






'''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,
)


__A   :							Optional[int]     	=						logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


__A   :							Union[str, Any]     	=						namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   BertTokenizer.from_pretrained("bert-base-uncased"				,      do_lower_case=snake_case__				)
      __A		 =   BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"				)
      model.to(args.device				)
      model.eval()

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

      if args.compute_rouge:
            __A		 =   []
            __A		 =   []

            import nltk
            import rouge

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

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      __A		 =   ""
      __A		 =   ""

      __A		 =   build_data_iterator(snake_case__				,      snake_case__				)
      __A		 =   build_predictor(snake_case__				,      snake_case__				,      snake_case__				,      snake_case__				)

      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(snake_case__				):
            __A		 =   predictor.translate_batch(snake_case__				)
            __A		 =   predictor.from_batch(snake_case__				)
            __A		 =   [format_summary(snake_case__				) for t in translations]
            save_summaries(snake_case__				,      args.summaries_output_dir				,      batch.document_names				)

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

      if args.compute_rouge:
            __A		 =   rouge_evaluator.get_scores(snake_case__				,      snake_case__				)
            __A		 =   format_rouge_scores(snake_case__				)
            save_rouge_scores(snake_case__				)
            print(snake_case__				)


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      for summary, document_name in zip(snake_case__				,      snake_case__				):
            # Prepare the summary file's name
            if "." in document_name:
                  __A		 =   ".".join(document_name.split("."				)[:-1]				)
                  __A		 =   document_name.split("."				)[-1]
                  __A		 =   bare_document_name + "_summary." + extension
            else:
                  __A		 =   document_name + "_summary"

            __A		 =   os.path.join(snake_case__				,      snake_case__				)
            with open(snake_case__				,      "w"				) as output:
                  output.write(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      __A ,					__A ,					__A		 =   translation
      __A		 =   (
          raw_summary.replace("[unused0]"				,      ""				)
          .replace("[unused3]"				,      ""				)
          .replace("[PAD]"				,      ""				)
          .replace("[unused1]"				,      ""				)
          .replace(r" +"				,      " "				)
          .replace(" [unused2] "				,      ". "				)
          .replace("[unused2]"				,      ""				)
          .strip()
      )

      return summary


def     _UpperCamelCase				(  snake_case__				):
      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     _UpperCamelCase				(  snake_case__				):
      with open("rouge_scores.txt"				,      "w"				) as output:
            output.write(snake_case__				)


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   load_and_cache_examples(snake_case__				,      snake_case__				)
      __A		 =   SequentialSampler(snake_case__				)

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

      __A		 =   DataLoader(
          snake_case__				,      sampler=snake_case__				,      batch_size=args.batch_size				,      collate_fn=snake_case__				,      )

      return iterator


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   CNNDMDataset(args.documents_dir				)
      return dataset


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   [x for x in data if not len(x[1]				) == 0]  # remove empty_files
      __A		 =   [name for name, _, _ in data]
      __A		 =   [" ".join(snake_case__				) for _, _, summary_list in data]

      __A		 =   [encode_for_summarization(snake_case__				,      snake_case__				,      snake_case__				) for _, story, summary in data]
      __A		 =   torch.tensor(
          [truncate_or_pad(snake_case__				,      snake_case__				,      tokenizer.pad_token_id				) for story, _ in encoded_text]				)
      __A		 =   compute_token_type_ids(snake_case__				,      tokenizer.cls_token_id				)
      __A		 =   build_mask(snake_case__				,      tokenizer.pad_token_id				)

      __A		 =   Batch(
          document_names=snake_case__				,      batch_size=len(snake_case__				)				,      src=encoded_stories.to(snake_case__				)				,      segs=encoder_token_type_ids.to(snake_case__				)				,      mask_src=encoder_mask.to(snake_case__				)				,      tgt_str=snake_case__				,      )

      return batch


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      __A		 =   summary_tokens.to("cpu"				).numpy()
      __A		 =   tokenizer.decode(snake_case__				)
      __A		 =   summary.split("."				)
      __A		 =   [s + "." for s in sentences]
      return sentences


def     _UpperCamelCase				(  ):
      __A		 =   argparse.ArgumentParser()
      parser.add_argument(
          "--documents_dir"				,      default=snake_case__				,      type=snake_case__				,      required=snake_case__				,      help="The folder where the documents to summarize are located."				,      )
      parser.add_argument(
          "--summaries_output_dir"				,      default=snake_case__				,      type=snake_case__				,      required=snake_case__				,      help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"				,      )
      parser.add_argument(
          "--compute_rouge"				,      default=snake_case__				,      type=snake_case__				,      required=snake_case__				,      help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."				,      )
      # EVALUATION options
      parser.add_argument(
          "--no_cuda"				,      default=snake_case__				,      type=snake_case__				,      help="Whether to force the execution on CPU."				,      )
      parser.add_argument(
          "--batch_size"				,      default=4				,      type=snake_case__				,      help="Batch size per GPU/CPU for training."				,      )
      # BEAM SEARCH arguments
      parser.add_argument(
          "--min_length"				,      default=50				,      type=snake_case__				,      help="Minimum number of tokens for the summaries."				,      )
      parser.add_argument(
          "--max_length"				,      default=200				,      type=snake_case__				,      help="Maixmum number of tokens for the summaries."				,      )
      parser.add_argument(
          "--beam_size"				,      default=5				,      type=snake_case__				,      help="The number of beams to start with for each example."				,      )
      parser.add_argument(
          "--alpha"				,      default=0.95				,      type=snake_case__				,      help="The value of alpha for the length penalty in the beam search."				,      )
      parser.add_argument(
          "--block_trigram"				,      default=snake_case__				,      type=snake_case__				,      help="Whether to block the existence of repeating trigrams in the text generated by beam search."				,      )
      __A		 =   parser.parse_args()

      # Select device (distibuted not available)
      __A		 =   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		 =   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=snake_case__				)

      evaluate(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      if not os.path.exists(snake_case__				):
            return False

      __A		 =   os.listdir(snake_case__				)
      if len(snake_case__				) == 0:
            return False

      return True


if __name__ == "__main__":
       main()










'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						int       )	->							Optional[int]:
           __A		 =   inspect.getfile(accelerate.test_utils       )
           __A		 =   os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "test_sync.py"]       )






     @require_cpu
     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           debug_launcher(test_sync.main					,					num_processes=1       )






     @require_cpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							Optional[Any]:
           debug_launcher(test_sync.main       )






     @require_single_gpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							List[str]:
           test_sync.main()






     @require_multi_gpu
     def      _UpperCamelCase (self     :						Optional[int]       )	->							List[str]:
           print(f"Found {torch.cuda.device_count()} devices."       )
           __A		 =   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
           with patch_environment(omp_num_threads=1       ):
                 execute_subprocess_async(lowercase__					,					env=os.environ.copy()       )


