




'''simple docstring'''




from collections import Counter

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

a_      :		Union[str, Any]							      =       datasets.load_iris()

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

a_      ,       a_      ,       a_      ,       a_      :		str							      =       train_test_split(X, y)





def      a_							(      __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[int]   )							->						Dict:



       """simple docstring"""
       return np.linalg.norm(np.array(snake_case_   ) - np.array(snake_case_   )   )





def      a_							(      __snake_case							:			int   ,       __snake_case							:			str   ,       __snake_case							:			str   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			str=5   )							->						Optional[Any]:



       """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]))











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

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __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

