



"""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     UpperCAmelCase    (					_lowercase   :      str  )	->   Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = BertTokenizer.from_pretrained('''bert-base-uncased''' ,      do_lower_case=lowercase_  )
	lowerCAmelCase_			       = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''  )
	model.to(args.device  )
	model.eval()

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

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

		import nltk
		import rouge

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

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

	lowerCAmelCase_			       = build_data_iterator(lowercase_ ,      lowercase_  )
	lowerCAmelCase_			       = build_predictor(lowercase_ ,      lowercase_ ,      lowercase_ ,      lowercase_  )

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

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

	if args.compute_rouge:
		lowerCAmelCase_			       = rouge_evaluator.get_scores(lowercase_ ,      lowercase_  )
		lowerCAmelCase_			       = format_rouge_scores(lowercase_  )
		save_rouge_scores(lowercase_  )
		print(lowercase_  )
def     UpperCAmelCase    (					_lowercase   :      List[Any] ,      _lowercase   :      Union[str, Any] ,      _lowercase   :      List[str]  )	->   List[str]:


	"""simple docstring"""




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

		lowerCAmelCase_			       = os.path.join(lowercase_ ,      lowercase_  )
		with open(lowercase_ ,      '''w'''  ) as output:
			output.write(lowercase_  )
def     UpperCAmelCase    (					_lowercase   :      Tuple  )	->   Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = translation
	lowerCAmelCase_			       = (
	    raw_summary.replace('''[unused0]''' ,      ''''''  )
	    .replace('''[unused3]''' ,      ''''''  )
	    .replace('''[PAD]''' ,      ''''''  )
	    .replace('''[unused1]''' ,      ''''''  )
	    .replace(r''' +''' ,      ''' '''  )
	    .replace(''' [unused2] ''' ,      '''. '''  )
	    .replace('''[unused2]''' ,      ''''''  )
	    .strip()
	)

	return summary
def     UpperCAmelCase    (					_lowercase   :      Any  )	->   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     UpperCAmelCase    (					_lowercase   :      int  )	->   Tuple:


	"""simple docstring"""




	with open('''rouge_scores.txt''' ,      '''w'''  ) as output:
		output.write(lowercase_  )
def     UpperCAmelCase    (					_lowercase   :      List[Any] ,      _lowercase   :      str  )	->   List[Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = load_and_cache_examples(lowercase_ ,      lowercase_  )
	lowerCAmelCase_			       = SequentialSampler(lowercase_  )

	def collate_fn(_lowercase   :      Optional[int]  ):
		return collate(lowercase_ ,      lowercase_ ,      block_size=5_1_2 ,      device=args.device  )

	lowerCAmelCase_			       = DataLoader(
	    lowercase_ ,      sampler=lowercase_ ,      batch_size=args.batch_size ,      collate_fn=lowercase_ ,      )

	return iterator
def     UpperCAmelCase    (					_lowercase   :      Optional[Any] ,      _lowercase   :      int  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = CNNDMDataset(args.documents_dir  )
	return dataset
def     UpperCAmelCase    (					_lowercase   :      List[str] ,      _lowercase   :      Any ,      _lowercase   :      int ,      _lowercase   :      int  )	->   Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = [x for x in data if not len(x[1]  ) == 0]  # remove empty_files
	lowerCAmelCase_			       = [name for name, _, _ in data]
	lowerCAmelCase_			       = [' '.join(lowercase_  ) for _, _, summary_list in data]

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

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

	return batch
def     UpperCAmelCase    (					_lowercase   :      Optional[int] ,      _lowercase   :      Any  )	->   List[Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = summary_tokens.to('''cpu'''  ).numpy()
	lowerCAmelCase_			       = tokenizer.decode(lowercase_  )
	lowerCAmelCase_			       = summary.split('''.'''  )
	lowerCAmelCase_			       = [s + '.' for s in sentences]
	return sentences
def     UpperCAmelCase    (					)	->   Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_			       = argparse.ArgumentParser()
	parser.add_argument(
	    '''--documents_dir''' ,      default=lowercase_ ,      type=lowercase_ ,      required=lowercase_ ,      help='''The folder where the documents to summarize are located.''' ,      )
	parser.add_argument(
	    '''--summaries_output_dir''' ,      default=lowercase_ ,      type=lowercase_ ,      required=lowercase_ ,      help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''' ,      )
	parser.add_argument(
	    '''--compute_rouge''' ,      default=lowercase_ ,      type=lowercase_ ,      required=lowercase_ ,      help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''' ,      )
	# EVALUATION options
	parser.add_argument(
	    '''--no_cuda''' ,      default=lowercase_ ,      type=lowercase_ ,      help='''Whether to force the execution on CPU.''' ,      )
	parser.add_argument(
	    '''--batch_size''' ,      default=4 ,      type=lowercase_ ,      help='''Batch size per GPU/CPU for training.''' ,      )
	# BEAM SEARCH arguments
	parser.add_argument(
	    '''--min_length''' ,      default=5_0 ,      type=lowercase_ ,      help='''Minimum number of tokens for the summaries.''' ,      )
	parser.add_argument(
	    '''--max_length''' ,      default=2_0_0 ,      type=lowercase_ ,      help='''Maixmum number of tokens for the summaries.''' ,      )
	parser.add_argument(
	    '''--beam_size''' ,      default=5 ,      type=lowercase_ ,      help='''The number of beams to start with for each example.''' ,      )
	parser.add_argument(
	    '''--alpha''' ,      default=0.9_5 ,      type=lowercase_ ,      help='''The value of alpha for the length penalty in the beam search.''' ,      )
	parser.add_argument(
	    '''--block_trigram''' ,      default=lowercase_ ,      type=lowercase_ ,      help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''' ,      )
	lowerCAmelCase_			       = parser.parse_args()

	# Select device (distibuted not available)
	lowerCAmelCase_			       = 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:
		lowerCAmelCase_			       = 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=lowercase_  )

	evaluate(lowercase_  )
def     UpperCAmelCase    (					_lowercase   :      Dict  )	->   List[Any]:


	"""simple docstring"""




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

	lowerCAmelCase_			       = os.listdir(lowercase_  )
	if len(lowercase_  ) == 0:
		return False

	return True


if __name__ == "__main__":
		main()



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							 =       {'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       ['PegasusTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       ['PegasusTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PegasusForCausalLM',
		    'PegasusForConditionalGeneration',
		    'PegasusModel',
		    'PegasusPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'TFPegasusForConditionalGeneration',
		    'TFPegasusModel',
		    'TFPegasusPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'FlaxPegasusForConditionalGeneration',
		    'FlaxPegasusModel',
		    'FlaxPegasusPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		lowercase_							 =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)