




"""simple docstring"""




import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


_snake_case					   =     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_snake_case					   =     namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			BertTokenizer.from_pretrained("""bert-base-uncased"""  ,     do_lower_case=UpperCamelCase__       )
  _a						: Optional[int]    =			BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""       )
  model.to(args.device       )
  model.eval()

  _a						: List[Any]    =			{
      """BOS""": tokenizer.vocab["""[unused0]"""],
      """EOS""": tokenizer.vocab["""[unused1]"""],
      """PAD""": tokenizer.vocab["""[PAD]"""],
  }

  if args.compute_rouge:
    _a						: Optional[int]    =			[]
    _a						: Tuple    =			[]

    import nltk
    import rouge

    nltk.download("""punkt"""       )
    _a						: int    =			rouge.Rouge(
        metrics=["""rouge-n""", """rouge-l"""]  ,     max_n=2  ,     limit_length=UpperCamelCase__  ,     length_limit=args.beam_size  ,     length_limit_type="""words"""  ,     apply_avg=UpperCamelCase__  ,     apply_best=UpperCamelCase__  ,     alpha=0.5  ,     weight_factor=1.2  ,     stemming=UpperCamelCase__  ,     )

  # these (unused) arguments are defined to keep the compatibility
  # with the legacy code and will be deleted in a next iteration.
  _a						: Optional[Any]    =			""""""
  _a						: Any    =			""""""

  _a						: Tuple    =			build_data_iterator(UpperCamelCase__  ,     UpperCamelCase__       )
  _a						: int    =			build_predictor(UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       )

  logger.info("""***** Running evaluation *****"""       )
  logger.info("""  Number examples = %d"""  ,     len(data_iterator.dataset       )       )
  logger.info("""  Batch size = %d"""  ,     args.batch_size       )
  logger.info(""""""       )
  logger.info("""***** Beam Search parameters *****"""       )
  logger.info("""  Beam size = %d"""  ,     args.beam_size       )
  logger.info("""  Minimum length = %d"""  ,     args.min_length       )
  logger.info("""  Maximum length = %d"""  ,     args.max_length       )
  logger.info("""  Alpha (length penalty) = %.2f"""  ,     args.alpha       )
  logger.info("""  Trigrams %s be blocked"""  ,     ("""will""" if args.block_trigram else """will NOT""")       )

  for batch in tqdm(UpperCamelCase__       ):
    _a						: List[str]    =			predictor.translate_batch(UpperCamelCase__       )
    _a						: Optional[int]    =			predictor.from_batch(UpperCamelCase__       )
    _a						: int    =			[format_summary(UpperCamelCase__       ) for t in translations]
    save_summaries(UpperCamelCase__  ,     args.summaries_output_dir  ,     batch.document_names       )

    if args.compute_rouge:
      reference_summaries += batch.tgt_str
      generated_summaries += summaries

  if args.compute_rouge:
    _a						: Union[str, Any]    =			rouge_evaluator.get_scores(UpperCamelCase__  ,     UpperCamelCase__       )
    _a						: int    =			format_rouge_scores(UpperCamelCase__       )
    save_rouge_scores(UpperCamelCase__       )
    print(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  for summary, document_name in zip(UpperCamelCase__  ,     UpperCamelCase__       ):
    # Prepare the summary file's name
    if "." in document_name:
      _a						: List[str]    =			""".""".join(document_name.split("""."""       )[:-1]       )
      _a						: Optional[int]    =			document_name.split("""."""       )[-1]
      _a						: Optional[int]    =			bare_document_name + """_summary.""" + extension
    else:
      _a						: Optional[int]    =			document_name + """_summary"""

    _a						: Dict    =			os.path.join(UpperCamelCase__  ,     UpperCamelCase__       )
    with open(UpperCamelCase__  ,     """w"""       ) as output:
      output.write(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Any    =			translation
  _a						: Dict    =			(
      raw_summary.replace("""[unused0]"""  ,     """"""       )
      .replace("""[unused3]"""  ,     """"""       )
      .replace("""[PAD]"""  ,     """"""       )
      .replace("""[unused1]"""  ,     """"""       )
      .replace(R""" +"""  ,     """ """       )
      .replace(""" [unused2] """  ,     """. """       )
      .replace("""[unused2]"""  ,     """"""       )
      .strip()
  )

  return summary

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
      scores["""rouge-1"""]["""f"""]  ,     scores["""rouge-1"""]["""p"""]  ,     scores["""rouge-1"""]["""r"""]  ,     scores["""rouge-2"""]["""f"""]  ,     scores["""rouge-2"""]["""p"""]  ,     scores["""rouge-2"""]["""r"""]  ,     scores["""rouge-l"""]["""f"""]  ,     scores["""rouge-l"""]["""p"""]  ,     scores["""rouge-l"""]["""r"""]  ,     )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  with open("""rouge_scores.txt"""  ,     """w"""       ) as output:
    output.write(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Any    =			load_and_cache_examples(UpperCamelCase__  ,     UpperCamelCase__       )
  _a						: List[Any]    =			SequentialSampler(UpperCamelCase__       )

  def collate_fn(UpperCamelCase__       ):
    return collate(UpperCamelCase__  ,     UpperCamelCase__  ,     block_size=5_1_2  ,     device=args.device       )

