
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 			__A:




									@staticmethod
									def 				SCREAMING_SNAKE_CASE_		(     *_snake_case		,     **_snake_case				)    ->      Optional[int]:


													'''simple docstring'''




													pass


def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    hashlib.mda(image.tobytes()  )
				return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			__A( unittest.TestCase			):
				snake_case_						   =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    DepthEstimationPipeline(model=_snake_case		,     image_processor=_snake_case				)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




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




				@require_tf
				@unittest.skip('''Depth estimation is not implemented in TF'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								pass




				@slow
				@require_torch
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    '''Intel/dpt-large'''
								__a										=    pipeline('''depth-estimation'''		,     model=_snake_case				)
								__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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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

from __future__ import annotations

import typing
from collections import Counter


def     __lowerCAmelCase			(			a__  )       ->  typing.Counter[int]:
				__a										=    Counter()
				for base in range(1						,    max_perimeter + 1  ):
								for perpendicular in range(a__						,    max_perimeter + 1  ):
												__a										=    (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a__  ):
																__a										=    int(base + perpendicular + hypotenuse  )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets


def     __lowerCAmelCase			(			a__ = 1000  )       ->  int:
				__a										=    pythagorean_triple(a__  )
				return triplets.most_common(1  )[0][0]


if __name__ == "__main__":
					print(F"Perimeter {solution()} has maximum solutions")