
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCamelCase_						     =					logging.get_logger(__name__)

lowerCamelCase_						     =					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_						     =					{
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase_						     =					{
    '''squeezebert/squeezebert-uncased''': 5_12,
    '''squeezebert/squeezebert-mnli''': 5_12,
    '''squeezebert/squeezebert-mnli-headless''': 5_12,
}


lowerCamelCase_						     =					{
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__         =      SqueezeBertTokenizer







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_="[UNK]"	,      SCREAMING_SNAKE_CASE_="[SEP]"	,      SCREAMING_SNAKE_CASE_="[PAD]"	,      SCREAMING_SNAKE_CASE_="[CLS]"	,      SCREAMING_SNAKE_CASE_="[MASK]"	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_	,      ):
       super().__init__(
           SCREAMING_SNAKE_CASE_	,      tokenizer_file=SCREAMING_SNAKE_CASE_	,      do_lower_case=SCREAMING_SNAKE_CASE_	,      unk_token=SCREAMING_SNAKE_CASE_	,      sep_token=SCREAMING_SNAKE_CASE_	,      pad_token=SCREAMING_SNAKE_CASE_	,      cls_token=SCREAMING_SNAKE_CASE_	,      mask_token=SCREAMING_SNAKE_CASE_	,      tokenize_chinese_chars=SCREAMING_SNAKE_CASE_	,      strip_accents=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_	,      )

       UpperCamelCase__				=    json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           normalizer_state.get("""lowercase"""	,      SCREAMING_SNAKE_CASE_						) != do_lower_case
           or normalizer_state.get("""strip_accents"""	,      SCREAMING_SNAKE_CASE_						) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""	,      SCREAMING_SNAKE_CASE_						) != tokenize_chinese_chars
       ):
            UpperCamelCase__				=    getattr(SCREAMING_SNAKE_CASE_	,      normalizer_state.pop("""type"""						)						)
            UpperCamelCase__				=    do_lower_case
            UpperCamelCase__				=    strip_accents
            UpperCamelCase__				=    tokenize_chinese_chars
            UpperCamelCase__				=    normalizer_class(**SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    do_lower_case







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None						):
       UpperCamelCase__				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

       return output







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    [self.sep_token_id]
       UpperCamelCase__				=    [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						) * [0] + len(token_ids_a + sep						) * [1]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_	,      name=SCREAMING_SNAKE_CASE_						)
       return tuple(SCREAMING_SNAKE_CASE_						)







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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




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






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





