
"""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


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






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   Dict=100		,					snake_case__      :   int=" "   ):
	A        =      text.split(snake_case__   )
	return [character.join(text[i : i + n]   ).strip() for i in range(0		,					len(snake_case__   )		,					snake_case__   )]






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






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






def 					_snake_case (						snake_case__      :   "RagExampleArguments"		,					snake_case__      :   "ProcessingArguments"		,					snake_case__      :   "IndexHnswArguments"		,					):
	######################################
	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(snake_case__		,					batched=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=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(snake_case__		,					ctx_encoder=snake_case__		,					ctx_tokenizer=snake_case__   )		,					batched=snake_case__		,					batch_size=processing_args.batch_size		,					features=snake_case__		,					)

	# And finally save your dataset
	A        =      os.path.join(rag_example_args.output_dir		,					'my_knowledge_dataset'   )
	dataset.save_to_disk(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=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(snake_case__   )
	# dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class  lowerCAmelCase_ :



						'''simple docstring'''


						_lowerCamelCase:			str					=			field(
						    default=str(Path(_lowercase			).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''			)						,    metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}						,    )
						_lowerCamelCase:			Optional[str]					=			field(
						    default=_lowercase						,    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=str(Path(_lowercase			).parent / '''test_run''' / '''dummy-kb'''			)						,    metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}						,    )







@dataclass
class  lowerCAmelCase_ :



						'''simple docstring'''


						_lowerCamelCase:			Optional[int]					=			field(
						    default=_lowercase						,    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  lowerCAmelCase_ :



						'''simple docstring'''


						_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)

		_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)






class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase ):
        A__											=						set_counts
        A__											=						max(__lowerCamelCase )
        A__											=						len(__lowerCamelCase )
        A__											=						[1] * num_sets
        A__											=						list(range(__lowerCamelCase ) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.get_parent(__lowerCamelCase )
        A__											=						self.get_parent(__lowerCamelCase )

        if src_parent == dst_parent:
               return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
               self.set_counts[dst_parent] += self.set_counts[src_parent]
               A__											=						0
               A__											=						dst_parent
               if self.ranks[dst_parent] == self.ranks[src_parent]:
                      self.ranks[dst_parent] += 1
               A__											=						self.set_counts[dst_parent]
        else:
               self.set_counts[src_parent] += self.set_counts[dst_parent]
               A__											=						0
               A__											=						src_parent
               A__											=						self.set_counts[src_parent]

        A__											=						max(self.max_set,__lowerCamelCase )
        return True




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.parents[disj_set] == disj_set:
               return disj_set
        A__											=						self.get_parent(self.parents[disj_set] )
        return self.parents[disj_set]





