'''simple docstring'''

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__  :						List[str]					=      '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCamelCase__  :						Optional[Any]					=      '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


UpperCamelCase__  :						Union[str, Any]					=      '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'







def __UpperCamelCase( _A       :					Dict     ):
   '''simple docstring'''

   def remove_articles(_A       :					Any     ):
      UpperCAmelCase__				:					Optional[Any] 					=	re.compile(R'''\b(a|an|the)\b'''	,      re.UNICODE     )
      return re.sub(_A	,      ''' '''	,      _A     )

   def white_space_fix(_A       :					Any     ):
      return " ".join(text.split()     )

   def remove_punc(_A       :					Any     ):
      UpperCAmelCase__				:					str 					=	set(string.punctuation     )
      return "".join(ch for ch in text if ch not in exclude     )

   def lower(_A       :					List[Any]     ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A     )     )     )     )







def __UpperCamelCase( _A       :					Optional[Any]	,      _A       :					List[str]     ):
   '''simple docstring'''
   return int(normalize_answer(_A     ) == normalize_answer(_A     )     )







def __UpperCamelCase( _A       :					Union[str, Any]	,      _A       :					str     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	[any(compute_exact(_A	,      _A     ) for ref in refs     ) for pred, refs in zip(_A	,      _A     )]
   return (sum(_A     ) / len(_A     )) * 1_00







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					Any	,      _A       :					Any	,      _A       :					int     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	[rgram for rgrams in rgramslist for rgram in rgrams]
   UpperCAmelCase__				:					Dict 					=	Counter(_A     )

   UpperCAmelCase__				:					Optional[int] 					=	Counter(_A     )
   UpperCAmelCase__				:					Any 					=	Counter()
   for sgram, scount in sgramcounter.items():
      UpperCAmelCase__				:					Union[str, Any] 					=	scount * numref

   UpperCAmelCase__				:					Dict 					=	Counter(_A     )
   UpperCAmelCase__				:					Optional[int] 					=	Counter()
   for cgram, ccount in cgramcounter.items():
      UpperCAmelCase__				:					List[str] 					=	ccount * numref

   # KEEP
   UpperCAmelCase__				:					Union[str, Any] 					=	sgramcounter_rep & cgramcounter_rep
   UpperCAmelCase__				:					List[Any] 					=	keepgramcounter_rep & rgramcounter
   UpperCAmelCase__				:					List[Any] 					=	sgramcounter_rep & rgramcounter

   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Dict 					=	0
   for keepgram in keepgramcountergood_rep:
      keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
      # Fix an alleged bug [2] in the keep score computation.
      # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
      keeptmpscorea += keepgramcountergood_rep[keepgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   #      a target exactly.
   UpperCAmelCase__				:					Tuple 					=	1
   UpperCAmelCase__				:					Optional[int] 					=	1
   if len(_A     ) > 0:
      UpperCAmelCase__				:					Union[str, Any] 					=	keeptmpscorea / len(_A     )
   if len(_A     ) > 0:
      # Fix an alleged bug [2] in the keep score computation.
      # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
      UpperCAmelCase__				:					Dict 					=	keeptmpscorea / sum(keepgramcounterall_rep.values()     )
   UpperCAmelCase__				:					Optional[Any] 					=	0
   if keepscore_precision > 0 or keepscore_recall > 0:
      UpperCAmelCase__				:					Optional[Any] 					=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

   # DELETION
   UpperCAmelCase__				:					int 					=	sgramcounter_rep - cgramcounter_rep
   UpperCAmelCase__				:					Dict 					=	delgramcounter_rep - rgramcounter
   UpperCAmelCase__				:					Union[str, Any] 					=	sgramcounter_rep - rgramcounter
   UpperCAmelCase__				:					Optional[Any] 					=	0
   UpperCAmelCase__				:					Optional[Any] 					=	0
   for delgram in delgramcountergood_rep:
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   UpperCAmelCase__				:					Any 					=	1
   if len(_A     ) > 0:
      UpperCAmelCase__				:					Any 					=	deltmpscorea / len(_A     )

   # ADDITION
   UpperCAmelCase__				:					str 					=	set(_A     ) - set(_A     )
   UpperCAmelCase__				:					Any 					=	set(_A     ) & set(_A     )
   UpperCAmelCase__				:					Optional[int] 					=	set(_A     ) - set(_A     )

   UpperCAmelCase__				:					str 					=	0
   for addgram in addgramcountergood:
      addtmpscore += 1

