





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


_SCREAMING_SNAKE_CASE							=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
_SCREAMING_SNAKE_CASE							=						"cuda" if torch.cuda.is_available() else "cpu"


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a=1_00      ,       __a=" "      ):
	snake_case_						:	Union[str, Any]      =		text.split(A__      )
	return [character.join(text[i : i + n]      ).strip() for i in range(0      ,       len(A__      )      ,       A__      )]


def 							SCREAMING_SNAKE_CASE__  (			__a      ):
	snake_case_  ,snake_case_						:	Optional[Any]      =		[], []
	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 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):
	snake_case_						:	Optional[int]      =		ctx_tokenizer(
	    documents['title']      ,       documents['text']      ,       truncation=A__      ,       padding='longest'      ,       return_tensors='pt'      )['input_ids']
	snake_case_						:	Any      =		ctx_encoder(input_ids.to(device=A__      )      ,       return_dict=A__      ).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       ):
	logger.info('Step 1 - Create the dataset'      )
	######################################

	# The dataset needed for RAG must have three columns:
	# - title (string): title of the document
	# - text (string): text of a passage of the document
	# - embeddings (array of dimension d): DPR representation of the passage
	# Let's say you have documents in tab-separated csv files with columns "title" and "text"
	assert os.path.isfile(rag_example_args.csv_path      ), "Please provide a valid path to a csv file"

	# You can load a Dataset object this way
	snake_case_						:	Union[str, Any]      =		load_dataset(
	    'csv'      ,       data_files=[rag_example_args.csv_path]      ,       split='train'      ,       delimiter='\t'      ,       column_names=['title', 'text']      )

	# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

	# Then split the documents into passages of 100 words
	snake_case_						:	int      =		dataset.map(A__      ,       batched=A__      ,       num_proc=processing_args.num_proc      )

	# And compute the embeddings
	snake_case_						:	List[str]      =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=A__      )
	snake_case_						:	str      =		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      )
	snake_case_						:	Tuple      =		Features(
	    {'text': Value('string'      ), 'title': Value('string'      ), 'embeddings': Sequence(Value('float32'      )      )}      )  # optional, save as float32 instead of float64 to save space
	snake_case_						:	Optional[int]      =		dataset.map(
	    partial(A__      ,       ctx_encoder=A__      ,       ctx_tokenizer=A__      )      ,       batched=A__      ,       batch_size=processing_args.batch_size      ,       features=A__      ,       )

	# And finally save your dataset
	snake_case_						:	str      =		os.path.join(rag_example_args.output_dir      ,       'my_knowledge_dataset'      )
	dataset.save_to_disk(A__      )
	# from datasets import load_from_disk
	# dataset = load_from_disk(passages_path)  # to reload the dataset

	######################################
	logger.info('Step 2 - Index the dataset'      )
	######################################

	# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
	snake_case_						:	Tuple      =		faiss.IndexHNSWFlat(index_hnsw_args.d      ,       index_hnsw_args.m      ,       faiss.METRIC_INNER_PRODUCT      )
	dataset.add_faiss_index('embeddings'      ,       custom_index=A__      )

	# And save the index
	snake_case_						:	List[Any]      =		os.path.join(rag_example_args.output_dir      ,       'my_knowledge_dataset_hnsw_index.faiss'      )
	dataset.get_index('embeddings'      ).save(A__      )
	# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:	int       					=				field(
						    default=str(Path(_a    ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"    )				, metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}				, )
						__magic_name__:	Tuple       					=				field(
						    default=_a				, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}				, )
						__magic_name__:	Any       					=				field(
						    default="facebook/rag-sequence-nq"				, metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}				, )
						__magic_name__:	List[Any]       					=				field(
						    default="facebook/dpr-ctx_encoder-multiset-base"				, metadata={
						        "help": (
						            "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
						            " 'facebook/dpr-ctx_encoder-multiset-base'"
						        )
						    }				, )
						__magic_name__:	Optional[int]       					=				field(
						    default=str(Path(_a    ).parent / "test_run" / "dummy-kb"    )				, metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}				, )
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:	Any       					=				field(
						    default=_a				, metadata={
						        "help": "The number of processes to use to split the documents into passages. Default is single process."
						    }				, )
						__magic_name__:	Any       					=				field(
						    default=16				, metadata={
						        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
						    }				, )
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:	List[Any]       					=				field(
						    default=768				, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				, )
						__magic_name__:	Tuple       					=				field(
						    default=128				, metadata={
						        "help": (
						            "The number of bi-directional links created for every new element during the HNSW index construction."
						        )
						    }				, )


if __name__ == "__main__":
						logging.basicConfig(level=logging.WARNING)
						logger.setLevel(logging.INFO)

						_SCREAMING_SNAKE_CASE							=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						_SCREAMING_SNAKE_CASE							=						parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												_SCREAMING_SNAKE_CASE							=						rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)






'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 10**9    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  1
   UpperCamelCase					=  2

   UpperCamelCase					=  0
   UpperCamelCase					=  0
   UpperCamelCase					=  0
   while perimeter <= max_perimeter:
      perimeters_sum += perimeter

      prev_value += 2 * value
      value += prev_value

      UpperCamelCase					=  2 * value + 2 if i % 2 == 0 else 2 * value - 2
      i += 1

   return perimeters_sum


if __name__ == "__main__":
   print(f'''{solution() = }''')

