





"""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 							lowercase__			:

					"""simple docstring"""







					@staticmethod
					def   _a      (      *_A						,   **_A    ):






											'''simple docstring'''







											pass

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			hashlib.mda(image.tobytes()     )
						return m.hexdigest()






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

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			DepthEstimationPipeline(model=_A						,   image_processor=_A    )
									return depth_estimator, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
									self.assertEqual({"""predicted_depth""": ANY(torch.Tensor    ), """depth""": ANY(Image.Image    )}						,   _A    )
									import datasets

									UpperCamelCase				:		Dict				   =			datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,   """image"""						,   split="""test"""    )
									UpperCamelCase				:		Tuple				   =			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   _a      (      self    ):






									'''simple docstring'''







									pass







			@slow
			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			"""Intel/dpt-large"""
									UpperCamelCase				:		Union[str, Any]				   =			pipeline("""depth-estimation"""						,   model=_A    )
									UpperCamelCase				:		Optional[Any]				   =			depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
									UpperCamelCase				:		Union[str, 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   _a      (      self    ):






									'''simple docstring'''







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










"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						UpperCamelCase				:		str				   =			""""""
						UpperCamelCase				:		Dict				   =			""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE     ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0

						# length[i] shows the length of palindromic substring with center i
						UpperCamelCase				:		List[Any]				   =			[1 for i in range(len(SCREAMING_SNAKE_CASE     )     )]

						# for each character in new_string find corresponding palindromic string
						UpperCamelCase				:		Optional[Any]				   =			0
						for j in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		Union[str, Any]				   =			1 if j > r else min(length[l + r - j] // 2							,     r - j + 1     )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE     )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												UpperCamelCase				:		int				   =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		UpperCamelCase				:		int				   =			j - k + 1  # noqa: E741
																		UpperCamelCase				:		List[Any]				   =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		UpperCamelCase				:		List[str]				   =			length[j]
																		UpperCamelCase				:		List[str]				   =			j

    # create that string
						UpperCamelCase				:		Optional[Any]				   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



