if addend == 0:
						break

		while addend > 0:
				_a	, _a					:				List[str]				=							divmod(lowerCAmelCase_						,      10  )
				digits.append(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_ = 10**15  )      ->				int:
		_a					:				Dict				=							[1]
		_a					:				int				=							1
		_a					:				Tuple				=							0
		while True:
				_a	, _a					:				str				=							next_term(lowerCAmelCase_						,      20						,      i + dn						,      lowerCAmelCase_  )
				dn += terms_jumped
				if dn == n - i:
						break

		_a					:				Union[str, Any]				=							0
		for j in range(len(lowerCAmelCase_  )  ):
				a_n += digits[j] * 10**j
		return a_n


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




