


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowercase       :      Optional[int]	=	{
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'xlm-roberta-xl'

				def __init__(						self				,   a__=25_0880				,   a__=2560				,   a__=36				,   a__=32				,   a__=1_0240				,   a__="gelu"				,   a__=0.1				,   a__=0.1				,   a__=514				,   a__=1				,   a__=0.02				,   a__=1e-05				,   a__=1				,   a__=0				,   a__=2				,   a__="absolute"				,   a__=True				,   a__=None				,   **a__				,   )	->				Any:
					super().__init__(pad_token_id=a__				,   bos_token_id=a__				,   eos_token_id=a__				,   **a__   )
					A								=				vocab_size
					A								=				hidden_size
					A								=				num_hidden_layers
					A								=				num_attention_heads
					A								=				hidden_act
					A								=				intermediate_size
					A								=				hidden_dropout_prob
					A								=				attention_probs_dropout_prob
					A								=				max_position_embeddings
					A								=				type_vocab_size
					A								=				initializer_range
					A								=				layer_norm_eps
					A								=				position_embedding_type
					A								=				use_cache
					A								=				classifier_dropout






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						A								=				{0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
						A								=				{0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )







from collections import Counter

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

_lowercase       :      Any	=	datasets.load_iris()

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







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



