


"""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,
)


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


__lowercase          = namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])


def 	lowercase     (							A_			)-> Tuple:
  '''simple docstring'''







  a		:						Tuple 				=     BertTokenizer.from_pretrained("bert-base-uncased"    ,  do_lower_case=A_			)
  a		:						List[str] 				=     BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"			)
  model.to(args.device			)
  model.eval()

  a		:						Optional[int] 				=     {
      "BOS": tokenizer.vocab["[unused0]"],
      "EOS": tokenizer.vocab["[unused1]"],
      "PAD": tokenizer.vocab["[PAD]"],
  }

  if args.compute_rouge:
    a		:						Union[str, Any] 				=     []
    a		:						List[str] 				=     []

    import nltk
    import rouge

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

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

  a		:						int 				=     build_data_iterator(A_    ,  A_			)
  a		:						Union[str, Any] 				=     build_predictor(A_    ,  A_    ,  A_    ,  A_			)

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

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

  if args.compute_rouge:
    a		:						Optional[Any] 				=     rouge_evaluator.get_scores(A_    ,  A_			)
    a		:						Union[str, Any] 				=     format_rouge_scores(A_			)
    save_rouge_scores(A_			)
    print(A_			)


def 	lowercase     (							A_    ,  A_    ,  A_			)-> List[str]:
  '''simple docstring'''







  for summary, document_name in zip(A_    ,  A_			):
    # Prepare the summary file's name
    if "." in document_name:
      a		:						Optional[int] 				=     ".".join(document_name.split("."			)[:-1]			)
      a		:						Any 				=     document_name.split("."			)[-1]
      a		:						Union[str, Any] 				=     bare_document_name + "_summary." + extension
    else:
      a		:						Tuple 				=     document_name + "_summary"

    a		:						List[Any] 				=     os.path.join(A_    ,  A_			)
    with open(A_    ,  "w"			) as output:
      output.write(A_			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  a				,						a				,						a		:						Dict 				=     translation
  a		:						Tuple 				=     (
      raw_summary.replace("[unused0]"    ,  ""			)
      .replace("[unused3]"    ,  ""			)
      .replace("[PAD]"    ,  ""			)
      .replace("[unused1]"    ,  ""			)
      .replace(R" +"    ,  " "			)
      .replace(" [unused2] "    ,  ". "			)
      .replace("[unused2]"    ,  ""			)
      .strip()
  )

  return summary


def 	lowercase     (							A_			)-> int:
  '''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 	lowercase     (							A_			)-> str:
  '''simple docstring'''







  with open("rouge_scores.txt"    ,  "w"			) as output:
    output.write(A_			)


def 	lowercase     (							A_    ,  A_			)-> int:
  '''simple docstring'''







  a		:						Dict 				=     load_and_cache_examples(A_    ,  A_			)
  a		:						Any 				=     SequentialSampler(A_			)

  def collate_fn(A_			):
    return collate(A_    ,  A_    ,  block_size=512    ,  device=args.device			)

  a		:						List[str] 				=     DataLoader(
      A_    ,  sampler=A_    ,  batch_size=args.batch_size    ,  collate_fn=A_    ,  )

  return iterator


def 	lowercase     (							A_    ,  A_			)-> List[str]:
  '''simple docstring'''







  a		:						Optional[int] 				=     CNNDMDataset(args.documents_dir			)
  return dataset


def 	lowercase     (							A_    ,  A_    ,  A_    ,  A_			)-> Optional[Any]:
  '''simple docstring'''







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

  a		:						Optional[Any] 				=     [encode_for_summarization(A_    ,  A_    ,  A_			) for _, story, summary in data]
  a		:						int 				=     torch.tensor(
      [truncate_or_pad(A_    ,  A_    ,  tokenizer.pad_token_id			) for story, _ in encoded_text]			)
  a		:						Optional[Any] 				=     compute_token_type_ids(A_    ,  tokenizer.cls_token_id			)
  a		:						Union[str, Any] 				=     build_mask(A_    ,  tokenizer.pad_token_id			)

  a		:						Any 				=     Batch(
      document_names=A_    ,  batch_size=len(A_			)    ,  src=encoded_stories.to(A_			)    ,  segs=encoder_token_type_ids.to(A_			)    ,  mask_src=encoder_mask.to(A_			)    ,  tgt_str=A_    ,  )

  return batch


def 	lowercase     (							A_    ,  A_			)-> List[Any]:
  '''simple docstring'''







  a		:						Tuple 				=     summary_tokens.to("cpu"			).numpy()
  a		:						str 				=     tokenizer.decode(A_			)
  a		:						Tuple 				=     summary.split("."			)
  a		:						List[str] 				=     [s + "." for s in sentences]
  return sentences


def 	lowercase     (							)-> Tuple:
  '''simple docstring'''







  a		:						Tuple 				=     argparse.ArgumentParser()
  parser.add_argument(
      "--documents_dir"    ,  default=A_    ,  type=A_    ,  required=A_    ,  help="The folder where the documents to summarize are located."    ,  )
  parser.add_argument(
      "--summaries_output_dir"    ,  default=A_    ,  type=A_    ,  required=A_    ,  help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"    ,  )
  parser.add_argument(
      "--compute_rouge"    ,  default=A_    ,  type=A_    ,  required=A_    ,  help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."    ,  )
  # EVALUATION options
  parser.add_argument(
      "--no_cuda"    ,  default=A_    ,  type=A_    ,  help="Whether to force the execution on CPU."    ,  )
  parser.add_argument(
      "--batch_size"    ,  default=4    ,  type=A_    ,  help="Batch size per GPU/CPU for training."    ,  )
  # BEAM SEARCH arguments
  parser.add_argument(
      "--min_length"    ,  default=50    ,  type=A_    ,  help="Minimum number of tokens for the summaries."    ,  )
  parser.add_argument(
      "--max_length"    ,  default=200    ,  type=A_    ,  help="Maixmum number of tokens for the summaries."    ,  )
  parser.add_argument(
      "--beam_size"    ,  default=5    ,  type=A_    ,  help="The number of beams to start with for each example."    ,  )
  parser.add_argument(
      "--alpha"    ,  default=0.9_5    ,  type=A_    ,  help="The value of alpha for the length penalty in the beam search."    ,  )
  parser.add_argument(
      "--block_trigram"    ,  default=A_    ,  type=A_    ,  help="Whether to block the existence of repeating trigrams in the text generated by beam search."    ,  )
  a		:						Dict 				=     parser.parse_args()

  # Select device (distibuted not available)
  a		:						Optional[Any] 				=     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		:						List[str] 				=     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=A_			)

  evaluate(A_			)


def 	lowercase     (							A_			)-> Optional[Any]:
  '''simple docstring'''







  if not os.path.exists(A_			):
    return False

  a		:						List[Any] 				=     os.listdir(A_			)
  if len(A_			) == 0:
    return False

  return True


if __name__ == "__main__":
 main()










"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowercase          = logging.getLogger(__name__)






@dataclass
class 		_A    :







    """simple docstring"""




    UpperCAmelCase			:				str      =			field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				bool      =			field(default=_a       ,metadata={"""help""": """Set this flag to use fast tokenization."""}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)






def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						Tuple 				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

  if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a				,						a				,						a		:						Any 				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    a				,						a				,						a		:						List[str] 				=     parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir			)
      and os.listdir(training_args.output_dir			)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        " --overwrite_output_dir to overcome."			)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,  )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,  training_args.local_rank    ,  training_args.device    ,  training_args.n_gpu    ,  bool(training_args.local_rank != -1			)    ,  training_args.fpaa    ,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"    ,  A_			)

  # Prepare Question-Answering task
  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a		:						str 				=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  )
  a		:						str 				=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  use_fast=A_    ,  )
  a		:						List[Any] 				=     AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path    ,  from_tf=bool(".ckpt" in model_args.model_name_or_path			)    ,  config=A_    ,  cache_dir=model_args.cache_dir    ,  )

  # Get datasets
  a		:						int 				=     hasattr(model.config    ,  "lang2id"			)
  a		:						Any 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir			)
      if training_args.do_train
      else None
  )
  a		:						Tuple 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  mode="dev"    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir    ,  )
      if training_args.do_eval
      else None
  )

  # Data collator
  a		:						str 				=     DataCollatorWithPadding(A_    ,  pad_to_multiple_of=8			) if training_args.fpaa else None

  # Initialize our Trainer
  a		:						Tuple 				=     Trainer(
      model=A_    ,  args=A_    ,  train_dataset=A_    ,  eval_dataset=A_    ,  data_collator=A_    ,  )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()






