




'''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


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"""cuda""" if torch.cuda.is_available() else """cpu"""


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str=1_0_0							,    snake_case							:	List[str]=" "					):

							'''simple docstring'''






							snake_case_      =				text.split(_snake_case					)
							return [character.join(text[i : i + n]					).strip() for i in range(0							,    len(_snake_case					)							,    _snake_case					)]


def 					UpperCamelCase_(     snake_case							:	dict					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	dict							,    snake_case							:	DPRContextEncoder							,    snake_case							:	DPRContextEncoderTokenizerFast					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	"RagExampleArguments"							,    snake_case							:	"ProcessingArguments"							,    snake_case							:	"IndexHnswArguments"							,    ):

							'''simple docstring'''






							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_      =				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_      =				dataset.map(_snake_case							,    batched=_snake_case							,    num_proc=processing_args.num_proc					)

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

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

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




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				Union[str, Any]							=   field(
			    default=str(Path(__snake_case							).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"							)						,						metadata={"help": "Path to a tab-separated csv file with columns \'title\' and \'text\'"}						,						)
			lowerCAmelCase_					:				Any							=   field(
			    default=__snake_case						,						metadata={"help": "Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."}						,						)
			lowerCAmelCase_					:				Optional[int]							=   field(
			    default="facebook/rag-sequence-nq"						,						metadata={"help": "The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\'"}						,						)
			lowerCAmelCase_					:				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\'"
			        )
			    }						,						)
			lowerCAmelCase_					:				Optional[Any]							=   field(
			    default=str(Path(__snake_case							).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		:
			lowerCAmelCase_					:				Dict							=   field(
			    default=__snake_case						,						metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }						,						)
			lowerCAmelCase_					:				Any							=   field(
			    default=16						,						metadata={
			        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
			    }						,						)




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				List[Any]							=   field(
			    default=768						,						metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						,						)
			lowerCAmelCase_					:				str							=   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							:       str						=			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
		_SCREAMING_SNAKE_CASE							:       Dict						=			parser.parse_args_into_dataclasses()
		with TemporaryDirectory() as tmp_dir:
				_SCREAMING_SNAKE_CASE							:       Optional[int]						=			rag_example_args.output_dir or tmp_dir
				main(rag_example_args, processing_args, index_hnsw_args)










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




