'''simple docstring'''




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


_a   :	List[Any] 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}






class 		__A       (_lowerCamelCase   ):
       snake_case       :Optional[Any]				    =					"timesformer"






       def __init__(      self				,     UpperCamelCase_=2_24				,     UpperCamelCase_=16				,     UpperCamelCase_=3				,     UpperCamelCase_=8				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-6				,     UpperCamelCase_=True				,     UpperCamelCase_="divided_space_time"				,     UpperCamelCase_=0				,     **UpperCamelCase_				,     ):
              super().__init__(**A__      )

              __UpperCAmelCase  :				Optional[Any]       				=		image_size
              __UpperCAmelCase  :				List[Any]       				=		patch_size
              __UpperCAmelCase  :				List[Any]       				=		num_channels
              __UpperCAmelCase  :				Union[str, Any]       				=		num_frames

              __UpperCAmelCase  :				Union[str, Any]       				=		hidden_size
              __UpperCAmelCase  :				Any       				=		num_hidden_layers
              __UpperCAmelCase  :				Any       				=		num_attention_heads
              __UpperCAmelCase  :				Any       				=		intermediate_size
              __UpperCAmelCase  :				List[Any]       				=		hidden_act
              __UpperCAmelCase  :				Dict       				=		hidden_dropout_prob
              __UpperCAmelCase  :				List[Any]       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				Optional[int]       				=		initializer_range
              __UpperCAmelCase  :				Union[str, Any]       				=		layer_norm_eps
              __UpperCAmelCase  :				Tuple       				=		qkv_bias

              __UpperCAmelCase  :				List[str]       				=		attention_type
              __UpperCAmelCase  :				List[Any]       				=		drop_path_rate







'''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]))






