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


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





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Tuple:






      '''simple docstring'''





      UpperCAmelCase     :			Tuple     	=BertTokenizer.from_pretrained('''bert-base-uncased'''		,       do_lower_case=__lowerCAmelCase    )
      UpperCAmelCase     :			Dict     	=BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''    )
      model.to(args.device    )
      model.eval()

      UpperCAmelCase     :			Tuple     	={
          '''BOS''': tokenizer.vocab['''[unused0]'''],
          '''EOS''': tokenizer.vocab['''[unused1]'''],
          '''PAD''': tokenizer.vocab['''[PAD]'''],
      }

      if args.compute_rouge:
            UpperCAmelCase     :			Union[str, Any]     	=[]
            UpperCAmelCase     :			Dict     	=[]

            import nltk
            import rouge

            nltk.download('''punkt'''    )
            UpperCAmelCase     :			Union[str, Any]     	=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.
      UpperCAmelCase     :			List[Any]     	=''''''
      UpperCAmelCase     :			Union[str, Any]     	=''''''

      UpperCAmelCase     :			Union[str, Any]     	=build_data_iterator(__lowerCAmelCase		,       __lowerCAmelCase    )
      UpperCAmelCase     :			Tuple     	=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    ):
            UpperCAmelCase     :			int     	=predictor.translate_batch(__lowerCAmelCase    )
            UpperCAmelCase     :			str     	=predictor.from_batch(__lowerCAmelCase    )
            UpperCAmelCase     :			Tuple     	=[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:
            UpperCAmelCase     :			str     	=rouge_evaluator.get_scores(__lowerCAmelCase		,       __lowerCAmelCase    )
            UpperCAmelCase     :			Dict     	=format_rouge_scores(__lowerCAmelCase    )
            save_rouge_scores(__lowerCAmelCase    )
            print(__lowerCAmelCase    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	List[Any]:






      '''simple docstring'''





      for summary, document_name in zip(__lowerCAmelCase		,       __lowerCAmelCase    ):
            # Prepare the summary file's name
            if "." in document_name:
                  UpperCAmelCase     :			Any     	='''.'''.join(document_name.split('''.'''    )[:-1]    )
                  UpperCAmelCase     :			List[str]     	=document_name.split('''.'''    )[-1]
                  UpperCAmelCase     :			str     	=bare_document_name + '''_summary.''' + extension
            else:
                  UpperCAmelCase     :			int     	=document_name + '''_summary'''

            UpperCAmelCase     :			str     	=os.path.join(__lowerCAmelCase		,       __lowerCAmelCase    )
            with open(__lowerCAmelCase		,       '''w'''    ) as output:
                  output.write(__lowerCAmelCase    )





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Any:






      '''simple docstring'''





      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=translation
      UpperCAmelCase     :			Tuple     	=(
          raw_summary.replace('''[unused0]'''		,       ''''''    )
          .replace('''[unused3]'''		,       ''''''    )
          .replace('''[PAD]'''		,       ''''''    )
          .replace('''[unused1]'''		,       ''''''    )
          .replace(R''' +'''		,       ''' '''    )
          .replace(''' [unused2] '''		,       '''. '''    )
          .replace('''[unused2]'''		,       ''''''    )
          .strip()
      )

      return summary





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	List[Any]:






      '''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 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Dict:






      '''simple docstring'''





      with open('''rouge_scores.txt'''		,       '''w'''    ) as output:
            output.write(__lowerCAmelCase    )





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






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=load_and_cache_examples(__lowerCAmelCase		,       __lowerCAmelCase    )
      UpperCAmelCase     :			Optional[Any]     	=SequentialSampler(__lowerCAmelCase    )

      def collate_fn(__lowerCAmelCase    ):
            return collate(__lowerCAmelCase		,       __lowerCAmelCase		,       block_size=5_12		,       device=args.device    )

      UpperCAmelCase     :			str     	=DataLoader(
          __lowerCAmelCase		,       sampler=__lowerCAmelCase		,       batch_size=args.batch_size		,       collate_fn=__lowerCAmelCase		,       )

      return iterator





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	List[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Dict     	=CNNDMDataset(args.documents_dir    )
      return dataset





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Dict:






      '''simple docstring'''





      UpperCAmelCase     :			Tuple     	=[x for x in data if not len(x[1]    ) == 0]  # remove empty_files
      UpperCAmelCase     :			Dict     	=[name for name, _, _ in data]
      UpperCAmelCase     :			int     	=[''' '''.join(__lowerCAmelCase    ) for _, _, summary_list in data]

