




"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_snake_case					   =     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''





  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  return max(metric_fn(__a  ,     __a       ) for gt in ground_truths       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Union[str, Any]    =			[line.strip() for line in open(__a  ,     """r"""       ).readlines()]
  _a						: str    =			[]

  if args.gold_data_mode == "qa":
    _a						: Union[str, Any]    =			pd.read_csv(__a  ,     sep="""\t"""  ,     header=__a       )
    for answer_list in data[1]:
      _a						: str    =			ast.literal_eval(__a       )
      answers.append(__a       )
  else:
    _a						: List[Any]    =			[line.strip() for line in open(__a  ,     """r"""       ).readlines()]
    _a						: List[str]    =			[[reference] for reference in references]

  _a						: Union[str, Any]    =			0
  for prediction, ground_truths in zip(__a  ,     __a       ):
    total += 1
    em += metric_max_over_ground_truths(__a  ,     __a  ,     __a       )
    fa += metric_max_over_ground_truths(__a  ,     __a  ,     __a       )

  _a						: Dict    =			100.0 * em / total
  _a						: str    =			100.0 * fa / total

  logger.info(F"""F1: {fa:.2f}"""       )
  logger.info(F"""EM: {em:.2f}"""       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: str    =			args.k
  _a						: int    =			[line.strip() for line in open(__a  ,     """r"""       ).readlines()]
  _a						: Dict    =			[line.strip() for line in open(__a  ,     """r"""       ).readlines()]

  _a						: Any    =			0
  for hypo, reference in zip(__a  ,     __a       ):
    _a						: Optional[int]    =			set(hypo.split("""\t"""       )[:k]       )
    _a						: List[Any]    =			set(reference.split("""\t"""       )       )
    total += 1
    em += len(hypo_provenance & ref_provenance       ) / k

  _a						: Optional[Any]    =			100.0 * em / total
  logger.info(F"""Precision@{k}: {em: .2f}"""       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''





  def strip_title(UpperCamelCase__       ):
    if title.startswith("""\""""       ):
      _a						: Union[str, Any]    =			title[1:]
    if title.endswith("""\""""       ):
      _a						: Dict    =			title[:-1]
    return title

  _a						: Optional[Any]    =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      __a  ,     return_tensors="""pt"""  ,     padding=__a  ,     truncation=__a  ,     )['''input_ids'''].to(args.device       )

  _a						: Dict    =			rag_model.rag.question_encoder(__a       )
  _a						: Optional[int]    =			question_enc_outputs[0]

  _a						: Union[str, Any]    =			rag_model.retriever(
      __a  ,     question_enc_pool_output.cpu().detach().to(torch.floataa       ).numpy()  ,     prefix=rag_model.rag.generator.config.prefix  ,     n_docs=rag_model.config.n_docs  ,     return_tensors="""pt"""  ,     )
  _a						: Optional[Any]    =			rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
  _a						: Union[str, Any]    =			[]
  for docs in all_docs:
    _a						: List[Any]    =			[strip_title(__a       ) for title in docs['''title''']]
    provenance_strings.append("""\t""".join(__a       )       )
  return provenance_strings

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''





  with torch.no_grad():
    _a						: List[str]    =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        __a  ,     return_tensors="""pt"""  ,     padding=__a  ,     truncation=__a       )

    _a						: int    =			inputs_dict.input_ids.to(args.device       )
    _a						: Tuple    =			inputs_dict.attention_mask.to(args.device       )
    _a						: Union[str, Any]    =			rag_model.generate(  # rag_model overwrites generate
        __a  ,     attention_mask=__a  ,     num_beams=args.num_beams  ,     min_length=args.min_length  ,     max_length=args.max_length  ,     early_stopping=__a  ,     num_return_sequences=1  ,     bad_words_ids=[[0, 0]]  ,     )
    _a						: Any    =			rag_model.retriever.generator_tokenizer.batch_decode(__a  ,     skip_special_tokens=__a       )

    if args.print_predictions:
      for q, a in zip(__a  ,     __a       ):
        logger.info("""Q: {} - A: {}""".format(__a  ,     __a       )       )

