


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


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

_snake_case					:		str      =    {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[Any]					  =  "roc_bert"

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Optional[Any]=30522   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  List[Any]=3072   ,     lowerCamelCase	:  int="gelu"   ,     lowerCamelCase	:  Optional[Any]=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  int=512   ,     lowerCamelCase	:  Optional[Any]=2   ,     lowerCamelCase	:  Optional[Any]=0.02   ,     lowerCamelCase	:  Any=1E-12   ,     lowerCamelCase	:  Optional[Any]=True   ,     lowerCamelCase	:  int=0   ,     lowerCamelCase	:  Union[str, Any]="absolute"   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  Union[str, Any]=768   ,     lowerCamelCase	:  List[str]=910   ,     lowerCamelCase	:  Any=512   ,     lowerCamelCase	:  List[Any]=24858   ,     lowerCamelCase	:  int=True   ,     **lowerCamelCase	:  str   ,     )     ->   Tuple:
														__snake_case		:      int										=       vocab_size
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Dict										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Tuple										=       intermediate_size
														__snake_case		:      str										=       hidden_act
														__snake_case		:      Dict										=       hidden_dropout_prob
														__snake_case		:      Tuple										=       attention_probs_dropout_prob
														__snake_case		:      List[Any]										=       initializer_range
														__snake_case		:      Tuple										=       type_vocab_size
														__snake_case		:      List[Any]										=       layer_norm_eps
														__snake_case		:      Union[str, Any]										=       use_cache
														__snake_case		:      List[Any]										=       enable_pronunciation
														__snake_case		:      Optional[Any]										=       enable_shape
														__snake_case		:      int										=       pronunciation_embed_dim
														__snake_case		:      List[str]										=       pronunciation_vocab_size
														__snake_case		:      List[str]										=       shape_embed_dim
														__snake_case		:      Optional[Any]										=       shape_vocab_size
														__snake_case		:      str										=       concat_input
														__snake_case		:      Any										=       position_embedding_type
														__snake_case		:      List[str]										=       classifier_dropout
														super().__init__(pad_token_id=__A   ,     **__A    )





from collections import Counter

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

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

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       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]))

