






"""simple docstring"""




from dataclasses import dataclass

lowercase  							= """0.1"""
lowercase  							= """Lucia Harcekova"""




@dataclass
class 			UpperCamelCase_				:



	'''simple docstring'''




	lowerCAmelCase													=			42
	lowerCAmelCase													=			42
	lowerCAmelCase													=			42




	def __repr__(	self )	->   List[Any]:
								return F'''({self.offset}, {self.length}, {self.indicator})'''






class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a = 13	,      a = 6 )	->   str:
								snake_case_           =							window_size
								snake_case_           =							lookahead_buffer_size
								snake_case_           =							self.window_size - self.lookahead_buffer_size




	def 							_UpperCamelCase      (	self	,      a )	->   List[str]:
								snake_case_           =							[]
								snake_case_           =							""

								# while there are still characters in text to compress
								while text:
															# find the next encoding phrase
															# - triplet with offset, length, indicator (the next encoding character)
															snake_case_           =							self._find_encoding_token(a	,      a )

															# update the search buffer:
															# - add new characters from text into it
															# - check if size exceed the max search buffer size, if so, drop the
															#   oldest elements
															search_buffer += text[: token.length + 1]
															if len(a ) > self.search_buffer_size:
																						snake_case_           =							search_buffer[-self.search_buffer_size :]

															# update the text
															snake_case_           =							text[token.length + 1 :]

															# append the token to output
															output.append(a )

								return output




	def 							_UpperCamelCase      (	self	,      a )	->   List[Any]:
								snake_case_           =							""

								for token in tokens:
															for _ in range(token.length ):
																						output += output[-token.offset]
															output += token.indicator

								return output




	def 							_UpperCamelCase      (	self	,      a	,      a )	->   List[Any]:

								if not text:
															raise ValueError('We need some text to work with.' )

								# Initialise result parameters to default values
								snake_case_           =							0, 0

								if not search_buffer:
															return Token(a	,      a	,      text[length] )

								for i, character in enumerate(a ):
															snake_case_           =							len(a ) - i
															if character == text[0]:
																						snake_case_           =							self._match_length_from_index(a	,      a	,      0	,      a )
																						# if the found length is bigger than the current or if it's equal,
																						# which means it's offset is smaller: update offset and length
																						if found_length >= length:
																													snake_case_           =							found_offset, found_length

								return Token(a	,      a	,      text[length] )






	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a )	->   Optional[Any]:
								if not text or text[text_index] != window[window_index]:
															return 0
								return 1 + self._match_length_from_index(
								    a	,      window + text[text_index]	,      text_index + 1	,      window_index + 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	# Initialize compressor class
	lowercase  							= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

	# Example
	lowercase  							= """cabracadabrarrarrad"""
	lowercase  							= lzaa_compressor.compress(TEXT)
	print(lzaa_compressor.compress("ababcbababaa"))
	lowercase  							= lzaa_compressor.decompress(compressed_text)
	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."













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