      UpperCAmelCase     :			Optional[Any]     	=[encode_for_summarization(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    ) for _, story, summary in data]
      UpperCAmelCase     :			int     	=torch.tensor(
          [truncate_or_pad(__lowerCAmelCase		,       __lowerCAmelCase		,       tokenizer.pad_token_id    ) for story, _ in encoded_text]    )
      UpperCAmelCase     :			str     	=compute_token_type_ids(__lowerCAmelCase		,       tokenizer.cls_token_id    )
      UpperCAmelCase     :			str     	=build_mask(__lowerCAmelCase		,       tokenizer.pad_token_id    )

      UpperCAmelCase     :			List[str]     	=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    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Dict     	=summary_tokens.to('''cpu'''    ).numpy()
      UpperCAmelCase     :			Tuple     	=tokenizer.decode(__lowerCAmelCase    )
      UpperCAmelCase     :			Optional[Any]     	=summary.split('''.'''    )
      UpperCAmelCase     :			Optional[Any]     	=[s + '''.''' for s in sentences]
      return sentences





def 	lowerCAmelCase_			(					)->	List[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=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.'''		,       )
      UpperCAmelCase     :			List[str]     	=parser.parse_args()

      # Select device (distibuted not available)
      UpperCAmelCase     :			Union[str, Any]     	=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:
            UpperCAmelCase     :			Optional[Any]     	=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    )->	List[Any]:






      '''simple docstring'''





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

      UpperCAmelCase     :			List[Any]     	=os.listdir(__lowerCAmelCase    )
      if len(__lowerCAmelCase    ) == 0:
            return False

      return True


if __name__ == "__main__":
     main()







import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A_        =  set()
A_        =  get_logger(__name__)





def  __UpperCamelCase   (     a = None)			->str:

					def decorator(a):
										global _emitted_deprecation_warnings

										if inspect.isclass(a):
															lowerCamelCase__      =							deprecated_class_or_function.__init__
															lowerCamelCase__      =							deprecated_class_or_function.__name__
										else:
															lowerCamelCase__      =							deprecated_class_or_function
															lowerCamelCase__      =							deprecated_function.__name__
															# Support deprecating __init__ class method: class name instead
															lowerCamelCase__      =							name if name != "__init__" else deprecated_function.__qualname__.split(".")[-2]

										lowerCamelCase__      =							(
										    f"{name} is deprecated and will be removed in the next major version of datasets." + f" {help_message}"
										    if help_message
										    else ""
										)

										@wraps(a)
										def wrapper(*a,  **a):
															lowerCamelCase__      =							hash(a)
															if func_hash not in _emitted_deprecation_warnings:
																				warnings.warn(a,  category=a,  stacklevel=2)
																				_emitted_deprecation_warnings.add(a)
															return deprecated_function(*a,  **a)

										lowerCamelCase__      =							"deprecated"

										if inspect.isclass(a):
															lowerCamelCase__      =							wrapper
															return deprecated_class_or_function
										else:
															return wrapper

					return decorator

class SCREAMING_SNAKE_CASE_			(       enum.EnumMeta ):





					"""simple docstring"""


					def __getattribute__(			cls						,	_lowerCAmelCase						):
										lowerCamelCase__      =							super().__getattribute__(_lowerCAmelCase						)
										if isinstance(_lowerCAmelCase						,	enum.Enum						) and obj._on_access:
															obj._on_access()
										return obj





					def __getitem__(			cls						,	_lowerCAmelCase						):
										lowerCamelCase__      =							super().__getitem__(_lowerCAmelCase						)
										if member._on_access:
															member._on_access()
										return member






					def __call__(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase=None						,	*, _lowerCAmelCase=None						,	_lowerCAmelCase=None						,	_lowerCAmelCase=None						,	_lowerCAmelCase=1						):
										lowerCamelCase__      =							super().__call__(_lowerCAmelCase						,	_lowerCAmelCase						,	module=_lowerCAmelCase						,	qualname=_lowerCAmelCase						,	type=_lowerCAmelCase						,	start=_lowerCAmelCase						)
										if isinstance(_lowerCAmelCase						,	enum.Enum						) and obj._on_access:
															obj._on_access()
										return obj

class SCREAMING_SNAKE_CASE_			(       enum.Enum				,							metaclass=lowercase_ ):





					"""simple docstring"""


					def __new__(			cls						,	_lowerCAmelCase						):
										lowerCamelCase__      =							object.__new__(cls						)
										lowerCamelCase__      =							value
										lowerCamelCase__      =							member.deprecate
										return member





					@property
					def        __magic_name__			(			self						):
										return ""






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							F" {self.help_message}" if self.help_message else ""
										warnings.warn(
										    F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
										    + help_message						,	_lowerCAmelCase						,	stacklevel=3						,	)

