"""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,
)


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


_snake_case					:		Tuple							=			namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])






def  A__    (      UpperCamelCase	):
							A		=  BertTokenizer.from_pretrained("bert-base-uncased" ,  do_lower_case=__A	)
							A		=  BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"	)
							model.to(args.device	)
							model.eval()

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

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

														import nltk
														import rouge

														nltk.download("punkt"	)
														A		=  rouge.Rouge(
														    metrics=["rouge-n", "rouge-l"] ,  max_n=2 ,  limit_length=__A ,  length_limit=args.beam_size ,  length_limit_type="words" ,  apply_avg=__A ,  apply_best=__A ,  alpha=0.5 ,  weight_factor=1.2 ,  stemming=__A ,  )

							# these (unused) arguments are defined to keep the compatibility
							# with the legacy code and will be deleted in a next iteration.
							A		=  ""
							A		=  ""

							A		=  build_data_iterator(__A ,  __A	)
							A		=  build_predictor(__A ,  __A ,  __A ,  __A	)

							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(__A	):
														A		=  predictor.translate_batch(__A	)
														A		=  predictor.from_batch(__A	)
														A		=  [format_summary(__A	) for t in translations]
														save_summaries(__A ,  args.summaries_output_dir ,  batch.document_names	)

														if args.compute_rouge:
																					reference_summaries += batch.tgt_str
																					generated_summaries += summaries

							if args.compute_rouge:
														A		=  rouge_evaluator.get_scores(__A ,  __A	)
														A		=  format_rouge_scores(__A	)
														save_rouge_scores(__A	)
														print(__A	)






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):



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

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






def  A__    (      UpperCamelCase	):
							A,     A,     A		=  translation
							A		=  (
							    raw_summary.replace("[unused0]" ,  ""	)
							    .replace("[unused3]" ,  ""	)
							    .replace("[PAD]" ,  ""	)
							    .replace("[unused1]" ,  ""	)
							    .replace(r" +" ,  " "	)
							    .replace(" [unused2] " ,  ". "	)
							    .replace("[unused2]" ,  ""	)
							    .strip()
							)

							return summary






def  A__    (      UpperCamelCase	):
							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	):



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






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  load_and_cache_examples(__A ,  __A	)
							A		=  SequentialSampler(__A	)

							def collate_fn(UpperCamelCase	):
														return collate(__A ,  __A ,  block_size=512 ,  device=args.device	)

							A		=  DataLoader(
							    __A ,  sampler=__A ,  batch_size=args.batch_size ,  collate_fn=__A ,  )

							return iterator






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  CNNDMDataset(args.documents_dir	)
							return dataset






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  [x for x in data if not len(x[1]	) == 0]  # remove empty_files
							A		=  [name for name, _, _ in data]
							A		=  [" ".join(__A	) for _, _, summary_list in data]

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

							A		=  Batch(
							    document_names=__A ,  batch_size=len(__A	) ,  src=encoded_stories.to(__A	) ,  segs=encoder_token_type_ids.to(__A	) ,  mask_src=encoder_mask.to(__A	) ,  tgt_str=__A ,  )

							return batch






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  summary_tokens.to("cpu"	).numpy()
							A		=  tokenizer.decode(__A	)
							A		=  summary.split("."	)
							A		=  [s + "." for s in sentences]
							return sentences






def  A__    (      ):
							A		=  argparse.ArgumentParser()
							parser.add_argument(
							    "--documents_dir" ,  default=__A ,  type=__A ,  required=__A ,  help="The folder where the documents to summarize are located." ,  )
							parser.add_argument(
							    "--summaries_output_dir" ,  default=__A ,  type=__A ,  required=__A ,  help="The folder in wich the summaries should be written. Defaults to the folder where the documents are" ,  )
							parser.add_argument(
							    "--compute_rouge" ,  default=__A ,  type=__A ,  required=__A ,  help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset." ,  )
							# EVALUATION options
							parser.add_argument(
							    "--no_cuda" ,  default=__A ,  type=__A ,  help="Whether to force the execution on CPU." ,  )
							parser.add_argument(
							    "--batch_size" ,  default=4 ,  type=__A ,  help="Batch size per GPU/CPU for training." ,  )
							# BEAM SEARCH arguments
							parser.add_argument(
							    "--min_length" ,  default=50 ,  type=__A ,  help="Minimum number of tokens for the summaries." ,  )
							parser.add_argument(
							    "--max_length" ,  default=200 ,  type=__A ,  help="Maixmum number of tokens for the summaries." ,  )
							parser.add_argument(
							    "--beam_size" ,  default=5 ,  type=__A ,  help="The number of beams to start with for each example." ,  )
							parser.add_argument(
							    "--alpha" ,  default=0.95 ,  type=__A ,  help="The value of alpha for the length penalty in the beam search." ,  )
							parser.add_argument(
							    "--block_trigram" ,  default=__A ,  type=__A ,  help="Whether to block the existence of repeating trigrams in the text generated by beam search." ,  )
							A		=  parser.parse_args()

							# Select device (distibuted not available)
							A		=  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:
														A		=  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=__A	)

							evaluate(__A	)






def  A__    (      UpperCamelCase	):



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

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

							return True


if __name__ == "__main__":
		main()









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


