



import numpy as np







class 				a_    :

						def __init__(       self      :str  ,		_lowercase      :str=5  ,		_lowercase      :str=5)						->   List[str]:
												UpperCAmelCase_					=	depth
												UpperCAmelCase_					=	0
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	min_leaf_size
												UpperCAmelCase_					=	None

						def       __a			(       self      :List[Any]  ,		_lowercase      :Dict  ,		_lowercase      :Any)						->   Union[str, Any]:
												if labels.ndim != 1:
																		print('''Error: Input labels must be one dimensional''')

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

						def       __a			(       self      :Any  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Tuple)						->   int:
												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:
																		UpperCAmelCase_					=	np.mean(_lowercase)
																		return

												if self.depth == 1:
																		UpperCAmelCase_					=	np.mean(_lowercase)
																		return

												UpperCAmelCase_					=	0
												UpperCAmelCase_					=	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:
																								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(_lowercase  ,		_lowercase)
																		self.right.train(_lowercase  ,		_lowercase)
												else:
																		UpperCAmelCase_					=	np.mean(_lowercase)

												return




						def       __a			(       self      :int  ,		_lowercase      :Any)						->   List[str]:
												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_    :

						@staticmethod
						def       __a			(       _lowercase      :Union[str, Any]  ,		_lowercase      :Dict)						->   List[Any]:
												UpperCAmelCase_					=	float(0)
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size)







def 							A     (						)			->			List[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	np.arange(-1.0				,   1.0				,   0.005      )
						UpperCAmelCase_					=	np.sin(__UpperCAmelCase      )

						UpperCAmelCase_					=	DecisionTree(depth=10				,   min_leaf_size=10      )
						tree.train(__UpperCAmelCase				,   __UpperCAmelCase      )

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





