




"""simple docstring"""







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       PhobertTokenizer
   A_				      =       False







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

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase              =	['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
       UpperCamelCase              =	dict(zip(lowerCamelCase_       ,					range(len(lowerCamelCase_))))
       UpperCamelCase              =	['''#version: 0.2''', '''l à</w>''']
       UpperCamelCase              =	{'''unk_token''': '''<unk>'''}

       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''vocab_file'''])
       UpperCamelCase              =	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(lowerCamelCase_))







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Any:
       kwargs.update(self.special_tokens_map)
       return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       UpperCamelCase              =	'''Tôi là VinAI Research'''
       UpperCamelCase              =	'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
       return input_text, output_text







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map)
       UpperCamelCase              =	'''Tôi là VinAI Research'''
       UpperCamelCase              =	'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
       UpperCamelCase              =	tokenizer.tokenize(lowerCamelCase_)
       print(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	tokens + [tokenizer.unk_token]

       UpperCamelCase              =	[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)       ,					lowerCamelCase_)






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      List[str]      =		XGBRegressor(verbosity=0   ,    random_state=42			)
		xgb.fit(_lowercase   ,    _lowercase			)
		# Predict target for test data
		lowercase__					:      Union[str, Any]      =		xgb.predict(_lowercase			)
		lowercase__					:      Tuple      =		predictions.reshape(len(_lowercase			)   ,    1			)
		return predictions


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


