






'''simple docstring'''







import os
from datetime import datetime as dt

from github import Github


__lowerCAmelCase									=   [
    'good first issue',
    'feature request',
    'wip',
]





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   Github(os.environ['''GITHUB_TOKEN''']   )
				a_		:	Tuple									=   g.get_repo('''huggingface/accelerate'''   )
				a_		:	Optional[Any]									=   repo.get_issues(state='''open'''   )

				for issue in open_issues:
								a_		:	int									=   sorted([comment for comment in issue.get_comments()]    ,		key=lambda __A   : i.created_at    ,		reverse=__A   )
								a_		:	int									=   comments[0] if len(__A   ) > 0 else None
								a_		:	Optional[int]									=   dt.utcnow()
								a_		:	int									=   (current_time - issue.updated_at).days
								a_		:	int									=   (current_time - issue.created_at).days
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and days_since_updated > 7
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
								):
												# Close issue since it has been 7 days of inactivity since bot mention.
												issue.edit(state='''closed'''   )
								elif (
								    days_since_updated > 23
								    and days_since_creation >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
								):
												# Add stale comment
												issue.create_comment(
												    '''This issue has been automatically marked as stale because it has not had '''
												    '''recent activity. If you think this still needs to be addressed '''
												    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
												    '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''   )


if __name__ == "__main__":
		main()












'''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 		_UpperCAmelCase (				__A				: str    ,		__A				: Optional[Any]=1_00    ,		__A				: int=" "   ):
				a_		:	Optional[int]									=   text.split(__A   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0    ,		len(__A   )    ,		__A   )]





def 		_UpperCAmelCase (				__A				: dict   ):
				a_					,      a_		:	List[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 		_UpperCAmelCase (				__A				: dict    ,		__A				: DPRContextEncoder    ,		__A				: DPRContextEncoderTokenizerFast   ):
				a_		:	int									=   ctx_tokenizer(
				    documents['''title''']    ,		documents['''text''']    ,		truncation=__A    ,		padding='''longest'''    ,		return_tensors='''pt'''   )['''input_ids''']
				a_		:	List[Any]									=   ctx_encoder(input_ids.to(device=__A   )    ,		return_dict=__A   ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}





def 		_UpperCAmelCase (				__A				: "RagExampleArguments"    ,		__A				: "ProcessingArguments"    ,		__A				: "IndexHnswArguments"    ,		):
				######################################
				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_		:	List[str]									=   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_		:	Dict									=   dataset.map(__A    ,		batched=__A    ,		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=__A   )
				a_		:	Tuple									=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
				a_		:	Union[str, Any]									=   Features(
				    {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space
				a_		:	Union[str, Any]									=   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
				a_		:	Tuple									=   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
				a_		:	Dict									=   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
				a_		:	Optional[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       :
			snake_case__	=					field(
			    default=str(Path(SCREAMING_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'"}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}						, )
			snake_case__	=					field(
			    default="facebook/rag-sequence-nq"						, metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}						, )
			snake_case__	=					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'"
			        )
			    }						, )
			snake_case__	=					field(
			    default=str(Path(SCREAMING_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 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }						, )
			snake_case__	=					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       :
			snake_case__	=					field(
			    default=768						, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						, )
			snake_case__	=					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)




