'''simple docstring'''




from collections import Counter

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

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


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







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"cvt"






       def __init__(      self				,     UpperCamelCase_=3				,     UpperCamelCase_=[7, 3, 3]				,     UpperCamelCase_=[4, 2, 2]				,     UpperCamelCase_=[2, 1, 1]				,     UpperCamelCase_=[64, 1_92, 3_84]				,     UpperCamelCase_=[1, 3, 6]				,     UpperCamelCase_=[1, 2, 10]				,     UpperCamelCase_=[4.0, 4.0, 4.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.1]				,     UpperCamelCase_=[True, True, True]				,     UpperCamelCase_=[False, False, True]				,     UpperCamelCase_=["dw_bn", "dw_bn", "dw_bn"]				,     UpperCamelCase_=[3, 3, 3]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[2, 2, 2]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		patch_sizes
              __UpperCAmelCase  :				List[str]       				=		patch_stride
              __UpperCAmelCase  :				Tuple       				=		patch_padding
              __UpperCAmelCase  :				int       				=		embed_dim
              __UpperCAmelCase  :				str       				=		num_heads
              __UpperCAmelCase  :				Any       				=		depth
              __UpperCAmelCase  :				List[str]       				=		mlp_ratio
              __UpperCAmelCase  :				List[str]       				=		attention_drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_path_rate
              __UpperCAmelCase  :				str       				=		qkv_bias
              __UpperCAmelCase  :				Optional[int]       				=		cls_token
              __UpperCAmelCase  :				Optional[Any]       				=		qkv_projection_method
              __UpperCAmelCase  :				Tuple       				=		kernel_qkv
              __UpperCAmelCase  :				Optional[Any]       				=		padding_kv
              __UpperCAmelCase  :				Optional[int]       				=		stride_kv
              __UpperCAmelCase  :				Any       				=		padding_q
              __UpperCAmelCase  :				List[Any]       				=		stride_q
              __UpperCAmelCase  :				Union[str, Any]       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		layer_norm_eps






