






import numpy as np


class      __SCREAMING_SNAKE_CASE       :






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






												"""simple docstring"""
												lowerCAmelCase__         =    depth
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    min_leaf_size
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






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

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






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												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:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												if self.depth == 1:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

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

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

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

												return







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""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(_UpperCamelCase )
																		else:
																								return self.left.predict(_UpperCamelCase )
												else:
																		print('Error: Decision tree not yet trained' )
																		return None




class      __SCREAMING_SNAKE_CASE       :






						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    float(0 )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size )







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    np.arange(-1.0     ,							1.0     ,							0.005		)
						lowerCAmelCase__         =    np.sin(UpperCamelCase_		)

						lowerCAmelCase__         =    DecisionTree(depth=10     ,							min_leaf_size=10		)
						tree.train(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    (np.random.rand(10		) * 2) - 1
						lowerCAmelCase__         =    np.array([tree.predict(UpperCamelCase_		) for x in test_cases]		)
						lowerCAmelCase__         =    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 functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.split(UpperCamelCase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)]







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__    ,							lowerCAmelCase__         =    [], []
						for title, text in zip(documents['title']     ,							documents['text']		):
												if text is not None:
																		for passage in split_text(UpperCamelCase_		):
																								titles.append(title if title is not None else ''		)
																								texts.append(UpperCamelCase_		)
						return {"title": titles, "text": texts}







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

						def embed(
						    UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       DPRContextEncoder     ,							UpperCamelCase_							:       DPRContextEncoderTokenizerFast     ,							UpperCamelCase_							:       Tuple		) -> dict:
												lowerCAmelCase__         =    ctx_tokenizer(
												    documents['title']     ,							documents['text']     ,							truncation=UpperCamelCase_     ,							padding='longest'     ,							return_tensors='pt'		)['input_ids']
												lowerCAmelCase__         =    ctx_encoder(input_ids.to(device=UpperCamelCase_		)     ,							return_dict=UpperCamelCase_		).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						lowerCAmelCase__         =    Features(
						    {'text': Value('string'		), 'title': Value('string'		), 'embeddings': Sequence(Value('float32'		)		)}		)  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





