





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					=					logging.get_logger(__name__)

UpperCAmelCase_					=					{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_					=					{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_					=					{
    """camembert-base""": 512,
}

UpperCAmelCase_					=					"""▁"""






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				int  						=						VOCAB_FILES_NAMES
   a		:				Any  						=						PRETRAINED_VOCAB_FILES_MAP
   a		:				int  						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:				Optional[Any]  						=						["input_ids", "attention_mask"]




   def __init__(       self,						__magic_name__,						__magic_name__="<s>",						__magic_name__="</s>",						__magic_name__="</s>",						__magic_name__="<s>",						__magic_name__="<unk>",						__magic_name__="<pad>",						__magic_name__="<mask>",						__magic_name__=["<s>NOTUSED", "</s>NOTUSED"],						__magic_name__ = None,						**__magic_name__,						)					-> str:






       """simple docstring"""
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__				:							str			  = AddedToken(lowerCamelCase_,						lstrip=lowerCamelCase_,						rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_,						lowerCamelCase_				) else mask_token

       UpperCamelCase__				:							List[Any]			  = {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=lowerCamelCase_,						eos_token=lowerCamelCase_,						unk_token=lowerCamelCase_,						sep_token=lowerCamelCase_,						cls_token=lowerCamelCase_,						pad_token=lowerCamelCase_,						mask_token=lowerCamelCase_,						additional_special_tokens=lowerCamelCase_,						sp_model_kwargs=self.sp_model_kwargs,						**lowerCamelCase_,						)
       UpperCamelCase__				:							Union[str, Any]			  = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(lowerCamelCase_				)				)
       UpperCamelCase__				:							Tuple			  = vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       UpperCamelCase__				:							Dict			  = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       UpperCamelCase__				:							Tuple			  = len(self.fairseq_tokens_to_ids				)
       UpperCamelCase__				:							List[str]			  = len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
       UpperCamelCase__				:							Tuple			  = {v: k for k, v in self.fairseq_tokens_to_ids.items()}




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = None				)					-> Dict:






       """simple docstring"""

       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__				:							List[str]			  = [self.cls_token_id]
       UpperCamelCase__				:							Optional[Any]			  = [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = None,						__magic_name__ = False				)					-> int:






       """simple docstring"""
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_,						token_ids_a=lowerCamelCase_,						already_has_special_tokens=lowerCamelCase_				)

       if token_ids_a is None:
           return [1] + ([0] * len(lowerCamelCase_				)) + [1]
       return [1] + ([0] * len(lowerCamelCase_				)) + [1, 1] + ([0] * len(lowerCamelCase_				)) + [1]




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = None				)					-> str:






       """simple docstring"""
       UpperCamelCase__				:							Any			  = [self.sep_token_id]
       UpperCamelCase__				:							Optional[Any]			  = [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]




   @property
   def  UpperCamelCase__    (       self				)					-> Union[str, Any]:






       """simple docstring"""
       return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)




   def  UpperCamelCase__    (       self				)					-> str:






       """simple docstring"""
       UpperCamelCase__				:							Dict			  = {self.convert_ids_to_tokens(lowerCamelCase_				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Optional[int]:






       """simple docstring"""
       return self.sp_model.encode(lowerCamelCase_,						out_type=lowerCamelCase_				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Union[str, Any]:






       """simple docstring"""
       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(lowerCamelCase_				) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase_				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Tuple:






       """simple docstring"""
       if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Tuple:






       """simple docstring"""
       UpperCamelCase__				:							Optional[Any]			  = []
       UpperCamelCase__				:							List[str]			  = ''
       UpperCamelCase__				:							str			  = False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(lowerCamelCase_				) + token
               UpperCamelCase__				:							int			  = True
               UpperCamelCase__				:							Dict			  = []
           else:
               current_sub_tokens.append(lowerCamelCase_				)
               UpperCamelCase__				:							str			  = False
       out_string += self.sp_model.decode(lowerCamelCase_				)
       return out_string.strip()




   def __getstate__(       self				)					-> Tuple:






       """simple docstring"""
       UpperCamelCase__				:							Union[str, Any]			  = self.__dict__.copy()
       UpperCamelCase__				:							Union[str, Any]			  = None
       return state




   def __setstate__(       self,						__magic_name__				)					-> Optional[int]:






       """simple docstring"""
       UpperCamelCase__				:							Dict			  = d

       # for backward compatibility
       if not hasattr(self,						'''sp_model_kwargs'''				):
           UpperCamelCase__				:							int			  = {}

       UpperCamelCase__				:							Dict			  = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = None				)					-> Optional[Any]:






       """simple docstring"""
       if not os.path.isdir(lowerCamelCase_				):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
           return
       UpperCamelCase__				:							List[Any]			  = os.path.join(
           lowerCamelCase_,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase_				) and os.path.isfile(self.vocab_file				):
           copyfile(self.vocab_file,						lowerCamelCase_				)
       elif not os.path.isfile(self.vocab_file				):
           with open(lowerCamelCase_,						'''wb'''				) as fi:
               UpperCamelCase__				:							Tuple			  = self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_				)

       return (out_vocab_file,)








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()


