

"""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,
)


_a				   =							logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_a				   =							namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])




def 						_A						(						UpperCamelCase_  : Tuple)			->		Optional[int]:
   '''simple docstring'''


   __lowercase			=		BertTokenizer.from_pretrained("bert-base-uncased",    do_lower_case=UpperCamelCase_)
   __lowercase			=		BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
   model.to(args.device)
   model.eval()

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

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

      import nltk
      import rouge

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

   __lowercase			=		build_data_iterator(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		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_):
      __lowercase			=		predictor.translate_batch(UpperCamelCase_)
      __lowercase			=		predictor.from_batch(UpperCamelCase_)
      __lowercase			=		[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:
      __lowercase			=		rouge_evaluator.get_scores(UpperCamelCase_,    UpperCamelCase_)
      __lowercase			=		format_rouge_scores(UpperCamelCase_)
      save_rouge_scores(UpperCamelCase_)
      print(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : List[Any],    UpperCamelCase_  : List[str])			->		Optional[Any]:
   '''simple docstring'''


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

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




def 						_A						(						UpperCamelCase_  : List[str])			->		int:
   '''simple docstring'''


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

   return summary




def 						_A						(						UpperCamelCase_  : str)			->		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_  : List[Any])			->		Any:
   '''simple docstring'''


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




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any])			->		Optional[int]:
   '''simple docstring'''


   __lowercase			=		load_and_cache_examples(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		SequentialSampler(UpperCamelCase_)

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

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

   return iterator




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : Union[str, Any])			->		str:
   '''simple docstring'''


   __lowercase			=		CNNDMDataset(args.documents_dir)
   return dataset




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : Optional[Any],    UpperCamelCase_  : str,    UpperCamelCase_  : List[Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		[x for x in data if not len(x[1]) == 0]  # remove empty_files
   __lowercase			=		[name for name, _, _ in data]
   __lowercase			=		[" ".join(UpperCamelCase_) for _, _, summary_list in data]

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

   __lowercase			=		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_  : Union[str, Any],    UpperCamelCase_  : List[Any])			->		Any:
   '''simple docstring'''


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




def 						_A						(						)			->		Optional[Any]:
   '''simple docstring'''


   __lowercase			=		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.",    )
   __lowercase			=		parser.parse_args()

   # Select device (distibuted not available)
   __lowercase			=		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:
      __lowercase			=		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_  : Any)			->		str:
   '''simple docstring'''


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

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

   return True


if __name__ == "__main__":
   main()







"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   _a				   =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




