



"""simple docstring"""
import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase						:		Tuple			= get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  unittest.TestCase   ):
				lowercase__      =   BartphoTokenizer
				lowercase__      =   False
				lowercase__      =   True

				def 					_UpperCAmelCase	(				self			:				str):







						"""simple docstring"""







						super().setUp()

						lowercase_								= ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
						lowercase_								= dict(zip(lowerCAmelCase_ ,  range(len(lowerCAmelCase_))))
						lowercase_								= {"""unk_token""": """<unk>"""}

						lowercase_								= os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""monolingual_vocab_file"""])
						with open(self.monolingual_vocab_file ,  """w""" ,  encoding="""utf-8""") as fp:
								for token in vocab_tokens:
										fp.write(F'''{token} {vocab_tokens[token]}\n''')

						lowercase_								= BartphoTokenizer(lowerCAmelCase_ ,  self.monolingual_vocab_file ,  **self.special_tokens_map)
						tokenizer.save_pretrained(self.tmpdirname)

				def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  **lowerCAmelCase_			:				Dict):







						"""simple docstring"""







						kwargs.update(self.special_tokens_map)
						return BartphoTokenizer.from_pretrained(self.tmpdirname ,  **lowerCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[str]):







						"""simple docstring"""







						lowercase_								= """This is a là test"""
						lowercase_								= """This is a<unk><unk> test"""
						return input_text, output_text




				def 					_UpperCAmelCase	(				self			:				Tuple):







						"""simple docstring"""







						lowercase_								= BartphoTokenizer(lowerCAmelCase_ ,  self.monolingual_vocab_file ,  **self.special_tokens_map)
						lowercase_								= """This is a là test"""
						lowercase_								= """▁This ▁is ▁a ▁l à ▁t est""".split()
						lowercase_								= tokenizer.tokenize(lowerCAmelCase_)
						self.assertListEqual(lowerCAmelCase_ ,  lowerCAmelCase_)

						lowercase_								= tokens + [tokenizer.unk_token]
						lowercase_								= [4, 5, 6, 3, 3, 7, 8, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) ,  lowerCAmelCase_)






"""simple docstring"""
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						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


  return (data["data"], data["target"])


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


  lowercase_								= XGBRegressor(verbosity=0 ,  random_state=42							)
  xgb.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Predict target for test data
  lowercase_								= xgb.predict(__lowerCAmelCase							)
  lowercase_								= predictions.reshape(len(__lowerCAmelCase							) ,  1							)
  return predictions


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= xgboost(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)
  print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

