
'''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,
)


__SCREAMING_SNAKE_CASE		:  str        =      logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE		:  Dict        =      'cuda' if torch.cuda.is_available() else 'cpu'

def 				_snake_case (		lowercase			,    lowercase=1_0_0			,    lowercase=" "						)       ->       Optional[int]:
				__a       :     Any								=	text.split(__lowerCamelCase						)
				return [character.join(text[i : i + n]						).strip() for i in range(0			,    len(__lowerCamelCase						)			,    __lowerCamelCase						)]

def 				_snake_case (		lowercase						)       ->       Optional[int]:
				__a							,	__a       :     Optional[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 				_snake_case (		lowercase			,    lowercase			,    lowercase						)       ->       str:
				__a       :     List[Any]								=	ctx_tokenizer(
				    documents["""title"""]			,    documents["""text"""]			,    truncation=__lowerCamelCase			,    padding="""longest"""			,    return_tensors="""pt"""						)["""input_ids"""]
				__a       :     Union[str, Any]								=	ctx_encoder(input_ids.to(device=__lowerCamelCase						)			,    return_dict=__lowerCamelCase						).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}

def 				_snake_case (		lowercase			,    lowercase			,    lowercase			,    )       ->       List[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       :     int								=	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       :     Optional[Any]								=	dataset.map(__lowerCamelCase			,    batched=__lowerCamelCase			,    num_proc=processing_args.num_proc						)

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

				# And finally save your dataset
				__a       :     List[str]								=	os.path.join(rag_example_args.output_dir			,    """my_knowledge_dataset"""						)
				dataset.save_to_disk(__lowerCamelCase						)
				# 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       :     Optional[Any]								=	faiss.IndexHNSWFlat(index_hnsw_args.d			,    index_hnsw_args.m			,    faiss.METRIC_INNER_PRODUCT						)
				dataset.add_faiss_index("""embeddings"""			,    custom_index=__lowerCamelCase						)

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

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

				# Easy way to load the model
				__a       :     Dict								=	RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name			,    index_name="""custom"""			,    indexed_dataset=__lowerCamelCase						)
				__a       :     List[Any]								=	RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,    retriever=__lowerCamelCase						)
				__a       :     Tuple								=	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       :     Optional[Any]								=	rag_example_args.question or """What does Moses' rod turn into ?"""
				__a       :     Tuple								=	tokenizer.question_encoder(__lowerCamelCase			,    return_tensors="""pt"""						)["""input_ids"""]
				__a       :     Tuple								=	model.generate(__lowerCamelCase						)
				__a       :     List[Any]								=	tokenizer.batch_decode(__lowerCamelCase			,    skip_special_tokens=__lowerCamelCase						)[0]
				logger.info("""Q: """ + question						)
				logger.info("""A: """ + generated_string						)
@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(
	    default=str(Path(snake_case_   ).parent / "test_data" / "my_knowledge_dataset.csv"   )			,					metadata={"help": "Path to a tab-separated csv file with columns \'title\' and \'text\'"}			,					)
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."}			,					)
	lowercase__	    =							field(
	    default="facebook/rag-sequence-nq"			,					metadata={"help": "The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"}			,					)
	lowercase__	    =							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\'"
	        )
	    }			,					)
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}			,					)







@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(
	    default=snake_case_			,					metadata={
	        "help": "The number of processes to use to split the documents into passages. Default is single process."
	    }			,					)
	lowercase__	    =							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__       :
	lowercase__	    =							field(
	    default=7_68			,					metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}			,					)
	lowercase__	    =							field(
	    default=1_28			,					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		:  Optional[Any]        =      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE		:  str        =      parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      rag_example_args.output_dir or tmp_dir
										main(rag_example_args, processing_args, index_hnsw_args)







"""simple docstring"""

import math






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number == 0:
												__magic_name__      =  "Zero"
												return words
						else:
												__magic_name__      =  math.logaa(__lowerCamelCase )
												__magic_name__      =  digits + 1
												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "One"
												__magic_name__      =  "Two"
												__magic_name__      =  "Three"
												__magic_name__      =  "Four"
												__magic_name__      =  "Five"
												__magic_name__      =  "Six"
												__magic_name__      =  "Seven"
												__magic_name__      =  "Eight"
												__magic_name__      =  "Nine"

												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "Twenty"
												__magic_name__      =  "Thirty"
												__magic_name__      =  "Forty"
												__magic_name__      =  "Fifty"
												__magic_name__      =  "Sixty"
												__magic_name__      =  "Seventy"
												__magic_name__      =  "Eighty"
												__magic_name__      =  "Ninety"

												__magic_name__      =  {}
												__magic_name__      =  "Ten"
												__magic_name__      =  "Eleven"
												__magic_name__      =  "Twelve"
												__magic_name__      =  "Thirteen"
												__magic_name__      =  "Fourteen"
												__magic_name__      =  "Fifteen"
												__magic_name__      =  "Sixteen"
												__magic_name__      =  "Seventeen"
												__magic_name__      =  "Eighteen"
												__magic_name__      =  "Nineteen"

												__magic_name__      =  {}
												__magic_name__      =  "Hundred,"
												__magic_name__      =  "Thousand,"
												__magic_name__      =  "Lakh,"
												__magic_name__      =  "Crore,"

												__magic_name__      =  number
												__magic_name__      =  ""
												__magic_name__      =  0
												__magic_name__      =  int(__lowerCamelCase )
												while counter < digits:
																		__magic_name__      =  temp_num % 1_0
																		if counter % 2 == 0:
																								__magic_name__      =  ""
																								if counter in placevalue and current != 0:
																														__magic_name__      =  placevalue[counter]
																								if counter == 2:
																														__magic_name__      =  singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words

																								else:
																														__magic_name__      =  doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				__magic_name__      =  teens[number % 1_0] + words
																														else:
																																				__magic_name__      =  ""
																																				if counter in placevalue:
																																										__magic_name__      =  placevalue[counter]
																																				__magic_name__      =  doubles[current] + addition + words
																								else:
																														__magic_name__      =  ""
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										__magic_name__      =  ""
																																				else:
																																										__magic_name__      =  placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words
																		counter += 1
																		__magic_name__      =  temp_num // 1_0
						return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()



