

'''simple docstring'''





import numpy as np







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				List[Any]     ,  A_    :				str=5     ,  A_    :				Any=5	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						depth
      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						min_leaf_size
      SCREAMING_SNAKE_CASE__					     =						None



  def      _UpperCamelCase    (	self    :				List[Any]     ,  A_    :				List[Any]     ,  A_    :				Tuple	)					-> Tuple:
      if labels.ndim != 1:
          print("Error: Input labels must be one dimensional"	)

      return np.mean((labels - prediction) ** 2	)



  def      _UpperCamelCase    (	self    :				Union[str, Any]     ,  A_    :				Optional[Any]     ,  A_    :				List[Any]	)					-> Dict:
      if x.ndim != 1:
          print("Error: Input data set must be one dimensional"	)
          return
      if len(A_	) != len(A_	):
          print("Error: X and y have different lengths"	)
          return
      if y.ndim != 1:
          print("Error: Data set labels must be one dimensional"	)
          return

      if len(A_	) < 2 * self.min_leaf_size:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)
          return

      if self.depth == 1:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)
          return

      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(A_     ,  np.mean(A_	)	) * 2
      for i in range(len(A_	)	):
          if len(x[:i]	) < self.min_leaf_size:
              continue
          elif len(x[i:]	) < self.min_leaf_size:
              continue
          else:
              SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(x[:i]     ,  np.mean(y[:i]	)	)
              SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(x[i:]     ,  np.mean(y[i:]	)	)
              SCREAMING_SNAKE_CASE__					     =						error_left + error_right
              if error < min_error:
                  SCREAMING_SNAKE_CASE__					     =						i
                  SCREAMING_SNAKE_CASE__					     =						error

      if best_split != 0:
          SCREAMING_SNAKE_CASE__					     =						x[:best_split]
          SCREAMING_SNAKE_CASE__					     =						y[:best_split]
          SCREAMING_SNAKE_CASE__					     =						x[best_split:]
          SCREAMING_SNAKE_CASE__					     =						y[best_split:]

          SCREAMING_SNAKE_CASE__					     =						x[best_split]
          SCREAMING_SNAKE_CASE__					     =						DecisionTree(
              depth=self.depth - 1     ,  min_leaf_size=self.min_leaf_size	)
          SCREAMING_SNAKE_CASE__					     =						DecisionTree(
              depth=self.depth - 1     ,  min_leaf_size=self.min_leaf_size	)
          self.left.train(A_     ,  A_	)
          self.right.train(A_     ,  A_	)
      else:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)

      return







  def      _UpperCamelCase    (	self    :				str     ,  A_    :				Optional[Any]	)					-> int:
      if self.prediction is not None:
          return self.prediction
      elif self.left or self.right is not None:
          if x >= self.decision_boundary:
              return self.right.predict(A_	)
          else:
              return self.left.predict(A_	)
      else:
          print("Error: Decision tree not yet trained"	)
          return None







class    UpperCamelCase_			:


  """simple docstring"""



  @staticmethod
  def      _UpperCamelCase    (	A_    :				Optional[Any]     ,  A_    :				Optional[int]	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						float(0	)
      for label in labels:
          squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size	)





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						np.arange(-1.0 ,       1.0 ,       0.005	)
    SCREAMING_SNAKE_CASE__					     =						np.sin(__A	)

    SCREAMING_SNAKE_CASE__					     =						DecisionTree(depth=10 ,       min_leaf_size=10	)
    tree.train(__A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						(np.random.rand(10	) * 2) - 1
    SCREAMING_SNAKE_CASE__					     =						np.array([tree.predict(__A	) for x in test_cases]	)
    SCREAMING_SNAKE_CASE__					     =						np.mean((predictions - test_cases) ** 2	)

    print("Test values: " + str(__A	)	)
    print("Predictions: " + str(__A	)	)
    print("Average error: " + str(__A	)	)


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod(name="mean_squarred_error", verbose=True)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.txt"}

A__        =			{
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

A__        =			{
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

A__        =			{
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: List[Any]										=      PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__				: str										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: Optional[Any]										=      SplinterTokenizer



  def __init__(	self    :				Tuple     ,  A_    :				Optional[Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				Optional[Any]=True     ,  A_    :				str="[UNK]"     ,  A_    :				Optional[Any]="[SEP]"     ,  A_    :				str="[PAD]"     ,  A_    :				List[Any]="[CLS]"     ,  A_    :				Union[str, Any]="[MASK]"     ,  A_    :				Any="[QUESTION]"     ,  A_    :				List[Any]=True     ,  A_    :				Union[str, Any]=None     ,  **A_    :				Optional[Any]     ,  )					-> Optional[Any]:
      super().__init__(
          A_     ,  tokenizer_file=A_     ,  do_lower_case=A_     ,  unk_token=A_     ,  sep_token=A_     ,  pad_token=A_     ,  cls_token=A_     ,  mask_token=A_     ,  tokenize_chinese_chars=A_     ,  strip_accents=A_     ,  additional_special_tokens=(question_token,)     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
      if (
          pre_tok_state.get("lowercase"     ,  A_	) != do_lower_case
          or pre_tok_state.get("strip_accents"     ,  A_	) != strip_accents
      ):
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						do_lower_case
          SCREAMING_SNAKE_CASE__					     =						strip_accents
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						do_lower_case



  @property
  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Optional[Any]:
      return self.convert_tokens_to_ids(self.question_token	)



  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if self.padding_side == "right":
          # Input is question-then-context
          return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
      else:
          # Input is context-then-question
          return cls + token_ids_a + sep + token_ids_a + question_suffix + sep



  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]

      if self.padding_side == "right":
          # Input is question-then-context
          return len(cls + token_ids_a + question_suffix + sep	) * [0] + len(token_ids_a + sep	) * [1]
      else:
          # Input is context-then-question
          return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + question_suffix + sep	) * [1]







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)


