

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]		):




      """simple docstring"""





      a		:Optional[Any]											= [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase_				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:List[Any]											= emb.weight.shape
      a		:Any											= nn.Linear(UpperCAmelCase_				,						UpperCAmelCase_				,						bias=UpperCAmelCase_		)
      a		:str											= emb.weight.data
      return lin_layer
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Tuple="uclanlp/plbart-base"				,						UpperCAmelCase_		:	Tuple=False				,						UpperCAmelCase_		:	Union[str, Any]=False		):




      """simple docstring"""





      a		:Any											= torch.load(UpperCAmelCase_				,						map_location='''cpu'''		)["model"]
      remove_ignore_keys_(UpperCAmelCase_		)
      a		:Union[str, Any]											= state_dict["encoder.embed_tokens.weight"].shape[0]

      a		:str											= PLBartConfig.from_pretrained(UpperCAmelCase_				,						vocab_size=UpperCAmelCase_		)

      a		:int											= state_dict["decoder.embed_tokens.weight"]
      if not classification:
            a		:Optional[Any]											= PLBartForConditionalGeneration(UpperCAmelCase_		)
            model.model.load_state_dict(UpperCAmelCase_		)
            if finetuned:
                  a		:Optional[int]											= make_linear_from_emb(model.model.shared		)

      else:
            a		:int											= {}
            for key, value in state_dict.copy().items():
                  if key.startswith('''classification_heads.sentence_classification_head'''		):
                        a		:Any											= value
                        state_dict.pop(UpperCAmelCase_		)
            a		:Dict											= PLBartForSequenceClassification(UpperCAmelCase_		)
            model.model.load_state_dict(UpperCAmelCase_		)
            model.classification_head.load_state_dict(UpperCAmelCase_		)

      return model


if __name__ == "__main__":
     snake_case       :					List[str]        =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''fairseq_path''', type=str, help='''model.pt on local filesystem.''')
     parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument(
         '''--hf_config''',
         default='''uclanlp/plbart-base''',
         type=str,
         help='''Which huggingface architecture to use: plbart-base''',
     )
     parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
     parser.add_argument(
         '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
     )
     snake_case       :					Union[str, Any]        =  parser.parse_args()
     snake_case       :					List[Any]        =  convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)









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       :					Union[str, Any]        =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case       :					int        =  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:Optional[Any]											= BertTokenizer.from_pretrained('''bert-base-uncased'''				,						do_lower_case=UpperCAmelCase_		)
      a		:int											= BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''		)
      model.to(args.device		)
      model.eval()

      a		:Dict											= {
          '''BOS''': tokenizer.vocab['''[unused0]'''],
          '''EOS''': tokenizer.vocab['''[unused1]'''],
          '''PAD''': tokenizer.vocab['''[PAD]'''],
      }

      if args.compute_rouge:
            a		:Dict											= []
            a		:List[Any]											= []

            import nltk
            import rouge

            nltk.download('''punkt'''		)
            a		:Tuple											= 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		:Union[str, Any]											= ''''''
      a		:List[Any]											= ''''''

      a		:Optional[int]											= build_data_iterator(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Optional[Any]											= 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		:Any											= predictor.translate_batch(UpperCAmelCase_		)
            a		:List[Any]											= predictor.from_batch(UpperCAmelCase_		)
            a		:Tuple											= [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		:Dict											= format_rouge_scores(UpperCAmelCase_		)
            save_rouge_scores(UpperCAmelCase_		)
            print(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Dict		):




      """simple docstring"""





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

            a		:Dict											= os.path.join(UpperCAmelCase_				,						UpperCAmelCase_		)
            with open(UpperCAmelCase_				,						'''w'''		) as output:
                  output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= translation
      a		:int											= (
          raw_summary.replace('''[unused0]'''				,						''''''		)
          .replace('''[unused3]'''				,						''''''		)
          .replace('''[PAD]'''				,						''''''		)
          .replace('''[unused1]'''				,						''''''		)
          .replace(R''' +'''				,						''' '''		)
          .replace(''' [unused2] '''				,						'''. '''		)
          .replace('''[unused2]'''				,						''''''		)
          .strip()
      )

      return summary
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """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_		:	int		):




      """simple docstring"""





      with open('''rouge_scores.txt'''				,						'''w'''		) as output:
            output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





      a		:Any											= load_and_cache_examples(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Union[str, Any]											= SequentialSampler(UpperCAmelCase_		)

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

      a		:List[Any]											= DataLoader(
          UpperCAmelCase_				,						sampler=UpperCAmelCase_				,						batch_size=args.batch_size				,						collate_fn=UpperCAmelCase_				,						)

      return iterator
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Union[str, Any]											= CNNDMDataset(args.documents_dir		)
      return dataset
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:Optional[int]											= [x for x in data if not len(x[1]		) == 0]  # remove empty_files
      a		:Tuple											= [name for name, _, _ in data]
      a		:str											= [''' '''.join(UpperCAmelCase_		) for _, _, summary_list in data]

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

      a		:List[Any]											= 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_		:	Union[str, Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:str											= summary_tokens.to('''cpu'''		).numpy()
      a		:Union[str, Any]											= tokenizer.decode(UpperCAmelCase_		)
      a		:Optional[Any]											= summary.split('''.'''		)
      a		: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=50				,						type=UpperCAmelCase_				,						help='''Minimum number of tokens for the summaries.'''				,						)
      parser.add_argument(
          '''--max_length'''				,						default=200				,						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		:List[str]											= parser.parse_args()

      # Select device (distibuted not available)
      a		:Optional[int]											= 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		:Union[str, Any]											= 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_		:	List[str]		):




      """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()





