


'''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,
)


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




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str=100	,  lowerCamelCase_ :				int=" "						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				dict						):
				'''simple docstring'''




				__magic_name__  ,      __magic_name__	  = [], []
				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       (       lowerCamelCase_ :				dict	,  lowerCamelCase_ :				DPRContextEncoder	,  lowerCamelCase_ :				DPRContextEncoderTokenizerFast						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				"RagExampleArguments"	,  lowerCamelCase_ :				"ProcessingArguments"	,  lowerCamelCase_ :				"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
				__magic_name__	  = 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
				__magic_name__	  = dataset.map(lowerCamelCase_	,  batched=lowerCamelCase_	,  num_proc=processing_args.num_proc						)

				# And compute the embeddings
				__magic_name__	  = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=lowerCamelCase_						)
				__magic_name__	  = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
				__magic_name__	  = Features(
				    {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space
				__magic_name__	  = 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
				__magic_name__	  = 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
				__magic_name__	  = 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
				__magic_name__	  = 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
				__magic_name__	  = RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name	,  index_name="custom"	,  indexed_dataset=lowerCamelCase_						)
				__magic_name__	  = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name	,  retriever=lowerCamelCase_						)
				__magic_name__	  = 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"						)
				######################################

				__magic_name__	  = rag_example_args.question or "What does Moses' rod turn into ?"
				__magic_name__	  = tokenizer.question_encoder(lowerCamelCase_	,  return_tensors="pt"						)["input_ids"]
				__magic_name__	  = model.generate(lowerCamelCase_						)
				__magic_name__	  = tokenizer.batch_decode(lowerCamelCase_	,  skip_special_tokens=lowerCamelCase_						)[0]
				logger.info("Q: " + question						)
				logger.info("A: " + generated_string						)






@dataclass
class 		UpperCamelCase_		:





						"""simple docstring"""




						UpperCAmelCase__	:       str         =     field(
						    default=str(Path(A   ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''   )   ,		metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}   ,		)
						UpperCAmelCase__	:       Optional[str]         =     field(
						    default=A   ,		metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}   ,		)
						UpperCAmelCase__	:       str         =     field(
						    default='''facebook/rag-sequence-nq'''   ,		metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}   ,		)
						UpperCAmelCase__	:       str         =     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__	:       Optional[str]         =     field(
						    default=A   ,		metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}   ,		)






@dataclass
class 		UpperCamelCase_		:





						"""simple docstring"""




						UpperCAmelCase__	:       Optional[int]         =     field(
						    default=A   ,		metadata={
						        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
						    }   ,		)
						UpperCAmelCase__	:       int         =     field(
						    default=16   ,		metadata={
						        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
						    }   ,		)






@dataclass
class 		UpperCamelCase_		:





						"""simple docstring"""




						UpperCAmelCase__	:       int         =     field(
						    default=768   ,		metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}   ,		)
						UpperCAmelCase__	:       int         =     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)

						__magic_name__   :   Tuple  							=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						__magic_name__							,    __magic_name__							,    __magic_name__   :   List[str]  							=parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												__magic_name__   :   Optional[int]  							=rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



