





'''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,
)


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


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







def 				_a	(					_lowerCamelCase   )   ->							Union[str, Any]:



			"""simple docstring"""


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

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

			if args.compute_rouge:
						__snake_case				:			Optional[Any]       			=    []
						__snake_case				:			Optional[int]       			=    []

						import nltk
						import rouge

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

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

			__snake_case				:			Tuple       			=    build_data_iterator(_lowerCamelCase						,  _lowerCamelCase   )
			__snake_case				:			List[str]       			=    build_predictor(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   )

			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(_lowerCamelCase   ):
						__snake_case				:			Union[str, Any]       			=    predictor.translate_batch(_lowerCamelCase   )
						__snake_case				:			str       			=    predictor.from_batch(_lowerCamelCase   )
						__snake_case				:			Optional[int]       			=    [format_summary(_lowerCamelCase   ) for t in translations]
						save_summaries(_lowerCamelCase						,  args.summaries_output_dir						,  batch.document_names   )

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

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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   )   ->							Optional[int]:



			"""simple docstring"""


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

						__snake_case				:			Optional[int]       			=    os.path.join(_lowerCamelCase						,  _lowerCamelCase   )
						with open(_lowerCamelCase						,  """w"""   ) as output:
									output.write(_lowerCamelCase   )







def 				_a	(					_lowerCamelCase   )   ->							str:



			"""simple docstring"""


			__snake_case			,    __snake_case			,    __snake_case				:			str       			=    translation
			__snake_case				:			Optional[Any]       			=    (
			    raw_summary.replace("""[unused0]"""						,  """"""   )
			    .replace("""[unused3]"""						,  """"""   )
			    .replace("""[PAD]"""						,  """"""   )
			    .replace("""[unused1]"""						,  """"""   )
			    .replace(R""" +"""						,  """ """   )
			    .replace(""" [unused2] """						,  """. """   )
			    .replace("""[unused2]"""						,  """"""   )
			    .strip()
			)

			return summary







def 				_a	(					_lowerCamelCase   )   ->							Dict:



			"""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 				_a	(					_lowerCamelCase   )   ->							Union[str, Any]:



			"""simple docstring"""


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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							int:



			"""simple docstring"""


			__snake_case				:			str       			=    load_and_cache_examples(_lowerCamelCase						,  _lowerCamelCase   )
			__snake_case				:			Dict       			=    SequentialSampler(_lowerCamelCase   )

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

			__snake_case				:			List[Any]       			=    DataLoader(
			    _lowerCamelCase						,  sampler=_lowerCamelCase						,  batch_size=args.batch_size						,  collate_fn=_lowerCamelCase						,  )

			return iterator







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							int:



			"""simple docstring"""


			__snake_case				:			List[str]       			=    CNNDMDataset(args.documents_dir   )
			return dataset







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   )   ->							str:



			"""simple docstring"""


			__snake_case				:			Dict       			=    [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
			__snake_case				:			Optional[int]       			=    [name for name, _, _ in data]
			__snake_case				:			Optional[int]       			=    [""" """.join(_lowerCamelCase   ) for _, _, summary_list in data]

			__snake_case				:			Any       			=    [encode_for_summarization(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   ) for _, story, summary in data]
			__snake_case				:			Dict       			=    torch.tensor(
			    [truncate_or_pad(_lowerCamelCase						,  _lowerCamelCase						,  tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
			__snake_case				:			Optional[int]       			=    compute_token_type_ids(_lowerCamelCase						,  tokenizer.cls_token_id   )
			__snake_case				:			Optional[int]       			=    build_mask(_lowerCamelCase						,  tokenizer.pad_token_id   )

			__snake_case				:			Optional[int]       			=    Batch(
			    document_names=_lowerCamelCase						,  batch_size=len(_lowerCamelCase   )						,  src=encoded_stories.to(_lowerCamelCase   )						,  segs=encoder_token_type_ids.to(_lowerCamelCase   )						,  mask_src=encoder_mask.to(_lowerCamelCase   )						,  tgt_str=_lowerCamelCase						,  )

			return batch







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							str:



			"""simple docstring"""


			__snake_case				:			Optional[int]       			=    summary_tokens.to("""cpu"""   ).numpy()
			__snake_case				:			Optional[Any]       			=    tokenizer.decode(_lowerCamelCase   )
			__snake_case				:			List[Any]       			=    summary.split("""."""   )
			__snake_case				:			Any       			=    [s + """.""" for s in sentences]
			return sentences







def 				_a	(					)   ->							int:



			"""simple docstring"""


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

			# Select device (distibuted not available)
			__snake_case				:			str       			=    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:
						__snake_case				:			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=_lowerCamelCase   )

			evaluate(_lowerCamelCase   )







def 				_a	(					_lowerCamelCase   )   ->							Any:



			"""simple docstring"""


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

			__snake_case				:			Optional[Any]       			=    os.listdir(_lowerCamelCase   )
			if len(_lowerCamelCase   ) == 0:
						return False

			return True


if __name__ == "__main__":
	main()









'''simple docstring'''






import torch
from torch import autograd




class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[Any]     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float     ,			__magic_name__				:      bool )				->						Optional[int]:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    inputs.numel()
					__snake_case				:			List[Any]       			=    int(0.005 * nb_elems ) + 1
					if sigmoid:
								__snake_case				:			Dict       			=    (torch.sigmoid(__magic_name__ ) > threshold).type(inputs.type() )
					else:
								__snake_case				:			Tuple       			=    (inputs > threshold).type(inputs.type() )
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__snake_case				:			Tuple       			=    inputs.flatten().kthvalue(max(nb_elems - nb_min     ,			1 ) ).values
								__snake_case				:			Union[str, Any]       			=    (inputs > k_threshold).type(inputs.type() )
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      int     ,			__magic_name__				:      Any )				->						Tuple:


					"""simple docstring"""

					return gradOutput, None, None





class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      Tuple     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						int:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Dict       			=    inputs.flatten().sort(descending=__magic_name__ )
					__snake_case				:			List[Any]       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Optional[Any]       			=    mask.flatten()
					__snake_case				:			List[str]       			=    0
					__snake_case				:			Any       			=    1
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[str]     ,			__magic_name__				:      Any )				->						Optional[Any]:


					"""simple docstring"""

					return gradOutput, None





class    _A   (				__lowercase		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Tuple       			=    inputs.abs().flatten().sort(descending=__magic_name__ )
					__snake_case				:			Any       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Union[str, Any]       			=    mask.flatten()
					__snake_case				:			Tuple       			=    0
					__snake_case				:			int       			=    1
					return mask


