



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,
)


lowerCamelCase__     =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__     =	"""cuda""" if torch.cuda.is_available() else """cpu"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     str=100			,       _SCREAMING_SNAKE_CASE    :     Any=" "      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     dict      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     dict			,       _SCREAMING_SNAKE_CASE    :     DPRContextEncoder			,       _SCREAMING_SNAKE_CASE    :     DPRContextEncoderTokenizerFast      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     "RagExampleArguments"			,       _SCREAMING_SNAKE_CASE    :     "ProcessingArguments"			,       _SCREAMING_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
				__a					       =						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					       =						dataset.map(_SCREAMING_SNAKE_CASE			,       batched=_SCREAMING_SNAKE_CASE			,       num_proc=processing_args.num_proc      )

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

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

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

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

				# Easy way to load the model
				__a					       =						RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name			,       index_name="""custom"""			,       indexed_dataset=_SCREAMING_SNAKE_CASE      )
				__a					       =						RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_SCREAMING_SNAKE_CASE      )
				__a					       =						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					       =						rag_example_args.question or """What does Moses' rod turn into ?"""
				__a					       =						tokenizer.question_encoder(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""      )["""input_ids"""]
				__a					       =						model.generate(_SCREAMING_SNAKE_CASE      )
				__a					       =						tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,       skip_special_tokens=_SCREAMING_SNAKE_CASE      )[0]
				logger.info("""Q: """ + question      )
				logger.info("""A: """ + generated_string      )






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






@dataclass
class 				SCREAMING_SNAKE_CASE    :
							__lowerCamelCase		:		Optional[int]      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }     , )
							__lowerCamelCase		:		int      =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    :
							__lowerCamelCase		:		int      =field(
							    default=768     , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}     , )
							__lowerCamelCase		:		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)

						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)





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os






class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		Tuple      ='block_{0}.dat'





							def __init__(				self						:						int ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						filename
											__a					       =						[]





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Dict ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.BLOCK_FILENAME_FORMAT.format(__lowercase		)
											with open(__lowercase ,			"""w"""		) as file:
															file.write(__lowercase		)
											self.block_filenames.append(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											return self.block_filenames





							def   UpperCamelCase_   (				self						:						Any ,			__lowercase						:						str ,			__lowercase						:						Tuple=None		):

											'''simple docstring'''
											__a					       =						0
											with open(self.filename		) as file:
															while True:
																			__a					       =						file.readlines(__lowercase		)

																			if lines == []:
																							break

																			if sort_key is None:
																							lines.sort()
																			else:
																							lines.sort(key=__lowercase		)

																			self.write_block("""""".join(__lowercase		) ,			__lowercase		)
																			i += 1







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											map(os.remove ,			self.block_filenames		)






class 				SCREAMING_SNAKE_CASE     :





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						-1
											__a					       =						None

											for i in range(len(__lowercase		)		):
															if min_str is None or choices[i] < min_str:
																			__a					       =						i

											return min_index






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						files
											__a					       =						set()
											__a					       =						len(__lowercase		)
											__a					       =						{i: None for i in range(self.num_buffers		)}





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											return {
											    i: self.buffers[i] for i in range(self.num_buffers		) if i not in self.empty
											}





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											for i in range(self.num_buffers		):
															if self.buffers[i] is None and i not in self.empty:
																			__a					       =						self.files[i].readline()

																			if self.buffers[i] == "":
																							self.empty.add(__lowercase		)
																							self.files[i].close()

											if len(self.empty		) == self.num_buffers:
															return False

											return True







							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.buffers[index]
											__a					       =						None

											return value






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						Any		):

											'''simple docstring'''
											__a					       =						merge_strategy





							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[str] ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						FilesArray(self.get_file_handles(__lowercase ,			__lowercase		)		)
											with open(__lowercase ,			"""w""" ,			__lowercase		) as outfile:
															while buffers.refresh():
																			__a					       =						self.merge_strategy.select(buffers.get_dict()		)
																			outfile.write(buffers.unshift(__lowercase		)		)







							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						str ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						{}

											for i in range(len(__lowercase		)		):
															__a					       =						open(filenames[i] ,			"""r""" ,			__lowercase		)  # noqa: UP015

											return files






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						str ,			__lowercase						:						Tuple		):

											'''simple docstring'''
											__a					       =						block_size





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						str ,			__lowercase						:						Union[str, Any]=None		):

											'''simple docstring'''
											__a					       =						self.get_number_blocks(__lowercase ,			self.block_size		)
											__a					       =						FileSplitter(__lowercase		)
											splitter.split(self.block_size ,			__lowercase		)

											__a					       =						FileMerger(NWayMerge()		)
											__a					       =						self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames() ,			filename + """.out""" ,			__lowercase		)

											splitter.cleanup()







							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Dict		):

											'''simple docstring'''
											return (os.stat(__lowercase		).st_size / block_size) + 1



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				if string[-1].lower() == "k":
								return int(string[:-1]      ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]      ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]      ) * 1024 * 1024 * 1024
				else:
								return int(_SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						argparse.ArgumentParser()
				parser.add_argument(
				    """-m"""			,       """--mem"""			,       help="""amount of memory to use for sorting"""			,       default="""100M"""      )
				parser.add_argument(
				    """filename"""			,       metavar="""<filename>"""			,       nargs=1			,       help="""name of file to sort"""      )
				__a					       =						parser.parse_args()

				__a					       =						ExternalSort(parse_memory(args.mem      )      )
				sorter.sort(args.filename[0]      )


if __name__ == "__main__":
						main()
