






"""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,
)


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






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:List[Any]=1_0_0			,    __lowerCamelCase:Optional[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 _lowerCAmelCase						(	__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 _lowerCAmelCase						(	__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 _lowerCAmelCase						(	__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     A_				:
				UpperCAmelCase__										=					field(
				    default=str(Path(snake_case_	).parent / '''test_data''' / '''my_knowledge_dataset.csv'''	)       ,				metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}       ,				)
				UpperCAmelCase__										=					field(
				    default=snake_case_       ,				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=snake_case_       ,				metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}       ,				)





@dataclass
class     A_				:
				UpperCAmelCase__										=					field(
				    default=snake_case_       ,				metadata={
				        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
				    }       ,				)
				UpperCAmelCase__										=					field(
				    default=1_6       ,				metadata={
				        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
				    }       ,				)





@dataclass
class     A_				:
				UpperCAmelCase__										=					field(
				    default=7_6_8       ,				metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}       ,				)
				UpperCAmelCase__										=					field(
				    default=1_2_8       ,				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)

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











"""simple docstring"""

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class     A_				(   unittest.TestCase	):
				UpperCAmelCase__										=					EsmTokenizer







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										super().setUp()
										__magic_name__      =  tempfile.mkdtemp()
										# fmt: off
										__magic_name__      =  ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
										# fmt: on
										__magic_name__      =  os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES["vocab_file"]						)
										with open(self.vocab_file    ,     "w"    ,     encoding="utf-8"						) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)







				def      _snake_case       (				self				:    Optional[Any]    ,     **__lowerCamelCase				:    str						)					->       List[PreTrainedTokenizerBase]:
										return [self.get_tokenizer(**__lowerCamelCase						)]







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    int						)					->       PreTrainedTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  tokenizer.tokenize("LAGVS"						)
										self.assertListEqual(__lowerCamelCase    ,     ["L", "A", "G", "V", "S"]						)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase						)    ,     [4, 5, 6, 7, 8]						)







				def      _snake_case       (				self				:    int						)					->       Dict:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  "LAGVS"
										self.assertListEqual(tokenizer.encode(__lowerCamelCase						)    ,     [0, 4, 5, 6, 7, 8, 2]						)







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizers(fast=__lowerCamelCase						)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																						__magic_name__      =  "<unk>"
																						__magic_name__      =  "<mask>"

																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)
																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)

																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)



