






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_		:		Any		     =			logging.get_logger(__name__)

A_		:		Any		     =			{'vocab_file': 'vocab.txt'}

A_		:		Tuple		     =			{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

A_		:		Union[str, Any]		     =			{
    'openbmb/cpm-ant-10b': 1024,
}

def   snake_case (UpperCAmelCase__  )	->    str:






  """simple docstring"""




  UpperCamelCase_: str   				=    collections.OrderedDict()
  with open(__lowerCAmelCase     ,   'r'     ,   encoding='utf-8'  ) as reader:
    UpperCamelCase_: List[Any]   				=    reader.readlines()
  for index, token in enumerate(__lowerCAmelCase  ):
    UpperCamelCase_: str   				=    token.rstrip('\n'  )
    UpperCamelCase_: int   				=    index
  return vocab




class     _lowerCAmelCase(	__lowerCamelCase ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase="<unk>"  ,      _lowerCamelCase=2_0_0							):
         UpperCamelCase_: Union[str, Any]   				=    vocab
         UpperCamelCase_: str   				=    unk_token
         UpperCamelCase_: Dict   				=    max_input_chars_per_word





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[Any]   				=    list(__lowercase							)
         if len(__lowercase							) > self.max_input_chars_per_word:
           return [self.unk_token]

         UpperCamelCase_: List[Any]   				=    0
         UpperCamelCase_: List[str]   				=    []
         while start < len(__lowercase							):
           UpperCamelCase_: Any   				=    len(__lowercase							)
           UpperCamelCase_: Any   				=    None
           while start < end:
             UpperCamelCase_: Tuple   				=    ''''''.join(chars[start:end]							)
             if substr in self.vocab:
               UpperCamelCase_: Union[str, Any]   				=    substr
               break
             end -= 1
           if cur_substr is None:
             sub_tokens.append(self.unk_token							)
             start += 1
           else:
             sub_tokens.append(__lowercase							)
             UpperCamelCase_: Union[str, Any]   				=    end

         return sub_tokens







class     _lowerCAmelCase(	__lowerCamelCase ):





       """simple docstring"""







       a    :		List[str]									=VOCAB_FILES_NAMES
       a    :		Any									=PRETRAINED_VOCAB_FILES_MAP
       a    :		str									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		Tuple									=["""input_ids""", """attention_mask"""]
       a    :		Optional[Any]									=False





       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase="<d>"  ,      _lowerCamelCase="</d>"  ,      _lowerCamelCase="<s>"  ,      _lowerCamelCase="</s>"  ,      _lowerCamelCase="<pad>"  ,      _lowerCamelCase="<unk>"  ,      _lowerCamelCase="</n>"  ,      _lowerCamelCase="</_>"  ,      _lowerCamelCase="left"  ,      **_lowerCamelCase  ,      ):
         requires_backends(self  ,      ['jieba']							)
         super().__init__(
             bod_token=__lowercase  ,      eod_token=__lowercase  ,      bos_token=__lowercase  ,      eos_token=__lowercase  ,      pad_token=__lowercase  ,      unk_token=__lowercase  ,      line_token=__lowercase  ,      space_token=__lowercase  ,      padding_side=__lowercase  ,      **__lowercase  ,      )
         UpperCamelCase_: List[str]   				=    bod_token
         UpperCamelCase_: List[Any]   				=    eod_token
         UpperCamelCase_: List[Any]   				=    load_vocab(__lowercase							)
         UpperCamelCase_: Any   				=    self.encoder[space_token]
         UpperCamelCase_: Dict   				=    self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         UpperCamelCase_: Union[str, Any]   				=    collections.OrderedDict(sorted(self.encoder.items()  ,      key=lambda _lowerCamelCase							: x[1]							)							)
         UpperCamelCase_: Any   				=    {v: k for k, v in self.encoder.items()}

         UpperCamelCase_: Any   				=    WordpieceTokenizer(vocab=self.encoder  ,      unk_token=self.unk_token							)





       @property
       def       _a   ( self							):
         return self.encoder[self.bod_token]





       @property
       def       _a   ( self							):
         return self.encoder[self.eod_token]





       @property
       def       _a   ( self							):
         return self.encoder["\n"]





       @property
       def       _a   ( self							):
         return len(self.encoder							)





       def       _a   ( self							):
         return dict(self.encoder  ,      **self.added_tokens_encoder							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    []
         for x in jieba.cut(__lowercase  ,      cut_all=__lowercase							):
           output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowercase							)							)
         return output_tokens





       def       _a   ( self  ,      _lowerCamelCase  ,      **_lowerCamelCase							):
         UpperCamelCase_: Dict   				=    [i for i in token_ids if i >= 0]
         UpperCamelCase_: Optional[int]   				=    [
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(__lowercase  ,      **__lowercase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return token in self.encoder





       def       _a   ( self  ,      _lowerCamelCase							):
         return "".join(__lowercase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.encoder.get(__lowercase  ,      self.encoder.get(self.unk_token							)							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.decoder.get(__lowercase  ,      self.unk_token							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         if os.path.isdir(__lowercase							):
           UpperCamelCase_: int   				=    os.path.join(
               __lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
         else:
           UpperCamelCase_: str   				=    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         UpperCamelCase_: List[str]   				=    0
         if " " in self.encoder:
           UpperCamelCase_: Dict   				=    self.encoder[''' ''']
           del self.encoder[" "]
         if "\n" in self.encoder:
           UpperCamelCase_: Union[str, Any]   				=    self.encoder['''\n''']
           del self.encoder["\n"]
         UpperCamelCase_: Dict   				=    collections.OrderedDict(sorted(self.encoder.items()  ,      key=lambda _lowerCamelCase							: x[1]							)							)
         with open(__lowercase  ,      'w'  ,      encoding='utf-8'							) as writer:
           for token, token_index in self.encoder.items():
             if index != token_index:
               logger.warning(
                   f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                   ' Please check that the vocabulary is not corrupted!'							)
               UpperCamelCase_: str   				=    token_index
             writer.write(token + '\n'							)
             index += 1
         return (vocab_file,)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         if token_ids_a is None:
           return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False							):

         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowercase  ,      token_ids_a=__lowercase  ,      already_has_special_tokens=__lowercase							)

         if token_ids_a is not None:
           return [1] + ([0] * len(__lowercase							)) + [1] + ([0] * len(__lowercase							))
         return [1] + ([0] * len(__lowercase							))







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   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()