



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 (    _lowerCamelCase			,     _lowerCamelCase=100			,     _lowerCamelCase=" "     ):
			A				:       str														=    text.split(_lowerCamelCase     )
			return [character.join(text[i : i + n]     ).strip() for i in range(0			,     len(_lowerCamelCase     )			,     _lowerCamelCase     )]




def 						UpperCAmelCase (    _lowerCamelCase     ):
			A							,			A				:       Any														=    [], []
			for title, text in zip(documents["title"]			,     documents["text"]     ):
						if text is not None:
									for passage in split_text(_lowerCamelCase     ):
												titles.append(title if title is not None else ""     )
												texts.append(_lowerCamelCase     )
			return {"title": titles, "text": texts}




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       Optional[Any]														=    load_dataset(
			    "csv"			,     data_files=[csv_path]			,     split="train"			,     delimiter="\t"			,     column_names=["title", "text"]     )
			A				:       Optional[int]														=    kb_dataset.map(
			    _lowerCamelCase			,     batched=_lowerCamelCase			,     num_proc=1     )  # if you want you can load already splitted csv.
			A				:       Optional[Any]														=    [kb_dataset.shard(_lowerCamelCase			,     _lowerCamelCase			,     contiguous=_lowerCamelCase     ) for i in range(_lowerCamelCase     )]
			A				:       str														=    kb_list[process_num]

			A				:       int														=    "data_" + str(_lowerCamelCase     )
			A				:       Dict														=    os.path.join(_lowerCamelCase			,     _lowerCamelCase     )

			A				:       Dict														=    DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"     )
			A				:       str														=    ctx_encoder.to(device=_lowerCamelCase     )

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

			A				:       Union[str, Any]														=    Features(
			    {"text": Value("string"     ), "title": Value("string"     ), "embeddings": Sequence(Value("float32"     )     )}     )  # optional, save as float32 instead of float64 to save space

			A				:       Optional[Any]														=    data_shrad.map(
			    partial(_lowerCamelCase			,     ctx_encoder=_lowerCamelCase			,     ctx_tokenizer=_lowerCamelCase			,     device=_lowerCamelCase     )			,     batched=_lowerCamelCase			,     batch_size=16			,     features=_lowerCamelCase			,     )
			dataset.save_to_disk(_lowerCamelCase     )






def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       Any														=    []

			for shard_address in glob(str(_lowerCamelCase     ) + "/*/"     ):
						data_shard_list.append(load_from_disk(_lowerCamelCase     )     )

			A				:       Optional[Any]														=    concatenate_datasets(_lowerCamelCase     )
			faiss.omp_set_num_threads(96     )

			A				:       str														=    faiss.IndexHNSWFlat(768			,     128			,     faiss.METRIC_INNER_PRODUCT     )
			concat.add_faiss_index("embeddings"			,     custom_index=_lowerCamelCase     )
			concat.get_index("embeddings"     ).save(
			    _lowerCamelCase     )  # since we load the index in to memory,we can directly update the index in the disk




from collections.abc import Callable

__SCREAMING_SNAKE_CASE     =	Callable[[float], float]  # type alias for a real -> real function




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       int														=    float(_lowerCamelCase     )
			while True:
						try:
									A				:       Optional[int]														=    prev_guess - function(_lowerCamelCase     ) / derivative(_lowerCamelCase     )
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"     ) from None
						if abs(prev_guess - next_guess     ) < 10**-5:
									return next_guess
						A				:       Any														=    next_guess




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (x**3) - (2 * x) - 5




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return 3 * (x**2) - 2


if __name__ == "__main__":
							print(newton(f, fa, 3))