
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_a   :      int       =				logging.get_logger(__name__)

_a   :      Any       =				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_a   :      Any       =				{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}






class   _UpperCAmelCase					(       _snake_case):
    def __init__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=False    ,     snake_case_=True    ,     snake_case_=False    ,     snake_case_="<s>"    ,     snake_case_="</s>"    ,     snake_case_="<unk>"    ,     snake_case_="<sep>"    ,     snake_case_="<pad>"    ,     snake_case_="<cls>"    ,     snake_case_="<mask>"    ,     snake_case_=["<eop>", "<eod>"]    ,     **snake_case_    ,     ):
      _snake_case  :    Tuple            = AddedToken(snake_case_    ,     lstrip=snake_case_    ,     rstrip=snake_case_  ) if isinstance(snake_case_    ,     snake_case_  ) else mask_token

      super().__init__(
          vocab_file=snake_case_    ,     tokenizer_file=snake_case_    ,     do_lower_case=snake_case_    ,     remove_space=snake_case_    ,     keep_accents=snake_case_    ,     bos_token=snake_case_    ,     eos_token=snake_case_    ,     unk_token=snake_case_    ,     sep_token=snake_case_    ,     pad_token=snake_case_    ,     cls_token=snake_case_    ,     mask_token=snake_case_    ,     additional_special_tokens=snake_case_    ,     **snake_case_    ,     )

      _snake_case  :    Optional[int]            = 3
      _snake_case  :    Dict            = do_lower_case
      _snake_case  :    Optional[int]            = remove_space
      _snake_case  :    Optional[Any]            = keep_accents
      _snake_case  :    Dict            = vocab_file
      _snake_case  :    int            = False if not self.vocab_file else True

      try:
        import jieba
      except ModuleNotFoundError as error:
        raise error.__class__(
            "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
            "See https://pypi.org/project/jieba/ for installation."  )
      _snake_case  :    List[Any]            = jieba
      _snake_case  :    Union[str, Any]            = str.maketrans(" \n"    ,     "\u2582\u2583"  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    Any            = [self.sep_token_id]
      _snake_case  :    str            = [self.cls_token_id]
      if token_ids_a is None:
        return token_ids_a + sep + cls
      return token_ids_a + sep + token_ids_a + sep + cls
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    Optional[int]            = [self.sep_token_id]
      _snake_case  :    Tuple            = [2]

      if token_ids_a is None:
        return len(token_ids_a + sep  ) * [0] + cls_segment_id
      return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer."  )

      if not os.path.isdir(snake_case_  ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
        return
      _snake_case  :    Any            = os.path.join(
          snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case_  ):
        copyfile(self.vocab_file    ,     snake_case_  )

      return (out_vocab_file,)
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = [
          " ".join([x.translate(self.translator  ) for x in self.jieba.cut(snake_case_    ,     cut_all=snake_case_  )]  )
          for text in batch_text_or_text_pairs
      ]
      return super()._batch_encode_plus(snake_case_    ,     *snake_case_    ,     **snake_case_  )


    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      _snake_case  :    Any            = super()._decode(*snake_case_    ,     **snake_case_  )
      _snake_case  :    Any            = text.replace(" "    ,     ""  ).replace("\u2582"    ,     " "  ).replace("\u2583"    ,     "\n"  )
      return text




import matplotlib.pyplot as plt
import numpy as np
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	len(__A      )  # Number of training samples
  UpperCAmelCase          =	np.eye(__A      )  # Initialize weights as identity matrix
  for j in range(__A      ):
    UpperCAmelCase          =	point - x_train[j]
    UpperCAmelCase          =	np.exp(diff @ diff.T / (-2.0 * tau**2)      )

  return weights
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	weight_matrix(__A ,							__A ,							__A      )
  UpperCAmelCase          =	np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	np.zeros(len(__A      )      )  # Initialize array of predictions
  for i, item in enumerate(__A      ):
    UpperCAmelCase          =	item @ local_weight(__A ,							__A ,							__A ,							__A      )

  return y_pred
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  import seaborn as sns

  UpperCAmelCase          =	sns.load_dataset(__A      )
  UpperCAmelCase          =	np.array(data[x_name]      )
  UpperCAmelCase          =	np.array(data[y_name]      )

  UpperCAmelCase          =	np.ones(len(__A      )      )

  # pairing elements of one and x_data
  UpperCAmelCase          =	np.column_stack((one, x_data)      )

  return x_train, x_data, y_data
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A ,							):
  UpperCAmelCase          =	np.sort(__A ,							axis=0      )
  plt.scatter(__A ,							__A ,							color="blue"      )
  plt.plot(
      x_train_sorted[:, 1] ,							preds[x_train[:, 1].argsort(0      )] ,							color="yellow" ,							linewidth=5 ,							)
  plt.title("Local Weighted Regression"      )
  plt.xlabel(__A      )
  plt.ylabel(__A      )
  plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							=			load_data("tips", "total_bill", "tip")
      lowerCAmelCase__							=			local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")



