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


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







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







			'''simple docstring'''



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

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







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







			'''simple docstring'''



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







def      A   (					UpperCamelCase_    :   Dict					)    ->				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_    :   str					)    ->				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      A   (					UpperCamelCase_    :   List[str]					)    ->				Tuple:







			'''simple docstring'''



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







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







			'''simple docstring'''



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

			def collate_fn(UpperCamelCase_    :   List[str]					):
						return collate(lowerCAmelCase__  ,		lowerCAmelCase__  ,		block_size=5_12  ,		device=args.device					)

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

			return iterator







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







			'''simple docstring'''



			lowerCAmelCase__									=							CNNDMDataset(args.documents_dir					)
			return dataset







def      A   (					UpperCamelCase_    :   Tuple  ,		UpperCamelCase_    :   Tuple  ,		UpperCamelCase_    :   Any  ,		UpperCamelCase_    :   str					)    ->				Dict:







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

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

			lowerCAmelCase__									=							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   (					UpperCamelCase_    :   Tuple  ,		UpperCamelCase_    :   Any					)    ->				Any:







			'''simple docstring'''



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







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







			'''simple docstring'''



			lowerCAmelCase__									=							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=2_00  ,		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."  ,		)
			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=lowerCAmelCase__					)

			evaluate(lowerCAmelCase__					)







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







			'''simple docstring'''



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

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

			return True


if __name__ == "__main__":
		main()






from ..utils import DummyObject, requires_backends
class 				a_ (						metaclass=SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					['tensorflow_text']




 def __init__(			self					:  Tuple			,    *lowercase__					:  str			,    **lowercase__					:  Union[str, Any]):







        '''simple docstring'''



        requires_backends(self			,    ['tensorflow_text'])
