
"""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


lowerCAmelCase__							=				logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase__							=				'cuda' if torch.cuda.is_available() else 'cpu'



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



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_ ,SCREAMING_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        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = ctx_tokenizer(
		    documents['''title'''],							documents['''text'''],							truncation=__a,							padding='''longest''',							return_tensors='''pt'''    )['''input_ids''']
		SCREAMING_SNAKE_CASE_						 = ctx_encoder(input_ids.to(device=__a    ),							return_dict=__a    ).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}



def        _lowerCamelCase ( __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
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = dataset.map(__a,							batched=__a,							num_proc=processing_args.num_proc    )

		# And compute the embeddings
		SCREAMING_SNAKE_CASE_						 = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=__a    )
		SCREAMING_SNAKE_CASE_						 = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
		SCREAMING_SNAKE_CASE_						 = Features(
		    {'''text''': Value('''string'''    ), '''title''': Value('''string'''    ), '''embeddings''': Sequence(Value('''float32'''    )    )}    )  # optional, save as float32 instead of float64 to save space
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = 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  snake_case     :
							UpperCAmelCase__						     =      field(
							    default=str(Path(__lowercase   ).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''   )       ,			metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}       ,			)
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			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=str(Path(__lowercase   ).parent / '''test_run''' / '''dummy-kb'''   )       ,			metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}       ,			)







@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			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)

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

"""simple docstring"""




def        _lowerCamelCase ( __a    ):

		if not isinstance(__a,							__a    ):
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be an integer'
				raise TypeError(__a    )

		if number < 1:
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be > 0'
				raise ValueError(__a    )

		SCREAMING_SNAKE_CASE_						 = 1

		for i in range(1,							__a    ):
				current_number *= 4 * i - 2
				current_number //= i + 1

		return current_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()