
'''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__				:	List[Any]					=						logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


UpperCAmelCase__				:	Optional[Any]					=						namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])







def 					A       ( UpperCamelCase_   :       str )   ->    Dict:







			'''simple docstring'''



			lowerCAmelCase__									=							BertTokenizer.from_pretrained("bert-base-uncased"  ,		do_lower_case=UpperCamelCase_ )
			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=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__									=							""
			lowerCAmelCase__									=							""

			lowerCAmelCase__									=							build_data_iterator(UpperCamelCase_  ,		UpperCamelCase_ )
			lowerCAmelCase__									=							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__									=							predictor.translate_batch(UpperCamelCase_ )
						lowerCAmelCase__									=							predictor.from_batch(UpperCamelCase_ )
						lowerCAmelCase__									=							[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__									=							rouge_evaluator.get_scores(UpperCamelCase_  ,		UpperCamelCase_ )
						lowerCAmelCase__									=							format_rouge_scores(UpperCamelCase_ )
						save_rouge_scores(UpperCamelCase_ )
						print(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Union[str, Any] )   ->    str:







			'''simple docstring'''



			for summary, document_name in zip(UpperCamelCase_  ,		UpperCamelCase_ ):
						# 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(UpperCamelCase_  ,		UpperCamelCase_ )
						with open(UpperCamelCase_  ,		"w" ) as output:
									output.write(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       Any )   ->    List[Any]:







			'''simple docstring'''



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

			return summary







def 					A       ( UpperCamelCase_   :       int )   ->    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       ( UpperCamelCase_   :       Union[str, Any] )   ->    Union[str, Any]:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       Tuple )   ->    Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__									=							load_and_cache_examples(UpperCamelCase_  ,		UpperCamelCase_ )
			lowerCAmelCase__									=							SequentialSampler(UpperCamelCase_ )

			def collate_fn(UpperCamelCase_   :       Tuple ):
						return collate(UpperCamelCase_  ,		UpperCamelCase_  ,		block_size=5_12  ,		device=args.device )

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

			return iterator







def 					A       ( UpperCamelCase_   :       Optional[int]  ,		UpperCamelCase_   :       Dict )   ->    str:







			'''simple docstring'''



			lowerCAmelCase__									=							CNNDMDataset(args.documents_dir )
			return dataset







def 					A       ( UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       Optional[int]  ,		UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       Tuple )   ->    List[str]:







			'''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(UpperCamelCase_ ) for _, _, summary_list in data]

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

			lowerCAmelCase__									=							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       ( UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       Tuple )   ->    Optional[Any]:







			'''simple docstring'''



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







def 					A       ( )   ->    Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__									=							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=2_00  ,		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__									=							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=UpperCamelCase_ )

			evaluate(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       Union[str, Any] )   ->    int:







			'''simple docstring'''



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

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

			return True


if __name__ == "__main__":
		main()







'''simple docstring'''

def 					A       ( )   ->    str:







			'''simple docstring'''



			lowerCAmelCase__									=							1

			while True:
						if (
						    sorted(str(UpperCamelCase_ ) )
						    == sorted(str(2 * i ) )
						    == sorted(str(3 * i ) )
						    == sorted(str(4 * i ) )
						    == sorted(str(5 * i ) )
						    == sorted(str(6 * i ) )
						):
									return i

						i += 1


if __name__ == "__main__":
		print(solution())