   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   UpperCAmelCase__				:					Any 					=	1
   UpperCAmelCase__				:					Union[str, Any] 					=	1
   if len(_A     ) > 0:
      UpperCAmelCase__				:					List[Any] 					=	addtmpscore / len(_A     )
   if len(_A     ) > 0:
      UpperCAmelCase__				:					Any 					=	addtmpscore / len(_A     )
   UpperCAmelCase__				:					List[Any] 					=	0
   if addscore_precision > 0 or addscore_recall > 0:
      UpperCAmelCase__				:					List[str] 					=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)







def __UpperCamelCase( _A       :					Union[str, Any]	,      _A       :					List[str]	,      _A       :					int     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	len(_A     )

   UpperCAmelCase__				:					Optional[int] 					=	ssent.split(''' '''     )
   UpperCAmelCase__				:					int 					=	csent.split(''' '''     )
   UpperCAmelCase__				:					Any 					=	[]
   UpperCAmelCase__				:					str 					=	[]
   UpperCAmelCase__				:					str 					=	[]
   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					str 					=	[]
   UpperCAmelCase__				:					Dict 					=	[]

   UpperCAmelCase__				:					int 					=	[]
   UpperCAmelCase__				:					Tuple 					=	[]
   UpperCAmelCase__				:					str 					=	[]
   UpperCAmelCase__				:					List[Any] 					=	[]
   for rsent in rsents:
      UpperCAmelCase__				:					Dict 					=	rsent.split(''' '''     )
      UpperCAmelCase__				:					Union[str, Any] 					=	[]
      UpperCAmelCase__				:					int 					=	[]
      UpperCAmelCase__				:					int 					=	[]
      ragramslist.append(_A     )
      for i in range(0	,      len(_A     ) - 1     ):
         if i < len(_A     ) - 1:
            UpperCAmelCase__				:					Any 					=	ragrams[i] + ''' ''' + ragrams[i + 1]
            ragrams.append(_A     )
         if i < len(_A     ) - 2:
            UpperCAmelCase__				:					int 					=	ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
            ragrams.append(_A     )
         if i < len(_A     ) - 3:
            UpperCAmelCase__				:					Tuple 					=	ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
            ragrams.append(_A     )
      ragramslist.append(_A     )
      ragramslist.append(_A     )
      ragramslist.append(_A     )

   for i in range(0	,      len(_A     ) - 1     ):
      if i < len(_A     ) - 1:
         UpperCAmelCase__				:					Union[str, Any] 					=	sagrams[i] + ''' ''' + sagrams[i + 1]
         sagrams.append(_A     )
      if i < len(_A     ) - 2:
         UpperCAmelCase__				:					Union[str, Any] 					=	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
         sagrams.append(_A     )
      if i < len(_A     ) - 3:
         UpperCAmelCase__				:					List[Any] 					=	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
         sagrams.append(_A     )

   for i in range(0	,      len(_A     ) - 1     ):
      if i < len(_A     ) - 1:
         UpperCAmelCase__				:					Optional[int] 					=	cagrams[i] + ''' ''' + cagrams[i + 1]
         cagrams.append(_A     )
      if i < len(_A     ) - 2:
         UpperCAmelCase__				:					Dict 					=	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
         cagrams.append(_A     )
      if i < len(_A     ) - 3:
         UpperCAmelCase__				:					Union[str, Any] 					=	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
         cagrams.append(_A     )

   ((UpperCAmelCase__)					,   (UpperCAmelCase__)					,   (UpperCAmelCase__))				:					Tuple 					=	SARIngram(_A	,      _A	,      _A	,      _A     )
   ((UpperCAmelCase__)					,   (UpperCAmelCase__)					,   (UpperCAmelCase__))				:					Tuple 					=	SARIngram(_A	,      _A	,      _A	,      _A     )
   ((UpperCAmelCase__)					,   (UpperCAmelCase__)					,   (UpperCAmelCase__))				:					List[Any] 					=	SARIngram(_A	,      _A	,      _A	,      _A     )
   ((UpperCAmelCase__)					,   (UpperCAmelCase__)					,   (UpperCAmelCase__))				:					Optional[Any] 					=	SARIngram(_A	,      _A	,      _A	,      _A     )
   UpperCAmelCase__				:					List[Any] 					=	sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
   UpperCAmelCase__				:					int 					=	sum([delascore, delascore, delascore, delascore]     ) / 4
   UpperCAmelCase__				:					Dict 					=	sum([addascore, addascore, addascore, addascore]     ) / 4
   UpperCAmelCase__				:					Dict 					=	(avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore







def __UpperCamelCase( _A       :					Union[str, Any]	,      _A       :					bool = True	,      _A       :					str = "13a"	,      _A       :					bool = True     ):
   '''simple docstring'''
   # Normalization is requried for the ASSET dataset (one of the primary
   # datasets in sentence simplification) to allow using space
   # to split the sentence. Even though Wiki-Auto and TURK datasets,
   # do not require normalization, we do it for consistency.
   # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
   # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

   if lowercase:
      UpperCAmelCase__				:					List[Any] 					=	sentence.lower()

