





'''simple docstring'''




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a__					:			Dict          =     logging.get_logger(__name__)






@add_end_docstrings(__magic_name__							)
class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		Dict:
		super().__init__(*a_      ,       **a_		)
		requires_backends(self      ,       'vision'		)
		self.check_model_type(a_		)





	def __call__(		self      ,       UpperCamelCase_      ,       **UpperCamelCase_		)      ->		str:
		return super().__call__(a_      ,       **a_		)





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Dict:
		return {}, {}, {}





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   load_image(a_		)
		snake_case__	       =   image.size
		snake_case__	       =   self.image_processor(images=a_      ,       return_tensors=self.framework		)
		return model_inputs





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		int:
		snake_case__	       =   self.model(**a_		)
		return model_outputs





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   model_outputs.predicted_depth
		snake_case__	       =   torch.nn.functional.interpolate(
		    predicted_depth.unsqueeze(1		)      ,       size=self.image_size[::-1]      ,       mode='bicubic'      ,       align_corners=a_		)
		snake_case__	       =   prediction.squeeze().cpu().numpy()
		snake_case__	       =   (output * 255 / np.max(a_		)).astype('uint8'		)
		snake_case__	       =   Image.fromarray(a_		)
		snake_case__	       =   {}
		snake_case__	       =   predicted_depth
		snake_case__	       =   depth
		return output_dict








from collections import Counter

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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





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