  _a						: List[str]    =			DataLoader(
      UpperCamelCase__  ,     sampler=UpperCamelCase__  ,     batch_size=args.batch_size  ,     collate_fn=UpperCamelCase__  ,     )

  return iterator

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			CNNDMDataset(args.documents_dir       )
  return dataset

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: str    =			[x for x in data if not len(x[1]       ) == 0]  # remove empty_files
  _a						: Any    =			[name for name, _, _ in data]
  _a						: List[Any]    =			[""" """.join(UpperCamelCase__       ) for _, _, summary_list in data]

  _a						: str    =			[encode_for_summarization(UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ) for _, story, summary in data]
  _a						: str    =			torch.tensor(
      [truncate_or_pad(UpperCamelCase__  ,     UpperCamelCase__  ,     tokenizer.pad_token_id       ) for story, _ in encoded_text]       )
  _a						: List[str]    =			compute_token_type_ids(UpperCamelCase__  ,     tokenizer.cls_token_id       )
  _a						: int    =			build_mask(UpperCamelCase__  ,     tokenizer.pad_token_id       )

  _a						: Tuple    =			Batch(
      document_names=UpperCamelCase__  ,     batch_size=len(UpperCamelCase__       )  ,     src=encoded_stories.to(UpperCamelCase__       )  ,     segs=encoder_token_type_ids.to(UpperCamelCase__       )  ,     mask_src=encoder_mask.to(UpperCamelCase__       )  ,     tgt_str=UpperCamelCase__  ,     )

  return batch

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			summary_tokens.to("""cpu"""       ).numpy()
  _a						: List[Any]    =			tokenizer.decode(UpperCamelCase__       )
  _a						: Dict    =			summary.split("""."""       )
  _a						: List[Any]    =			[s + """.""" for s in sentences]
  return sentences

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Dict    =			argparse.ArgumentParser()
  parser.add_argument(
      """--documents_dir"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""The folder where the documents to summarize are located."""  ,     )
  parser.add_argument(
      """--summaries_output_dir"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are"""  ,     )
  parser.add_argument(
      """--compute_rouge"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     required=UpperCamelCase__  ,     help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."""  ,     )
  # EVALUATION options
  parser.add_argument(
      """--no_cuda"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     help="""Whether to force the execution on CPU."""  ,     )
  parser.add_argument(
      """--batch_size"""  ,     default=4  ,     type=UpperCamelCase__  ,     help="""Batch size per GPU/CPU for training."""  ,     )
  # BEAM SEARCH arguments
  parser.add_argument(
      """--min_length"""  ,     default=5_0  ,     type=UpperCamelCase__  ,     help="""Minimum number of tokens for the summaries."""  ,     )
  parser.add_argument(
      """--max_length"""  ,     default=2_0_0  ,     type=UpperCamelCase__  ,     help="""Maixmum number of tokens for the summaries."""  ,     )
  parser.add_argument(
      """--beam_size"""  ,     default=5  ,     type=UpperCamelCase__  ,     help="""The number of beams to start with for each example."""  ,     )
  parser.add_argument(
      """--alpha"""  ,     default=0.95  ,     type=UpperCamelCase__  ,     help="""The value of alpha for the length penalty in the beam search."""  ,     )
  parser.add_argument(
      """--block_trigram"""  ,     default=UpperCamelCase__  ,     type=UpperCamelCase__  ,     help="""Whether to block the existence of repeating trigrams in the text generated by beam search."""  ,     )
  _a						: Optional[int]    =			parser.parse_args()

  # Select device (distibuted not available)
  _a						: Tuple    =			torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""       )

  # Check the existence of directories
  if not args.summaries_output_dir:
    _a						: Dict    =			args.documents_dir

  if not documents_dir_is_valid(args.documents_dir       ):
    raise FileNotFoundError(
        """We could not find the directory you specified for the documents to summarize, or it was empty. Please"""
        """ specify a valid path."""       )
  os.makedirs(args.summaries_output_dir  ,     exist_ok=UpperCamelCase__       )

  evaluate(UpperCamelCase__       )

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if not os.path.exists(UpperCamelCase__       ):
    return False

  _a						: Union[str, Any]    =			os.listdir(UpperCamelCase__       )
  if len(UpperCamelCase__       ) == 0:
    return False

  return True


if __name__ == "__main__":
 main()









"""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)

