






'''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


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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=100     ,      lowerCamelCase_=" ")							->     List[str]:
		UpperCamelCase__				:					Optional[int]					=					text.split(lowerCamelCase_)
		return [character.join(text[i : i + n]).strip() for i in range(0     ,      len(lowerCamelCase_)     ,      lowerCamelCase_)]







def       __UpperCAmelCase						(  lowerCamelCase_)							->     dict:
		UpperCamelCase__,	UpperCamelCase__				:					Any					=					[], []
		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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     dict:
		UpperCamelCase__				:					str					=					ctx_tokenizer(
		    documents['title']     ,      documents['text']     ,      truncation=lowerCamelCase_     ,      padding='longest'     ,      return_tensors='pt')['input_ids']
		UpperCamelCase__				:					Dict					=					ctx_encoder(input_ids.to(device=lowerCamelCase_)     ,      return_dict=lowerCamelCase_).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )							->     int:
		######################################
		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__				:					Union[str, Any]					=					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__				:					str					=					dataset.map(lowerCamelCase_     ,      batched=lowerCamelCase_     ,      num_proc=processing_args.num_proc)

		# And compute the embeddings
		UpperCamelCase__				:					Optional[int]					=					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=lowerCamelCase_)
		UpperCamelCase__				:					str					=					DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
		UpperCamelCase__				:					str					=					Features(
		    {'text': Value('string'), 'title': Value('string'), 'embeddings': Sequence(Value('float32'))})  # optional, save as float32 instead of float64 to save space
		UpperCamelCase__				:					List[Any]					=					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
		UpperCamelCase__				:					Optional[int]					=					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
		UpperCamelCase__				:					List[Any]					=					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
		UpperCamelCase__				:					int					=					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







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







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







@dataclass
class       __lowercase					:
	_lowerCamelCase              =  field(
	    default=768   ,				metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}   ,				)
	_lowerCamelCase              =  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)












'''simple docstring'''




from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase__          =						300  # TEMPERATURE (unit = K)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )							->     float:

		if donor_conc <= 0:
				raise ValueError('Donor concentration should be positive')
		elif acceptor_conc <= 0:
				raise ValueError('Acceptor concentration should be positive')
		elif intrinsic_conc <= 0:
				raise ValueError('Intrinsic concentration should be positive')
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    'Donor concentration should be greater than intrinsic concentration')
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    'Acceptor concentration should be greater than intrinsic concentration')
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