    return answers

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[int]    =			argparse.ArgumentParser()
  parser.add_argument(
      """--model_type"""  ,     choices=["""rag_sequence""", """rag_token""", """bart"""]  ,     type=__a  ,     help=(
          """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
          """ model_name_or_path"""
      )  ,     )
  parser.add_argument(
      """--index_name"""  ,     default=__a  ,     choices=["""exact""", """compressed""", """legacy"""]  ,     type=__a  ,     help="""RAG model retriever type"""  ,     )
  parser.add_argument(
      """--index_path"""  ,     default=__a  ,     type=__a  ,     help="""Path to the retrieval index"""  ,     )
  parser.add_argument("""--n_docs"""  ,     default=5  ,     type=__a  ,     help="""Number of retrieved docs"""       )
  parser.add_argument(
      """--model_name_or_path"""  ,     default=__a  ,     type=__a  ,     required=__a  ,     help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""  ,     )
  parser.add_argument(
      """--eval_mode"""  ,     choices=["""e2e""", """retrieval"""]  ,     default="""e2e"""  ,     type=__a  ,     help=(
          """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
          """ precision@k."""
      )  ,     )
  parser.add_argument("""--k"""  ,     default=1  ,     type=__a  ,     help="""k for the precision@k calculation"""       )
  parser.add_argument(
      """--evaluation_set"""  ,     default=__a  ,     type=__a  ,     required=__a  ,     help="""Path to a file containing evaluation samples"""  ,     )
  parser.add_argument(
      """--gold_data_path"""  ,     default=__a  ,     type=__a  ,     required=__a  ,     help="""Path to a tab-separated file with gold samples"""  ,     )
  parser.add_argument(
      """--gold_data_mode"""  ,     default="""qa"""  ,     type=__a  ,     choices=["""qa""", """ans"""]  ,     help=(
          """Format of the gold data file"""
          """qa - a single line in the following format: question [tab] answer_list"""
          """ans - a single line of the gold file contains the expected answer string"""
      )  ,     )
  parser.add_argument(
      """--predictions_path"""  ,     type=__a  ,     default="""predictions.txt"""  ,     help="""Name of the predictions file, to be stored in the checkpoints directory"""  ,     )
  parser.add_argument(
      """--eval_all_checkpoints"""  ,     action="""store_true"""  ,     help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""  ,     )
  parser.add_argument(
      """--eval_batch_size"""  ,     default=8  ,     type=__a  ,     help="""Batch size per GPU/CPU for evaluation."""  ,     )
  parser.add_argument(
      """--recalculate"""  ,     help="""Recalculate predictions even if the prediction file exists"""  ,     action="""store_true"""  ,     )
  parser.add_argument(
      """--num_beams"""  ,     default=4  ,     type=__a  ,     help="""Number of beams to be used when generating answers"""  ,     )
  parser.add_argument("""--min_length"""  ,     default=1  ,     type=__a  ,     help="""Min length of the generated answers"""       )
  parser.add_argument("""--max_length"""  ,     default=5_0  ,     type=__a  ,     help="""Max length of the generated answers"""       )

  parser.add_argument(
      """--print_predictions"""  ,     action="""store_true"""  ,     help="""If True, prints predictions while evaluating."""  ,     )
  parser.add_argument(
      """--print_docs"""  ,     action="""store_true"""  ,     help="""If True, prints docs retried while generating."""  ,     )
  _a						: Dict    =			parser.parse_args()
  _a						: Optional[Any]    =			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )
  return args

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: int    =			{}
  if args.model_type is None:
    _a						: Union[str, Any]    =			infer_model_type(args.model_name_or_path       )
    assert args.model_type is not None
  if args.model_type.startswith("""rag"""       ):
    _a						: Any    =			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
    _a						: Dict    =			args.n_docs
    if args.index_name is not None:
      _a						: int    =			args.index_name
    if args.index_path is not None:
      _a						: int    =			args.index_path
  else:
    _a						: Any    =			BartForConditionalGeneration

  _a						: Optional[Any]    =			(
      [f.path for f in os.scandir(args.model_name_or_path       ) if f.is_dir()]
      if args.eval_all_checkpoints
      else [args.model_name_or_path]
  )

  logger.info("""Evaluate the following checkpoints: %s"""  ,     __a       )

  _a						: Tuple    =			get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
  _a						: int    =			evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

  for checkpoint in checkpoints:
    if os.path.exists(args.predictions_path       ) and (not args.recalculate):
      logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path       )       )
      score_fn(__a  ,     args.predictions_path  ,     args.gold_data_path       )
      continue

    logger.info("""***** Running evaluation for {} *****""".format(__a       )       )
    logger.info("""  Batch size = %d"""  ,     args.eval_batch_size       )
    logger.info("""  Predictions will be stored under {}""".format(args.predictions_path       )       )

    if args.model_type.startswith("""rag"""       ):
      _a						: Optional[Any]    =			RagRetriever.from_pretrained(__a  ,     **__a       )
      _a						: int    =			model_class.from_pretrained(__a  ,     retriever=__a  ,     **__a       )
      model.retriever.init_retrieval()
    else:
      _a						: Tuple    =			model_class.from_pretrained(__a  ,     **__a       )
    model.to(args.device       )

    with open(args.evaluation_set  ,     """r"""       ) as eval_file, open(args.predictions_path  ,     """w"""       ) as preds_file:
      _a						: Optional[int]    =			[]
      for line in tqdm(__a       ):
        questions.append(line.strip()       )
        if len(__a       ) == args.eval_batch_size:
          _a						: Any    =			evaluate_batch_fn(__a  ,     __a  ,     __a       )
          preds_file.write("""\n""".join(__a       ) + """\n"""       )
          preds_file.flush()
          _a						: Optional[Any]    =			[]
      if len(__a       ) > 0:
        _a						: Tuple    =			evaluate_batch_fn(__a  ,     __a  ,     __a       )
        preds_file.write("""\n""".join(__a       )       )
        preds_file.flush()

      score_fn(__a  ,     args.predictions_path  ,     args.gold_data_path       )


if __name__ == "__main__":
 _snake_case					   =     get_args()
 main(args)









"""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,
)


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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  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__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''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
  _a						: Dict    =			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						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

  # And compute the embeddings
  _a						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: str    =			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
  _a						: List[Any]    =			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
  _a						: List[str]    =			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
  _a						: List[str]    =			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
  _a						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[Any]    =			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"""       )
  ######################################

  _a						: Optional[Any]    =			rag_example_args.question or """What does Moses' rod turn into ?"""
  _a						: int    =			tokenizer.question_encoder(UpperCamelCase__  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Union[str, Any]    =			model.generate(UpperCamelCase__       )
  _a						: Any    =			tokenizer.batch_decode(UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__       )[0]
  logger.info("""Q: """ + question       )
  logger.info("""A: """ + generated_string       )






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







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







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

 _snake_case					   =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _snake_case   ,				_snake_case   ,				_snake_case					   =     parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _snake_case					   =     rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)

