"""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			(__UpperCamelCase							:    str							,					__UpperCamelCase							:    Optional[Any]=1_0_0							,					__UpperCamelCase							:    Union[str, Any]=" "			):
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    dict			):
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    dict							,					__UpperCamelCase							:    DPRContextEncoder							,					__UpperCamelCase							:    DPRContextEncoderTokenizerFast			):
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    "RagExampleArguments"							,					__UpperCamelCase							:    "ProcessingArguments"							,					__UpperCamelCase							:    "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
							__UpperCamelCase							=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
							__UpperCamelCase							=dataset.map(__UpperCamelCase							,					batched=__UpperCamelCase							,					num_proc=processing_args.num_proc			)

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

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

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

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

							# Easy way to load the model
							__UpperCamelCase							=RagRetriever.from_pretrained(
							    rag_example_args.rag_model_name							,					index_name='''custom'''							,					indexed_dataset=__UpperCamelCase			)
							__UpperCamelCase							=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,					retriever=__UpperCamelCase			)
							__UpperCamelCase							=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'''			)
							######################################

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






@dataclass
class 		_lowercase :



						"""simple docstring"""




						lowercase__       =  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\''''}					,					)
						lowercase__       =  field(
						    default=__a					,					metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}					,					)
						lowercase__       =  field(
						    default='''facebook/rag-sequence-nq'''					,					metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}					,					)
						lowercase__       =  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\''''
						        )
						    }					,					)
						lowercase__       =  field(
						    default=__a					,					metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}					,					)






@dataclass
class 		_lowercase :



						"""simple docstring"""




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






@dataclass
class 		_lowercase :



						"""simple docstring"""




						lowercase__       =  field(
						    default=7_68					,					metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}					,					)
						lowercase__       =  field(
						    default=1_28					,					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 json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


							class 		_lowercase :



													"""simple docstring"""




													def __init__(				self							:    int     , UpperCamelCase__							:    Union[str, Any]				)				->				None:
																				'''simple docstring'''



																				assert isinstance(UpperCamelCase__     , (bytes, str)				)
																				__UpperCamelCase							=content





													def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
																				'''simple docstring'''



																				return json.loads(self.content				)

							def mock_response(*__UpperCamelCase							:    Any							,					**__UpperCamelCase							:    Union[str, Any]			):
														assert args[0] == AUTHENTICATED_USER_ENDPOINT
														assert "Authorization" in kwargs["headers"]
														assert kwargs["headers"]["Authorization"].startswith('''token '''			)
														assert "Accept" in kwargs["headers"]
														return FakeResponse(b'''{"login":"test","id":1}'''			)

							monkeypatch.setattr(__UpperCamelCase							,					'''get'''							,					__UpperCamelCase			)
							__UpperCamelCase							=fetch_github_info('''token'''			)
							assert result["login"] == "test"
							assert result["id"] == 1



