






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


_a				     =  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])


def __A   (				__lowerCAmelCase     )->  Dict:





						"""simple docstring"""



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

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

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

												import nltk
												import rouge

												nltk.download('punkt'     )
												_UpperCAmelCase     =      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.
						_UpperCAmelCase     =      ''
						_UpperCAmelCase     =      ''

						_UpperCAmelCase     =      build_data_iterator(UpperCamelCase_			,		UpperCamelCase_     )
						_UpperCAmelCase     =      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_     ):
												_UpperCAmelCase     =      predictor.translate_batch(UpperCamelCase_     )
												_UpperCAmelCase     =      predictor.from_batch(UpperCamelCase_     )
												_UpperCAmelCase     =      [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:
												_UpperCAmelCase     =      rouge_evaluator.get_scores(UpperCamelCase_			,		UpperCamelCase_     )
												_UpperCAmelCase     =      format_rouge_scores(UpperCamelCase_     )
												save_rouge_scores(UpperCamelCase_     )
												print(UpperCamelCase_     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  str:





						"""simple docstring"""



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

												_UpperCAmelCase     =      os.path.join(UpperCamelCase_			,		UpperCamelCase_     )
												with open(UpperCamelCase_			,		'w'     ) as output:
																		output.write(UpperCamelCase_     )


def __A   (				__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



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

						return summary


def __A   (				__lowerCAmelCase     )->  List[str]:





						"""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(UpperCamelCase_     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      load_and_cache_examples(UpperCamelCase_			,		UpperCamelCase_     )
						_UpperCAmelCase     =      SequentialSampler(UpperCamelCase_     )

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

						_UpperCAmelCase     =      DataLoader(
						    UpperCamelCase_			,		sampler=UpperCamelCase_			,		batch_size=args.batch_size			,		collate_fn=UpperCamelCase_			,		)

						return iterator


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  str:





						"""simple docstring"""



						_UpperCAmelCase     =      CNNDMDataset(args.documents_dir     )
						return dataset


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[str]:





						"""simple docstring"""



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

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

						_UpperCAmelCase     =      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 __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      summary_tokens.to('cpu'     ).numpy()
						_UpperCAmelCase     =      tokenizer.decode(UpperCamelCase_     )
						_UpperCAmelCase     =      summary.split('.'     )
						_UpperCAmelCase     =      [s + '.' for s in sentences]
						return sentences


def __A   (				)->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      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.'			,		)
						_UpperCAmelCase     =      parser.parse_args()

						# Select device (distibuted not available)
						_UpperCAmelCase     =      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:
												_UpperCAmelCase     =      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 __A   (				__lowerCAmelCase     )->  int:





						"""simple docstring"""



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

						_UpperCAmelCase     =      os.listdir(UpperCamelCase_     )
						if len(UpperCamelCase_     ) == 0:
												return False

						return True


if __name__ == "__main__":
		main()








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_a				     =  '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''





class __lowerCamelCase			(				unittest.TestCase					,	snake_case__):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      load_tool('summarization'   )
							self.tool.setup()
							_UpperCAmelCase     =      load_tool('summarization'	,   remote=UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tool(UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'	,   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.remote_tool(UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'	,   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tool(text=UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'	,   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.remote_tool(text=UpperCAmelCase   )
							self.assertEqual(
							    UpperCAmelCase	,   'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'	,   )
