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


_A     =  logging.get_logger(__name__)

_A     =  {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}





class 			_lowerCAmelCase      (			UpperCAmelCase__ ):
				_lowercase    	='gpt_neox_japanese'




				def __init__(      self       ,       _UpperCamelCase=32_000       ,       _UpperCamelCase=2_560       ,       _UpperCamelCase=32       ,       _UpperCamelCase=32       ,       _UpperCamelCase=4       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=1.00       ,       _UpperCamelCase=10_000       ,       _UpperCamelCase=2_048       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-5       ,       _UpperCamelCase=True       ,       _UpperCamelCase=31_996       ,       _UpperCamelCase=31_999       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.0       ,       **_UpperCamelCase       ,       )       ->   Any:
							super().__init__(bos_token_id=_UpperCamelCase       ,       eos_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )
							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			max_position_embeddings
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			intermediate_multiple_size
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			rotary_pct
							lowerCAmelCase_      			=			rotary_emb_base
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			layer_norm_eps
							lowerCAmelCase_      			=			use_cache
							lowerCAmelCase_      			=			attention_dropout
							lowerCAmelCase_      			=			hidden_dropout





from collections import Counter

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

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



