

import numpy as np

class      _a				:




      """simple docstring"""

      def __init__(      self       :							Optional[int]	, UpperCAmelCase       :							Optional[Any]=5	, UpperCAmelCase       :							Dict=5  ):
            A_	     =							depth
            A_	     =							0
            A_	     =							None
            A_	     =							None
            A_	     =							min_leaf_size
            A_	     =							None





      def 		__A  (      self       :							int	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any  ):
            if labels.ndim != 1:
                  print("Error: Input labels must be one dimensional"  )

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





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Any  ):
            if x.ndim != 1:
                  print("Error: Input data set must be one dimensional"  )
                  return
            if len(UpperCAmelCase  ) != len(UpperCAmelCase  ):
                  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(UpperCAmelCase  ) < 2 * self.min_leaf_size:
                  A_	     =							np.mean(UpperCAmelCase  )
                  return

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

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

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

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

            return







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Optional[Any]  ):
            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(UpperCAmelCase  )
                  else:
                        return self.left.predict(UpperCAmelCase  )
            else:
                  print("Error: Decision tree not yet trained"  )
                  return None




class      _a				:




      """simple docstring"""

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

            return float(squared_error_sum / labels.size  )




def 			__snake_case	(   )     ->   Optional[int]:



      """simple docstring"""

      A_	     =							np.arange(-1.0							,1.0							,0.005						)
      A_	     =							np.sin(__UpperCamelCase						)

      A_	     =							DecisionTree(depth=10							,min_leaf_size=10						)
      tree.train(__UpperCamelCase							,__UpperCamelCase						)

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

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


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

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


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_	     =							[self.cls_token_id]
            A_	     =							[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )

            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  )) + [1]
            return [1] + ([0] * len(UpperCAmelCase  )) + [1]





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            A_	     =							[self.sep_token_id]
            A_	     =							[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 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = 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(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

            return (out_vocab_file,)