




import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      PhobertTokenizer
    lowerCamelCase_      :	List[str]	     =      False







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

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowerCamelCase   :      Union[str, Any]			 =					["T@@", "i", "I", "R@@", "r", "e@@"]
           lowerCamelCase   :      Optional[Any]			 =					dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__     )     )     )     )
           lowerCamelCase   :      Union[str, Any]			 =					["#version: 0.2", "l à</w>"]
           lowerCamelCase   :      Dict			 =					{"unk_token": "<unk>"}

           lowerCamelCase   :      int			 =					os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["vocab_file"]     )
           lowerCamelCase   :      Any			 =					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(UpperCamelCase__     )     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Tuple:
           kwargs.update(self.special_tokens_map     )
           return PhobertTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					"Tôi là VinAI Research"
           lowerCamelCase   :      List[Any]			 =					"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
           return input_text, output_text







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					PhobertTokenizer(self.vocab_file     ,  self.merges_file     ,  **self.special_tokens_map     )
           lowerCamelCase   :      Any			 =					"Tôi là VinAI Research"
           lowerCamelCase   :      str			 =					"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
           lowerCamelCase   :      Tuple			 =					tokenizer.tokenize(UpperCamelCase__     )
           print(UpperCamelCase__     )
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					tokens + [tokenizer.unk_token]

           lowerCamelCase   :      Any			 =					[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(UpperCamelCase__     )     ,  UpperCamelCase__     )












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      A      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					xgboost(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Error printing
       print(f'''Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )
       print(f'''Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()






