



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    SCREAMING_SNAKE_CASE			:





								@staticmethod
								def 						UpperCamelCase_					(   *__lowercase		:		List[Any]      ,				**__lowercase		:		int	):

												'''simple docstring'''
												pass

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Image    ):
				"""simple docstring"""







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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =MODEL_FOR_DEPTH_ESTIMATION_MAPPING





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		Any      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		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		:		Tuple      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
											self.assertEqual({"""predicted_depth""": ANY(torch.Tensor	), """depth""": ANY(Image.Image	)}      ,				A__	)
											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	)},
											    ]      ,				A__      ,				)





							@require_tf
							@unittest.skip("""Depth estimation is not implemented in TF"""	)
							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											pass





							@slow
							@require_torch
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		"""Intel/dpt-large"""
											__a       =		pipeline("""depth-estimation"""      ,				model=A__	)
											__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()	)      ,				29.304	)
											self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()	)      ,				2.662	)







							@require_torch
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''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"""	)





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 3				,     _SCREAMING_SNAKE_CASE       :			int = 7				,     _SCREAMING_SNAKE_CASE       :			int = 100_0000    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1

				for current_denominator in range(1				,     limit + 1    ):
								__a       =		current_denominator * numerator // denominator
								if current_denominator % denominator == 0:
												current_numerator -= 1
								if current_numerator * max_denominator > current_denominator * max_numerator:
												__a       =		current_numerator
												__a       =		current_denominator
				return max_numerator


if __name__ == "__main__":
						print(solution(numerator=3, denominator=7, limit=100_0000))
