


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class     a		(tf.keras.layers.Layer				):




							"""simple docstring"""


							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  int = None   ,     lowerCamelCase	:  int = None    )     ->   str:
														super().__init__()
														__snake_case		:      List[str]										=       pad_token_id
														__snake_case		:      Any										=       max_length
														__snake_case		:      List[Any]										=       vocab
														__snake_case		:      Optional[Any]										=       merges
														__snake_case		:      Optional[int]										=       BytePairTokenizer(lowerCamelCase   ,     lowerCamelCase   ,     sequence_length=lowerCamelCase    )

							@classmethod
							def    __snake_case (							cls	:  Optional[Any]   ,     lowerCamelCase	:  GPTaTokenizer   ,     *lowerCamelCase	:  Dict   ,     **lowerCamelCase	:  Optional[int]    )     ->   Any:
														__snake_case		:      List[str]										=       [" ".join(lowerCamelCase    ) for m in tokenizer.bpe_ranks.keys()]
														__snake_case		:      int										=       tokenizer.get_vocab()
														return cls(lowerCamelCase   ,     lowerCamelCase   ,     *lowerCamelCase   ,     **lowerCamelCase    )

							@classmethod
							def    __snake_case (							cls	:  List[Any]   ,     lowerCamelCase	:  Union[str, os.PathLike]   ,     *lowerCamelCase	:  int   ,     **lowerCamelCase	:  Any    )     ->   List[Any]:
														__snake_case		:      Tuple										=       GPTaTokenizer.from_pretrained(lowerCamelCase   ,     *lowerCamelCase   ,     **lowerCamelCase    )
														return cls.from_tokenizer(lowerCamelCase   ,     *lowerCamelCase   ,     **lowerCamelCase    )

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  Dict    )     ->   Any:
														return cls(**lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Dict:
														return {
														    "vocab": self.vocab,
														    "merges": self.merges,
														    "max_length": self.max_length,
														    "pad_token_id": self.pad_token_id,
														}






							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  int = None    )     ->   str:
														__snake_case		:      List[str]										=       self.tf_tokenizer(lowerCamelCase    )
														__snake_case		:      Tuple										=       tf.ones_like(lowerCamelCase    )

														if self.pad_token_id is not None:
																					# pad the tokens up to max length
																					__snake_case		:      Optional[Any]										=       max_length if max_length is not None else self.max_length

																					if max_length is not None:
																												__snake_case			,      __snake_case		:      List[str]										=       pad_model_inputs(
																												    lowerCamelCase   ,     max_seq_length=lowerCamelCase   ,     pad_value=self.pad_token_id    )

														return {"attention_mask": attention_mask, "input_ids": input_ids}





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]))

