


from collections import Counter

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

snake_case__			 =						datasets.load_iris()

snake_case__			 =						np.array(data['''data'''])
snake_case__			 =						np.array(data['''target'''])
snake_case__			 =						data['''target_names''']

snake_case__ ,snake_case__ ,snake_case__ ,snake_case__			 =						train_test_split(X, y)


def 				lowerCamelCase__	(      a	:   Any	,  a	:   List[str]				)    ->	Tuple:





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


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   List[str]	,  a	:   Any	,  a	:   List[Any]	,  a	:   List[Any]=5				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :int						  =    zip(a	,  a				)
			# List of distances of all points from the point to be classified
			a__ :Tuple						  =    []
			for data_point in data:
						a__ :Any						  =    euclidean_distance(data_point[0]	,  a				)
						distances.append((distance, data_point[1])				)
			# Choosing 'k' points with the least distances.
			a__ :Dict						  =    [i[1] for i in sorted(a				)[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			a__ :Union[str, Any]						  =    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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


