
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,
)


SCREAMING_SNAKE_CASE__     :    Any      							=					logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE__     :    str      							=					"cuda" if torch.cuda.is_available() else "cpu"




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		str=100	,						__lowerCAmelCase			:		Tuple=" "							)    ->     List[str]:
				__lowerCamelCase					      =			text.split(__lowerCAmelCase							)
				return [character.join(text[i : i + n]							).strip() for i in range(0	,						len(__lowerCAmelCase							)	,						__lowerCAmelCase							)]




def 				__magic_name__				(     __lowerCAmelCase			:		dict							)    ->     dict:
				__lowerCamelCase      ,					__lowerCamelCase					      =			[], []
				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 				__magic_name__				(     __lowerCAmelCase			:		dict	,						__lowerCAmelCase			:		DPRContextEncoder	,						__lowerCAmelCase			:		DPRContextEncoderTokenizerFast							)    ->     dict:
				__lowerCamelCase					      =			ctx_tokenizer(
				    documents['''title''']	,						documents['''text''']	,						truncation=__lowerCAmelCase	,						padding='''longest'''	,						return_tensors='''pt'''							)['''input_ids''']
				__lowerCamelCase					      =			ctx_encoder(input_ids.to(device=__lowerCAmelCase							)	,						return_dict=__lowerCAmelCase							).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}




def 				__magic_name__				(     __lowerCAmelCase			:		"RagExampleArguments"	,						__lowerCAmelCase			:		"ProcessingArguments"	,						__lowerCAmelCase			:		"IndexHnswArguments"	,						)    ->     Dict:
				######################################
				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
				__lowerCamelCase					      =			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
				__lowerCamelCase					      =			dataset.map(__lowerCAmelCase	,						batched=__lowerCAmelCase	,						num_proc=processing_args.num_proc							)

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

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

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





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





@dataclass
class      lowerCAmelCase__  :
		a__ :		int   						=	field(
		    default=768						,			metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}						,			)
		a__ :		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)

					SCREAMING_SNAKE_CASE__     :    List[str]      							=					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										SCREAMING_SNAKE_CASE__     :    Dict      							=					rag_example_args.output_dir or tmp_dir
										main(rag_example_args, processing_args, index_hnsw_args)





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



