




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					:

								"""simple docstring"""







								@staticmethod
								def  lowercase_       ( *lowerCamelCase__     ,		**lowerCamelCase__				)       -> Dict:



												'''simple docstring'''
												pass







def      lowerCamelCase_						(			UpperCamelCase__							: Image )	->			str:





				"""simple docstring"""


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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       DepthEstimationPipeline(model=lowerCamelCase__     ,		image_processor=lowerCamelCase__				)
							return depth_estimator, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							self.assertEqual({'predicted_depth': ANY(torch.Tensor				), 'depth': ANY(Image.Image				)}     ,		lowerCamelCase__				)
							import datasets

							__lowerCamelCase									=       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,		'image'     ,		split='test'				)
							__lowerCamelCase									=       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  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@slow
			@require_torch
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       'Intel/dpt-large'
							__lowerCamelCase									=       pipeline('depth-estimation'     ,		model=lowerCamelCase__				)
							__lowerCamelCase									=       depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'				)
							__lowerCamelCase									=       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				)       -> Optional[Any]:



							'''simple docstring'''
							# This is highly irregular to have no small tests.
							self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'				)








from string import ascii_uppercase

__A								=       {str(ord(c) - 55): c for c in ascii_uppercase}







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: int )	->			str:





				"""simple docstring"""


				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('int() can\'t convert non-string with explicit base' )
				if num < 0:
								raise ValueError('parameter must be positive int' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'str\' object cannot be interpreted as an integer' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'float\' object cannot be interpreted as an integer' )
				if base in (0, 1):
								raise ValueError('base must be >= 2' )
				if base > 36:
								raise ValueError('base must be <= 36' )
				__lowerCamelCase									=       ''
				__lowerCamelCase									=       0
				__lowerCamelCase									=       0
				while div != 1:
								__lowerCamelCase			,		__lowerCamelCase									=       divmod(UpperCamelCase__					,	UpperCamelCase__ )
								if base >= 11 and 9 < mod < 36:
												__lowerCamelCase									=       ALPHABET_VALUES[str(UpperCamelCase__ )]
								else:
												__lowerCamelCase									=       str(UpperCamelCase__ )
								new_value += actual_value
								__lowerCamelCase									=       num // base
								__lowerCamelCase									=       div
								if div == 0:
												return str(new_value[::-1] )
								elif div == 1:
												new_value += str(UpperCamelCase__ )
												return str(new_value[::-1] )

				return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 37):
										for num in range(10_00):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)


