




from collections import Counter

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

__lowerCAmelCase		:				Any       			=		datasets.load_iris()

__lowerCAmelCase		:				List[Any]       			=		np.array(data['data'])
__lowerCAmelCase		:				List[str]       			=		np.array(data['target'])
__lowerCAmelCase		:				Dict       			=		data['target_names']

__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase		:				List[str]       			=		train_test_split(X, y)




def 			__magic_name__				( A		:					Optional[Any],  A		:					Optional[Any]   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					Tuple,  A		:					List[Any],  A		:					int,  A		:					str,  A		:					int=5   ):






			'''simple docstring'''







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


__lowerCAmelCase		:				Optional[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					"""openai-gpt"""
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=4_04_78					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     List[str]=7_68					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=12					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=1e-5					,      __lowerCamelCase				:     Optional[int]=0.02					,      __lowerCamelCase				:     Optional[int]="cls_index"					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Tuple=0.1					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			vocab_size
							a    =			n_positions
							a    =			n_embd
							a    =			n_layer
							a    =			n_head
							a    =			afn
							a    =			resid_pdrop
							a    =			embd_pdrop
							a    =			attn_pdrop
							a    =			layer_norm_epsilon
							a    =			initializer_range
							a    =			summary_type
							a    =			summary_use_proj
							a    =			summary_activation
							a    =			summary_first_dropout
							a    =			summary_proj_to_labels
							super().__init__(**__lowerCamelCase							)






