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        _UpperCAmelCase				(       *A_						,					**A_	):







															'''simple docstring'''


															pass

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Image )   ->       str:
				_UpperCAmelCase       :		Dict 		=    hashlib.mda(image.tobytes() )
				return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				a  (	unittest.TestCase				):
						_lowercase    						=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    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						,					A_						,					A_	):







										'''simple docstring'''


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


						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@slow
						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







										'''simple docstring'''


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





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       int:
				if len(lowerCAmelCase ) != len(lowerCAmelCase ):
								raise ValueError("String lengths must match!" )

				_UpperCAmelCase       :		List[Any] 		=    0

				for chara, chara in zip(lowerCAmelCase  ,      lowerCAmelCase ):
								if chara != chara:
												count += 1

				return count


if __name__ == "__main__":
					import doctest

					doctest.testmod()


