
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 			_lowerCAmelCase      :




							@staticmethod
							def   __a		(      *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
										pass
def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


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





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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			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       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							self.assertEqual({"predicted_depth": ANY(torch.Tensor  ), "depth": ANY(Image.Image  )}       ,       _A  )
							import datasets

							lowerCAmelCase_      			=			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"       ,       "image"       ,       split="test"  )
							lowerCAmelCase_      			=			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  )       ->   Dict:
							pass




				@slow
				@require_torch
				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			"Intel/dpt-large"
							lowerCAmelCase_      			=			pipeline("depth-estimation"       ,       model=_A  )
							lowerCAmelCase_      			=			depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"  )
							lowerCAmelCase_      			=			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   __a		(      self  )       ->   Optional[int]:
							# This is highly irregular to have no small tests.
							self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"  )





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			return np.linalg.norm(np.array(__lowerCAmelCase       ) - np.array(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			zip(__lowerCAmelCase					,							__lowerCAmelCase       )
			# List of distances of all points from the point to be classified
			lowerCAmelCase_      			=			[]
			for data_point in data:
						lowerCAmelCase_      			=			euclidean_distance(data_point[0]					,							__lowerCAmelCase       )
						distances.append((distance, data_point[1])       )
			# Choosing 'k' points with the least distances.
			lowerCAmelCase_      			=			[i[1] for i in sorted(__lowerCAmelCase       )[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			lowerCAmelCase_      			=			Counter(__lowerCAmelCase       ).most_common(1       )[0][0]
			return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



