



'''simple docstring'''





import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =BertweetTokenizer
  a_							 =False




  def 						_lowercase					(			self				:     Dict       ) -> Dict:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase	:	Optional[Any]   	=			['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
    __lowerCamelCase	:	int   	=			dict(zip(_a      ,     range(len(_a       )       )       )       )
    __lowerCamelCase	:	Union[str, Any]   	=			['#version: 0.2', 'a m</w>']
    __lowerCamelCase	:	Dict   	=			{'unk_token': '<unk>'}

    __lowerCamelCase	:	Tuple   	=			os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']       )
    __lowerCamelCase	:	Tuple   	=			os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']       )
    with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'       ) as fp:
      for token in vocab_tokens:
        fp.write(f'{token} {vocab_tokens[token]}\n'       )
    with open(self.merges_file      ,     'w'      ,     encoding='utf-8'       ) as fp:
      fp.write('\n'.join(_a       )       )




  def 						_lowercase					(			self				:     Tuple      ,     **_a				:     Union[str, Any]       ) -> List[Any]:
    kwargs.update(self.special_tokens_map       )
    return BertweetTokenizer.from_pretrained(self.tmpdirname      ,     **_a       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Optional[Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			'I am VinAI Research'
    __lowerCamelCase	:	Any   	=			'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
    return input_text, output_text




  def 						_lowercase					(			self				:     int       ) -> Optional[int]:
    __lowerCamelCase	:	str   	=			BertweetTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map       )
    __lowerCamelCase	:	Union[str, Any]   	=			'I am VinAI Research'
    __lowerCamelCase	:	Optional[int]   	=			'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
    __lowerCamelCase	:	List[Any]   	=			tokenizer.tokenize(_a       )
    self.assertListEqual(_a      ,     _a       )

    __lowerCamelCase	:	int   	=			tokens + [tokenizer.unk_token]

    __lowerCamelCase	:	Dict   	=			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )      ,     _a       )











'''simple docstring'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






