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,
)


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


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






def       __lowerCamelCase		(						lowerCAmelCase__	):
       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       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       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       __lowerCamelCase		(						lowerCAmelCase__	):
       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       __lowerCamelCase		(						lowerCAmelCase__	):
       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       __lowerCamelCase		(						lowerCAmelCase__	):
       with open('rouge_scores.txt'	,  'w'	) as output:
              output.write(lowerCAmelCase__	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					load_and_cache_examples(lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					SequentialSampler(lowerCAmelCase__	)

       def collate_fn(lowerCAmelCase__	):
              return collate(lowerCAmelCase__	,  lowerCAmelCase__	,  block_size=5_1_2	,  device=args.device	)

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

       return iterator






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					CNNDMDataset(args.documents_dir	)
       return dataset






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       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       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					summary_tokens.to('cpu'	).numpy()
       lowerCAmelCase__     	=					tokenizer.decode(lowerCAmelCase__	)
       lowerCAmelCase__     	=					summary.split('.'	)
       lowerCAmelCase__     	=					[s + '.' for s in sentences]
       return sentences






def       __lowerCamelCase		(						):
       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=5_0	,  type=lowerCAmelCase__	,  help='Minimum number of tokens for the summaries.'	,  )
       parser.add_argument(
           '--max_length'	,  default=2_0_0	,  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.'	,  )
       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       __lowerCamelCase		(						lowerCAmelCase__	):
       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					'vilt'




 def __init__(			self					:  Optional[Any]			,    lowercase__					:  Dict=30_522			,    lowercase__					:  Tuple=2			,    lowercase__					:  int=2			,    lowercase__					:  Optional[int]=40			,    lowercase__					:  Optional[int]=768			,    lowercase__					:  Tuple=12			,    lowercase__					:  Tuple=12			,    lowercase__					:  Union[str, Any]=3_072			,    lowercase__					:  int="gelu"			,    lowercase__					:  Tuple=0.0			,    lowercase__					:  Dict=0.0			,    lowercase__					:  Optional[int]=0.02			,    lowercase__					:  Tuple=1e-12			,    lowercase__					:  Dict=384			,    lowercase__					:  int=32			,    lowercase__					:  List[str]=3			,    lowercase__					:  int=True			,    lowercase__					:  List[str]=-1			,    lowercase__					:  Union[str, Any]=False			,    lowercase__					:  List[str]=-1			,    **lowercase__					:  int			,    ):







        '''simple docstring'''



        super().__init__(tie_word_embeddings=lowercase__			,    **lowercase__)

        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					type_vocab_size
        lowerCAmelCase__     	=					modality_type_vocab_size
        lowerCAmelCase__     	=					max_position_embeddings

        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					layer_norm_eps

        lowerCAmelCase__     	=					image_size
        lowerCAmelCase__     	=					patch_size
        lowerCAmelCase__     	=					num_channels
        lowerCAmelCase__     	=					qkv_bias
        lowerCAmelCase__     	=					max_image_length
        lowerCAmelCase__     	=					num_images
