


import numpy as np





class    A					:


							def __init__(self   ,       lowerCAmelCase=5   ,       lowerCAmelCase=5      ):
									__lowercase=    depth
									__lowercase=    0
									__lowercase=    None
									__lowercase=    None
									__lowercase=    min_leaf_size
									__lowercase=    None


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									if labels.ndim != 1:
											print('Error: Input labels must be one dimensional'      )

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


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									if x.ndim != 1:
											print('Error: Input data set must be one dimensional'      )
											return
									if len(lowerCAmelCase      ) != len(lowerCAmelCase      ):
											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(lowerCAmelCase      ) < 2 * self.min_leaf_size:
											__lowercase=    np.mean(lowerCAmelCase      )
											return

									if self.depth == 1:
											__lowercase=    np.mean(lowerCAmelCase      )
											return

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

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

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

									return







							def     _A (self   ,       lowerCAmelCase      ):
									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(lowerCAmelCase      )
											else:
													return self.left.predict(lowerCAmelCase      )
									else:
											print('Error: Decision tree not yet trained'      )
											return None





class    A					:


							@staticmethod
							def     _A (lowerCAmelCase   ,       lowerCAmelCase      ):
									__lowercase=    float(0      )
									for label in labels:
											squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size      )







def  _lowerCamelCase(			)		->			Dict:

		'''simple docstring'''







		__lowercase=    np.arange(-1.0     ,				1.0     ,				0.005			)
		__lowercase=    np.sin(lowercase__			)

		__lowercase=    DecisionTree(depth=1_0     ,				min_leaf_size=1_0			)
		tree.train(lowercase__     ,				lowercase__			)

		__lowercase=    (np.random.rand(1_0			) * 2) - 1
		__lowercase=    np.array([tree.predict(lowercase__			) for x in test_cases]			)
		__lowercase=    np.mean((predictions - test_cases) ** 2			)

		print('Test values: ' + str(lowercase__			)			)
		print('Predictions: ' + str(lowercase__			)			)
		print('Average error: ' + str(lowercase__			)			)


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

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










from typing import Dict, List, Optional, Tuple

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


lowerCAmelCase					 =	logging.get_logger(__name__)





class    A					(     A_				):
							UpperCamelCase_     :							Any =['''input_ids''', '''attention_mask''']


							def __init__(self   ,       lowerCAmelCase="[PAD]"   ,       lowerCAmelCase="[BOS]"   ,       lowerCAmelCase="[EOS]"   ,       lowerCAmelCase="[MASK]"   ,       lowerCAmelCase="[CLS]"   ,       lowerCAmelCase="[SEP]"   ,       lowerCAmelCase=2_0_4_8   ,       **lowerCAmelCase   ,       ):
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else pad_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else bos_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else eos_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else mask_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else cls_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else sep_token

									super().__init__(
									    pad_token=lowerCAmelCase   ,       bos_token=lowerCAmelCase   ,       eos_token=lowerCAmelCase   ,       mask_token=lowerCAmelCase   ,       cls_token=lowerCAmelCase   ,       sep_token=lowerCAmelCase   ,       model_max_length=lowerCAmelCase   ,       **lowerCAmelCase   ,       )

									__lowercase=    2**8  # utf is 8 bits

									# define special tokens dict
									__lowercase=    {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									__lowercase=    len(self.special_tokens_encoder      )
									__lowercase=    {v: k for k, v in self.special_tokens_encoder.items()}


							def     _A (self      ):
									__lowercase=    self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder      )
									for i in range(self._utf_vocab_size      ):
											__lowercase=    chr(lowerCAmelCase      )
											__lowercase=    i + len(self.special_tokens_encoder      )
									return vocab


							@property
							def     _A (self      ):
									return self._utf_vocab_size + self._num_special_tokens


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = False      ):
									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase   ,       token_ids_a=lowerCAmelCase   ,       already_has_special_tokens=lowerCAmelCase      )

									# normal case: some special tokens
									if token_ids_a is None:
											return [1] + [0] * len(lowerCAmelCase      ) + [1]
									return [1] + ([0] * len(lowerCAmelCase      )) + [1] + ([0] * len(lowerCAmelCase      )) + [1]


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    [chr(lowerCAmelCase      ) for i in text.encode('utf-8'      )]
									return tokens


							def     _A (self   ,       lowerCAmelCase      ):
									if token in self.special_tokens_encoder:
											__lowercase=    self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
											__lowercase=    self.added_tokens_encoder[token]
									elif len(lowerCAmelCase      ) != 1:
											__lowercase=    self.unk_token_id
									else:
											__lowercase=    ord(lowerCAmelCase      ) + self._num_special_tokens
									return token_id


							def     _A (self   ,       lowerCAmelCase      ):
									if index in self.special_tokens_decoder:
											__lowercase=    self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
											__lowercase=    self.added_tokens_decoder[index]
									else:
											__lowercase=    chr(index - self._num_special_tokens      )
									return token


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    B''
									for token in tokens:
											if token in self.special_tokens_decoder:
													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
											elif token in self.added_tokens_decoder:
													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
											elif token in self.special_tokens_encoder:
													__lowercase=    token.encode('utf-8'      )
											elif token in self.added_tokens_encoder:
													__lowercase=    token.encode('utf-8'      )
											else:
													__lowercase=    bytes([ord(lowerCAmelCase      )]      )
											bstring += tok_string
									__lowercase=    bstring.decode('utf-8'   ,       errors='replace'      )
									return string







							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
									return ()