   if tokenizer in ["13a", "intl"]:
      if version.parse(sacrebleu.__version__     ).major >= 2:
         UpperCAmelCase__				:					str 					=	sacrebleu.metrics.bleu._get_tokenizer(_A     )()(_A     )
      else:
         UpperCAmelCase__				:					Union[str, Any] 					=	sacrebleu.TOKENIZERS[tokenizer]()(_A     )
   elif tokenizer == "moses":
      UpperCAmelCase__				:					str 					=	sacremoses.MosesTokenizer().tokenize(_A	,      return_str=_A	,      escape=_A     )
   elif tokenizer == "penn":
      UpperCAmelCase__				:					Any 					=	sacremoses.MosesTokenizer().penn_tokenize(_A	,      return_str=_A     )
   else:
      UpperCAmelCase__				:					List[str] 					=	sentence

   if not return_str:
      UpperCAmelCase__				:					List[Any] 					=	normalized_sent.split()

   return normalized_sent







def __UpperCamelCase( _A       :					Union[str, Any]	,      _A       :					Optional[Any]	,      _A       :					int     ):
   '''simple docstring'''
   if not (len(_A     ) == len(_A     ) == len(_A     )):
      raise ValueError('''Sources length must match predictions and references lengths.'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   for src, pred, refs in zip(_A	,      _A	,      _A     ):
      sari_score += SARIsent(normalize(_A     )	,      normalize(_A     )	,      [normalize(_A     ) for sent in refs]     )
   UpperCAmelCase__				:					Optional[Any] 					=	sari_score / len(_A     )
   return 1_00 * sari_score







def __UpperCamelCase( _A       :					int	,      _A       :					List[str]	,      _A       :					Union[str, Any]="exp"	,      _A       :					str=None	,      _A       :					Tuple=False	,      _A       :					Optional[Any]=False	,      _A       :					List[str]=False	,      ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	len(references[0]     )
   if any(len(_A     ) != references_per_prediction for refs in references     ):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''     )
   UpperCAmelCase__				:					Optional[int] 					=	[[refs[i] for refs in references] for i in range(_A     )]
   UpperCAmelCase__				:					List[str] 					=	sacrebleu.corpus_bleu(
       _A	,      _A	,      smooth_method=_A	,      smooth_value=_A	,      force=_A	,      lowercase=_A	,      use_effective_order=_A	,      )
   return output.score


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION )
class 					_lowercase						(   datasets.Metric ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return datasets.MetricInfo(
             description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''       ,id='''sequence'''				),
                     '''references''': datasets.Sequence(datasets.Value('''string'''       ,id='''sequence'''				)       ,id='''references'''				),
                 }				)       ,codebase_urls=[
                 '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                 '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                 '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                 '''https://github.com/mjpost/sacreBLEU''',
             ]       ,reference_urls=[
                 '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                 '''https://github.com/mjpost/sacreBLEU''',
                 '''https://en.wikipedia.org/wiki/BLEU''',
                 '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
             ]       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	{}
         result.update({'''sari''': compute_sari(sources=lowerCamelCase_       ,predictions=lowerCamelCase_       ,references=lowerCamelCase_				)}				)
         result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase_       ,references=lowerCamelCase_				)}				)
         result.update({'''exact''': compute_em(predictions=lowerCamelCase_       ,references=lowerCamelCase_				)}				)
         return result





'''simple docstring'''

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__  :						Dict					=      logging.getLogger(__name__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	argparse.ArgumentParser(
       description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''     )
   parser.add_argument('''--file_path'''	,      type=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

   logger.info(F'''Loading Tokenizer ({args.tokenizer_name})'''     )
   if args.tokenizer_type == "bert":
      UpperCAmelCase__				:					List[str] 					=	BertTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
      UpperCAmelCase__				:					Union[str, Any] 					=	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      UpperCAmelCase__				:					Union[str, Any] 					=	RobertaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
      UpperCAmelCase__				:					Dict 					=	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
   elif args.tokenizer_type == "gpt2":
      UpperCAmelCase__				:					List[str] 					=	GPTaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

   logger.info(F'''Loading text from {args.file_path}'''     )
   with open(args.file_path	,      '''r'''	,      encoding='''utf8'''     ) as fp:
      UpperCAmelCase__				:					List[Any] 					=	fp.readlines()

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

   UpperCAmelCase__				:					Dict 					=	F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
   UpperCAmelCase__				:					Dict 					=	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      UpperCAmelCase__				:					Any 					=	[np.uintaa(_A     ) for d in rslt]
   else:
      UpperCAmelCase__				:					str 					=	[np.intaa(_A     ) for d in rslt]
   random.shuffle(rslt_     )
   logger.info(F'''Dump to {dp_file}'''     )
   with open(_A	,      '''wb'''     ) as handle:
      pickle.dump(rslt_	,      _A	,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
  main()




