

"""simple docstring"""







from typing import Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)






class     __a			(					lowerCAmelCase_			):
       _lowerCamelCase							:							Optional[int]       =			['input_ids', 'attention_mask']







       def __init__(					self							,lowerCamelCase_="[PAD]"							,lowerCamelCase_="[BOS]"							,lowerCamelCase_="[EOS]"							,lowerCamelCase_="[MASK]"							,lowerCamelCase_="[CLS]"							,lowerCamelCase_="[SEP]"							,lowerCamelCase_=2048							,**lowerCamelCase_							,):



          '''simple docstring'''

          _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else pad_token
          _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else bos_token
          _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else eos_token
          _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else mask_token
          _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else cls_token
          _UpperCAmelCase   	=					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_							,)

          _UpperCAmelCase   	=					2**8  # utf is 8 bits

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







       def    _snake_case			(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.special_tokens_encoder.copy()
          vocab.update(self.added_tokens_encoder		)
          for i in range(self._utf_vocab_size		):
             _UpperCAmelCase   	=					chr(lowerCamelCase_		)
             _UpperCAmelCase   	=					i + len(self.special_tokens_encoder		)
          return vocab







       @property
       def    _snake_case			(					self		):



          '''simple docstring'''

          return self._utf_vocab_size + self._num_special_tokens







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None							,lowerCamelCase_ = False		):



          '''simple docstring'''

          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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):



          '''simple docstring'''

          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    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					[chr(lowerCamelCase_		) for i in text.encode("utf-8"		)]
          return tokens







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          if token in self.special_tokens_encoder:
             _UpperCAmelCase   	=					self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
             _UpperCAmelCase   	=					self.added_tokens_encoder[token]
          elif len(lowerCamelCase_		) != 1:
             _UpperCAmelCase   	=					self.unk_token_id
          else:
             _UpperCAmelCase   	=					ord(lowerCamelCase_		) + self._num_special_tokens
          return token_id







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          if index in self.special_tokens_decoder:
             _UpperCAmelCase   	=					self.special_tokens_decoder[index]
          elif index in self.added_tokens_decoder:
             _UpperCAmelCase   	=					self.added_tokens_decoder[index]
          else:
             _UpperCAmelCase   	=					chr(index - self._num_special_tokens		)
          return token







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					b""
          for token in tokens:
             if token in self.special_tokens_decoder:
                _UpperCAmelCase   	=					self.special_tokens_decoder[token].encode("utf-8"		)
             elif token in self.added_tokens_decoder:
                _UpperCAmelCase   	=					self.special_tokens_decoder[token].encode("utf-8"		)
             elif token in self.special_tokens_encoder:
                _UpperCAmelCase   	=					token.encode("utf-8"		)
             elif token in self.added_tokens_encoder:
                _UpperCAmelCase   	=					token.encode("utf-8"		)
             else:
                _UpperCAmelCase   	=					bytes([ord(lowerCamelCase_		)]		)
             bstring += tok_string
          _UpperCAmelCase   	=					bstring.decode("utf-8"							,errors="replace"		)
          return string







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):



          '''simple docstring'''

          return ()



"""simple docstring"""







import numpy as np






class     __a			:







       def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):



          '''simple docstring'''

          _UpperCAmelCase   	=					depth
          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					None
          _UpperCAmelCase   	=					min_leaf_size
          _UpperCAmelCase   	=					None







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          if labels.ndim != 1:
             print("Error: Input labels must be one dimensional"		)

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







       def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          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:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
             return

          if self.depth == 1:
             _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
             return

          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					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:
                _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                _UpperCAmelCase   	=					error_left + error_right
                if error < min_error:
                   _UpperCAmelCase   	=					i
                   _UpperCAmelCase   	=					error

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

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

          return







       def    _snake_case			(					self							,lowerCamelCase_		):



          '''simple docstring'''

          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    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					float(0		)
          for label in labels:
             squared_error_sum += (label - prediction) ** 2

          return float(squared_error_sum / labels.size		)



def 			__UpperCAmelCase		(		)     ->       Dict:
   _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
   _UpperCAmelCase   	=					np.sin(a_				)

   _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
   tree.train(a_    ,  a_				)

   _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
   _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
   _UpperCAmelCase   	=					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)

