






import timeit




class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a )	->   List[str]:
								if isinstance(a	,      a ):  # Initializes a matrix identical to the one provided.
															snake_case_           =							arg
															snake_case_           =							len(a )
								else:  # Initializes a square matrix of the given size and set values to zero.
															snake_case_           =							arg
															snake_case_           =							[[0 for _ in range(self.n )] for _ in range(self.n )]






	def __mul__(	self	,      a )	->   List[str]:
								snake_case_           =							Matrix(self.n )
								for i in range(self.n ):
															for j in range(self.n ):
																						for k in range(self.n ):
																													matrix.t[i][j] += self.t[i][k] * b.t[k][j]
								return matrix






def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							Matrix([[1, 0], [0, 1]])
							while b > 0:
														if b & 1:
																					matrix *= a
														a *= a
														b >>= 1
							return matrix





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							# Trivial Cases
							if n == 1:
														return fa
							elif n == 2:
														return fa
							snake_case_           =							Matrix([[1, 1], [1, 0]])
							snake_case_           =							modular_exponentiation(a_							,					n - 2)
							return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							# Trivial Cases
							if n == 1:
														return fa
							elif n == 2:
														return fa

							snake_case_           =							fa
							snake_case_           =							fa
							n -= 2

							while n > 0:
														snake_case_      ,		snake_case_           =							fn_a + fn_a, fn_a
														n -= 1

							return fn_a





def 						__UpperCAmelCase      (					):
							snake_case_           =							'\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
							snake_case_           =							'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
							snake_case_           =							timeit.timeit(setup=a_							,					stmt=a_							,					number=1_00)
							print('With matrix exponentiation the average execution time is '							,					exec_time / 1_00)
							return exec_time





def 						__UpperCAmelCase      (					):
							snake_case_           =							'\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
							snake_case_           =							'simple_fibonacci(randint(1,70000), 1, 1)'
							snake_case_           =							timeit.timeit(setup=a_							,					stmt=a_							,					number=1_00)
							print(
							    'Without matrix exponentiation the average execution time is '							,					exec_time / 1_00)
							return exec_time





def 						__UpperCAmelCase      (					):
							matrix_exponentiation_time()
							simple_fibonacci_time()


if __name__ == "__main__":
	main()













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      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





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





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

							snake_case_           =							'data_' + str(a_)
							snake_case_           =							os.path.join(a_							,					a_)

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

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

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

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





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

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

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk





