


"""simple docstring"""


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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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






def    a__       ( __lowercase   ,	__lowercase=100   ,	__lowercase=" "      )		->		List[str]:
				_A         =	text.split(__lowercase      )
				return [character.join(text[i : i + n]      ).strip() for i in range(0   ,	len(__lowercase      )   ,	__lowercase      )]






def    a__       ( __lowercase      )		->		dict:
				_A     ,						_A         =	[], []
				for title, text in zip(documents["title"]   ,	documents["text"]      ):
								if text is not None:
												for passage in split_text(__lowercase      ):
																titles.append(title if title is not None else ""      )
																texts.append(__lowercase      )
				return {"title": titles, "text": texts}






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






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	)		->		Union[str, Any]:
				######################################
				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
				_A         =	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
				_A         =	dataset.map(__lowercase   ,	batched=__lowercase   ,	num_proc=processing_args.num_proc      )

				# And compute the embeddings
				_A         =	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=__lowercase      )
				_A         =	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      )
				_A         =	Features(
				    {"text": Value("string"      ), "title": Value("string"      ), "embeddings": Sequence(Value("float32"      )      )}      )  # optional, save as float32 instead of float64 to save space
				_A         =	dataset.map(
				    partial(__lowercase   ,	ctx_encoder=__lowercase   ,	ctx_tokenizer=__lowercase      )   ,	batched=__lowercase   ,	batch_size=processing_args.batch_size   ,	features=__lowercase   ,	)

				# And finally save your dataset
				_A         =	os.path.join(rag_example_args.output_dir   ,	"my_knowledge_dataset"      )
				dataset.save_to_disk(__lowercase      )
				# 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
				_A         =	faiss.IndexHNSWFlat(index_hnsw_args.d   ,	index_hnsw_args.m   ,	faiss.METRIC_INNER_PRODUCT      )
				dataset.add_faiss_index("embeddings"   ,	custom_index=__lowercase      )

				# And save the index
				_A         =	os.path.join(rag_example_args.output_dir   ,	"my_knowledge_dataset_hnsw_index.faiss"      )
				dataset.get_index("embeddings"      ).save(__lowercase      )
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

				######################################
				logger.info("Step 3 - Load RAG"      )
				######################################

				# Easy way to load the model
				_A         =	RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name   ,	index_name="custom"   ,	indexed_dataset=__lowercase      )
				_A         =	RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name   ,	retriever=__lowercase      )
				_A         =	RagTokenizer.from_pretrained(rag_example_args.rag_model_name      )

				# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
				# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

				######################################
				logger.info("Step 4 - Have fun"      )
				######################################

				_A         =	rag_example_args.question or "What does Moses' rod turn into ?"
				_A         =	tokenizer.question_encoder(__lowercase   ,	return_tensors="pt"      )["input_ids"]
				_A         =	model.generate(__lowercase      )
				_A         =	tokenizer.batch_decode(__lowercase   ,	skip_special_tokens=__lowercase      )[0]
				logger.info("Q: " + question      )
				logger.info("A: " + generated_string      )
@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    default=str(Path(_UpperCamelCase).parent / 'test_data' / 'my_knowledge_dataset.csv')   , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}   , )
		__UpperCamelCase							=	field(
		    default='facebook/rag-sequence-nq'   , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}   , )
		__UpperCamelCase							=	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\''
		        )
		    }   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}   , )





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={
		        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
		    }   , )
		__UpperCamelCase							=	field(
		    default=16   , metadata={
		        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
		    }   , )





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    default=768   , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   , )
		__UpperCamelCase							=	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)

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



"""simple docstring"""


from __future__ import annotations

a_											=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class snake_case					:







		def __init__(    self					:				Optional[int]			,      a__					:				int						)				->  None:





						'''simple docstring'''







						_A         =	-1
						_A         =	-1
						_A         =	[-1] * length
						_A         =	None
						_A         =	None







		def __repr__(    self					:				Any						)				->  str:





						'''simple docstring'''







						return F"""Node(min_value={self.minn} max_value={self.maxx})"""






def    a__       ( __lowercase      )		->		Node | None:
				_A         =	Node(len(__lowercase      )      )
				_A     ,						_A         =	min(__lowercase      ), max(__lowercase      )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_A         =	(root.minn + root.maxx) // 2

				_A         =	[]
				_A         =	[]

				for index, num in enumerate(__lowercase      ):
								if num <= pivot:
												left_arr.append(__lowercase      )
								else:
												right_arr.append(__lowercase      )
								_A         =	len(__lowercase      )
				_A         =	build_tree(__lowercase      )
				_A         =	build_tree(__lowercase      )
				return root






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_A         =	(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left   ,	__lowercase   ,	node.map_left[index] - 1      )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right   ,	__lowercase   ,	index - node.map_left[index]      )






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if start > end:
								return 0
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	__lowercase      )
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	start - 1      )
				return rank_till_end - rank_before_start






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_A         =	node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left   ,	__lowercase   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	)
				else:
								return quantile(
								    node.right   ,	index - num_elements_in_left_tree   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	)






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_A         =	range_counting(
				    node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowercase   ,	__lowercase   ,	)
				_A         =	range_counting(
				    node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowercase   ,	__lowercase   ,	)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()