




"""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__			:     str			       =				logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


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




def    snake_case__						(							lowerCAmelCase :		Dict		):
					__snake_case        =						BertTokenizer.from_pretrained("""bert-base-uncased"""					,       do_lower_case=lowerCAmelCase		)
					__snake_case        =						BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""		)
					model.to(args.device		)
					model.eval()

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

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

										import nltk
										import rouge

										nltk.download("""punkt"""		)
										__snake_case        =						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.
					__snake_case        =						""""""
					__snake_case        =						""""""

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




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		Optional[Any]		):
					for summary, document_name in zip(lowerCAmelCase					,       lowerCAmelCase		):
										# Prepare the summary file's name
										if "." in document_name:
															__snake_case        =						""".""".join(document_name.split("""."""		)[:-1]		)
															__snake_case        =						document_name.split("""."""		)[-1]
															__snake_case        =						bare_document_name + """_summary.""" + extension
										else:
															__snake_case        =						document_name + """_summary"""

										__snake_case        =						os.path.join(lowerCAmelCase					,       lowerCAmelCase		)
										with open(lowerCAmelCase					,       """w"""		) as output:
															output.write(lowerCAmelCase		)




def    snake_case__						(							lowerCAmelCase :		str		):
					__snake_case   ,   __snake_case   ,   __snake_case        =						translation
					__snake_case        =						(
					    raw_summary.replace("""[unused0]"""					,       """"""		)
					    .replace("""[unused3]"""					,       """"""		)
					    .replace("""[PAD]"""					,       """"""		)
					    .replace("""[unused1]"""					,       """"""		)
					    .replace(R""" +"""					,       """ """		)
					    .replace(""" [unused2] """					,       """. """		)
					    .replace("""[unused2]"""					,       """"""		)
					    .strip()
					)

					return summary




def    snake_case__						(							lowerCAmelCase :		int		):
					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    snake_case__						(							lowerCAmelCase :		int		):
					with open("""rouge_scores.txt"""					,       """w"""		) as output:
										output.write(lowerCAmelCase		)




def    snake_case__						(							lowerCAmelCase :		Union[str, Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						load_and_cache_examples(lowerCAmelCase					,       lowerCAmelCase		)
					__snake_case        =						SequentialSampler(lowerCAmelCase		)

					def collate_fn(lowerCAmelCase :		List[str]		):
										return collate(lowerCAmelCase					,       lowerCAmelCase					,       block_size=512					,       device=args.device		)

					__snake_case        =						DataLoader(
					    lowerCAmelCase					,       sampler=lowerCAmelCase					,       batch_size=args.batch_size					,       collate_fn=lowerCAmelCase					,       )

					return iterator




def    snake_case__						(							lowerCAmelCase :		int					,       lowerCAmelCase :		Union[str, Any]		):
					__snake_case        =						CNNDMDataset(args.documents_dir		)
					return dataset




def    snake_case__						(							lowerCAmelCase :		Dict					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Any		):
					__snake_case        =						[x for x in data if not len(x[1]		) == 0]  # remove empty_files
					__snake_case        =						[name for name, _, _ in data]
					__snake_case        =						[""" """.join(lowerCAmelCase		) for _, _, summary_list in data]

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

					__snake_case        =						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    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict		):
					__snake_case        =						summary_tokens.to("""cpu"""		).numpy()
					__snake_case        =						tokenizer.decode(lowerCAmelCase		)
					__snake_case        =						summary.split("""."""		)
					__snake_case        =						[s + """.""" for s in sentences]
					return sentences




def    snake_case__						(							):
					__snake_case        =						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=200					,       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=0.9_5					,       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."""					,       )
					__snake_case        =						parser.parse_args()

					# Select device (distibuted not available)
					__snake_case        =						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:
										__snake_case        =						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    snake_case__						(							lowerCAmelCase :		Optional[Any]		):
					if not os.path.exists(lowerCAmelCase		):
										return False

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

					return True


if __name__ == "__main__":
		main()








"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims


