


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_lowercase       :      Any	=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class       _UpperCamelCase    (					snake_case_				):

				"""simple docstring"""







				lowerCAmelCase 		= field(default=snake_case_    ,		metadata={'help': 'Whether to use SortishSampler or not.'}				)
				lowerCAmelCase 		= field(
				    default=snake_case_    ,		metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}				)
				lowerCAmelCase 		= field(
				    default=snake_case_    ,		metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }    ,		)
				lowerCAmelCase 		= field(
				    default=snake_case_    ,		metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }    ,		)
				lowerCAmelCase 		= field(
				    default=snake_case_    ,		metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }    ,		)

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				super().to_dict()
					for k, v in d.items():
						if isinstance(a__				,   a__   ):
							A								=				v.to_dict()
					return d







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]))



