






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  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Dict	    =   hashlib.mda(image.tobytes()					)
	return m.hexdigest()




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





	"""simple docstring"""




	_A       :				Optional[int]    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Any	    =   DepthEstimationPipeline(model=lowerCAmelCase_     ,     image_processor=lowerCAmelCase_							)
		return depth_estimator, [
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		]





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		self.assertEqual({"""predicted_depth""": ANY(torch.Tensor							), """depth""": ANY(Image.Image							)}     ,     lowerCAmelCase_							)
		import datasets

		A_		:						List[str]	    =   datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""     ,     """image"""     ,     split="""test"""							)
		A_		:						Optional[Any]	    =   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    lowerCamelCase(self							):
		pass





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   """Intel/dpt-large"""
		A_		:						Optional[Any]	    =   pipeline("""depth-estimation"""     ,     model=lowerCAmelCase_							)
		A_		:						List[str]	    =   depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
		A_		:						Union[str, Any]	    =   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    lowerCamelCase(self							):
		# 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"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

