




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,
)


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


_lowerCamelCase					:     int					  =  namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])




def 							_UpperCAmelCase     (UpperCamelCase_       :       int				):
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[int]        =					BertTokenizer.from_pretrained("""bert-base-uncased"""					,			do_lower_case=UpperCamelCase_				)
				_lowerCAmelCase			:			Dict        =					BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""				)
				model.to(args.device				)
				model.eval()

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

				if args.compute_rouge:
								_lowerCAmelCase			:			str        =					[]
								_lowerCAmelCase			:			str        =					[]

								import nltk
								import rouge

								nltk.download("""punkt"""				)
								_lowerCAmelCase			:			Union[str, Any]        =					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.
				_lowerCAmelCase			:			Tuple        =					""""""
				_lowerCAmelCase			:			Dict        =					""""""

				_lowerCAmelCase			:			Tuple        =					build_data_iterator(UpperCamelCase_					,			UpperCamelCase_				)
				_lowerCAmelCase			:			Dict        =					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_				):
								_lowerCAmelCase			:			Any        =					predictor.translate_batch(UpperCamelCase_				)
								_lowerCAmelCase			:			int        =					predictor.from_batch(UpperCamelCase_				)
								_lowerCAmelCase			:			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:
								_lowerCAmelCase			:			Any        =					rouge_evaluator.get_scores(UpperCamelCase_					,			UpperCamelCase_				)
								_lowerCAmelCase			:			int        =					format_rouge_scores(UpperCamelCase_				)
								save_rouge_scores(UpperCamelCase_				)
								print(UpperCamelCase_				)




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

								_lowerCAmelCase			:			List[str]        =					os.path.join(UpperCamelCase_					,			UpperCamelCase_				)
								with open(UpperCamelCase_					,			"""w"""				) as output:
												output.write(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]				):
				'''simple docstring'''
				_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					translation
				_lowerCAmelCase			:			Any        =					(
				    raw_summary.replace("""[unused0]"""					,			""""""				)
				    .replace("""[unused3]"""					,			""""""				)
				    .replace("""[PAD]"""					,			""""""				)
				    .replace("""[unused1]"""					,			""""""				)
				    .replace(R""" +"""					,			""" """				)
				    .replace(""" [unused2] """					,			""". """				)
				    .replace("""[unused2]"""					,			""""""				)
				    .strip()
				)

				return summary




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[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     (UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				with open("""rouge_scores.txt"""					,			"""w"""				) as output:
								output.write(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			Any        =					load_and_cache_examples(UpperCamelCase_					,			UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					SequentialSampler(UpperCamelCase_				)

				def collate_fn(UpperCamelCase_       :       Optional[int]				):
								return collate(UpperCamelCase_					,			UpperCamelCase_					,			block_size=512					,			device=args.device				)

				_lowerCAmelCase			:			Optional[int]        =					DataLoader(
				    UpperCamelCase_					,			sampler=UpperCamelCase_					,			batch_size=args.batch_size					,			collate_fn=UpperCamelCase_					,			)

				return iterator




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       str				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					CNNDMDataset(args.documents_dir				)
				return dataset




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[int]					,			UpperCamelCase_       :       str					,			UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			int        =					[x for x in data if not len(x[1]				) == 0]  # remove empty_files
				_lowerCAmelCase			:			Any        =					[name for name, _, _ in data]
				_lowerCAmelCase			:			Tuple        =					[""" """.join(UpperCamelCase_				) for _, _, summary_list in data]

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

				_lowerCAmelCase			:			Dict        =					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 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					summary_tokens.to("""cpu"""				).numpy()
				_lowerCAmelCase			:			List[str]        =					tokenizer.decode(UpperCamelCase_				)
				_lowerCAmelCase			:			Optional[Any]        =					summary.split("""."""				)
				_lowerCAmelCase			:			Optional[int]        =					[s + """.""" for s in sentences]
				return sentences




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Tuple        =					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."""					,			)
				_lowerCAmelCase			:			str        =					parser.parse_args()

				# Select device (distibuted not available)
				_lowerCAmelCase			:			List[Any]        =					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			:			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=UpperCamelCase_				)

				evaluate(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				if not os.path.exists(UpperCamelCase_				):
								return False

				_lowerCAmelCase			:			Optional[int]        =					os.listdir(UpperCamelCase_				)
				if len(UpperCamelCase_				) == 0:
								return False

				return True


if __name__ == "__main__":
	main()



"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		assert (
		    isinstance(a_,						a_			) and mask > 0
		), F"""mask needs to be positive integer, your input {mask}"""
		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			Tuple		=    mask

		while submask:
				all_submasks.append(a_			)
				lowerCamelCase     :			Optional[int]		=    (submask - 1) & mask

		return all_submasks


if __name__ == "__main__":
					import doctest

					doctest.testmod()






