

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


_SCREAMING_SNAKE_CASE  :						str         =    logging.getLogger(__name__)
torch.set_grad_enabled(False)
_SCREAMING_SNAKE_CASE  :						List[Any]         =    '''cuda''' if torch.cuda.is_available() else '''cpu'''
def        UpperCAmelCase_     (		_A   ,	_A=1_00   ,	_A=" "       ):





     '''simple docstring'''




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





     '''simple docstring'''




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





     '''simple docstring'''




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





     '''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
     SCREAMING_SNAKE_CASE__					=					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
     SCREAMING_SNAKE_CASE__					=					dataset.map(_A   ,	batched=_A   ,	num_proc=processing_args.num_proc       )

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

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

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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=7_68		,  metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}		,  )
  a     =      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)

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





def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for data in source_data:
          for i, el in enumerate(_A       ):
               if len(_A       ) < i + 1:
                    data_lists.append([]       )
               data_lists[i].append(float(_A       )       )
     return data_lists
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for dlist, weight in zip(_A   ,	_A       ):
          SCREAMING_SNAKE_CASE__					=					min(_A       )
          SCREAMING_SNAKE_CASE__					=					max(_A       )

          SCREAMING_SNAKE_CASE__					=					[]
          # for weight 0 score is 1 - actual score
          if weight == 0:
               for item in dlist:
                    try:
                         score.append(1 - ((item - mind) / (maxd - mind))       )
                    except ZeroDivisionError:
                         score.append(1       )

          elif weight == 1:
               for item in dlist:
                    try:
                         score.append((item - mind) / (maxd - mind)       )
                    except ZeroDivisionError:
                         score.append(0       )

        # weight not 0 or 1
          else:
               SCREAMING_SNAKE_CASE__					=					F'''Invalid weight of {weight:f} provided'''
               raise ValueError(_A       )

          score_lists.append(_A       )

     return score_lists
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[0 for i in range(len(score_lists[0]       )       )]

     for slist in score_lists:
          for j, ele in enumerate(_A       ):
               SCREAMING_SNAKE_CASE__					=					final_scores[j] + ele

     return final_scores







def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_data(_A       )
     SCREAMING_SNAKE_CASE__					=					calculate_each_score(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					generate_final_scores(_A       )

     # append scores to source data
     for i, ele in enumerate(_A       ):
          source_data[i].append(_A       )

     return source_data


