

import numpy as np







class 							a__							:







  """simple docstring"""




  def __init__(      self     :			str					,					UpperCAmelCase__     :			str=5					,					UpperCAmelCase__     :			str=5		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = depth
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 0
        SCREAMING_SNAKE_CASE			:   Dict	  = None
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = None
        SCREAMING_SNAKE_CASE			:   Any	  = min_leaf_size
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = None


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Dict					,					UpperCAmelCase__     :			Any		)       ->Union[str, Any]:
        """simple docstring"""







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

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


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->int:
        """simple docstring"""







        if x.ndim != 1:
              print("""Error: Input data set must be one dimensional"""		)
              return
        if len(_lowercase		) != len(_lowercase		):
              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(_lowercase		) < 2 * self.min_leaf_size:
              SCREAMING_SNAKE_CASE			:   Any	  = np.mean(_lowercase		)
              return

        if self.depth == 1:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = np.mean(_lowercase		)
              return

        SCREAMING_SNAKE_CASE			:   Any	  = 0
        SCREAMING_SNAKE_CASE			:   Tuple	  = self.mean_squared_error(_lowercase					,					np.mean(_lowercase		)		) * 2
        for i in range(len(_lowercase		)		):
              if len(x[:i]		) < self.min_leaf_size:
                    continue
              elif len(x[i:]		) < self.min_leaf_size:
                    continue
              else:
                    SCREAMING_SNAKE_CASE			:   int	  = self.mean_squared_error(x[:i]					,					np.mean(y[:i]		)		)
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.mean_squared_error(x[i:]					,					np.mean(y[i:]		)		)
                    SCREAMING_SNAKE_CASE			:   Dict	  = error_left + error_right
                    if error < min_error:
                          SCREAMING_SNAKE_CASE			:   List[str]	  = i
                          SCREAMING_SNAKE_CASE			:   Optional[Any]	  = error

        if best_split != 0:
              SCREAMING_SNAKE_CASE			:   Dict	  = x[:best_split]
              SCREAMING_SNAKE_CASE			:   int	  = y[:best_split]
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = x[best_split:]
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = y[best_split:]

              SCREAMING_SNAKE_CASE			:   Dict	  = x[best_split]
              SCREAMING_SNAKE_CASE			:   List[Any]	  = DecisionTree(
                  depth=self.depth - 1					,					min_leaf_size=self.min_leaf_size		)
              SCREAMING_SNAKE_CASE			:   Any	  = DecisionTree(
                  depth=self.depth - 1					,					min_leaf_size=self.min_leaf_size		)
              self.left.train(_lowercase					,					_lowercase		)
              self.right.train(_lowercase					,					_lowercase		)
        else:
              SCREAMING_SNAKE_CASE			:   str	  = np.mean(_lowercase		)

        return




  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			Any		)       ->List[str]:
        """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(_lowercase		)
              else:
                    return self.left.predict(_lowercase		)
        else:
              print("""Error: Decision tree not yet trained"""		)
              return None







class 							a__							:







  """simple docstring"""




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Dict		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = float(0		)
        for label in labels:
              squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size		)






def        __lowercase   (  )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Any	  = np.arange(-1.0    ,     1.0    ,     0.005      )
      SCREAMING_SNAKE_CASE			:   Tuple	  = np.sin(__UpperCAmelCase      )

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = DecisionTree(depth=10    ,     min_leaf_size=10      )
      tree.train(__UpperCAmelCase    ,     __UpperCAmelCase      )

      SCREAMING_SNAKE_CASE			:   int	  = (np.random.rand(10      ) * 2) - 1
      SCREAMING_SNAKE_CASE			:   str	  = np.array([tree.predict(__UpperCAmelCase      ) for x in test_cases]      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = 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
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 				a_    (						unittest.TestCase							):
						UpperCamelCase__ :		Any										=EsmTokenizer

						def       __a			(       self      :Optional[int])						->   Optional[int]:
												super().setUp()
												UpperCAmelCase_					=	tempfile.mkdtemp()
												# fmt: off
												UpperCAmelCase_					=	["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
												# fmt: on
												UpperCAmelCase_					=	os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''vocab_file'''])
												with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8''') as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

						def       __a			(       self      :List[str]  ,		**_lowercase      :Union[str, Any])						->   List[PreTrainedTokenizerBase]:
												return [self.get_tokenizer(**_lowercase)]

						def       __a			(       self      :Optional[int]  ,		**_lowercase      :List[str])						->   PreTrainedTokenizer:
												return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**_lowercase)

						def       __a			(       self      :int)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	tokenizer.tokenize('''LAGVS''')
												self.assertListEqual(_lowercase  ,		['''L''', '''A''', '''G''', '''V''', '''S'''])
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)  ,		[4, 5, 6, 7, 8])

						def       __a			(       self      :List[Any])						->   Optional[int]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	'''LAGVS'''
												self.assertListEqual(tokenizer.encode(_lowercase)  ,		[0, 4, 5, 6, 7, 8, 2])

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]])

						def       __a			(       self      :str)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]])




						def       __a			(       self      :Dict)						->   Optional[int]:
												UpperCAmelCase_					=	self.get_tokenizers(fast=_lowercase)
												for tokenizer in tokenizers:
																		with self.subTest(f"{tokenizer.__class__.__name__}"):
																								UpperCAmelCase_					=	'''<unk>'''
																								UpperCAmelCase_					=	'''<mask>'''

																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)
																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)

																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(token_a[0]  ,		_lowercase)
																								self.assertEqual(token_a[0]  ,		_lowercase)





