



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase_:  Optional[Any]     						=					'\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'

lowercase_:  str     						=					'\\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'


lowercase_:  List[str]     						=					'\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 	_lowercase	(     UpperCAmelCase_):




 """simple docstring"""




 def remove_articles(UpperCAmelCase_):
  snake_case__				:							str      							= re.compile(R"""\b(a|an|the)\b"""						,   re.UNICODE)
  return re.sub(lowerCAmelCase__						,   """ """						,   lowerCAmelCase__)

 def white_space_fix(UpperCAmelCase_):
  return " ".join(text.split())

 def remove_punc(UpperCAmelCase_):
  snake_case__				:							List[str]      							= set(string.punctuation)
  return "".join(ch for ch in text if ch not in exclude)

 def lower(UpperCAmelCase_):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__))))
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 return int(normalize_answer(lowerCAmelCase__) == normalize_answer(lowerCAmelCase__))
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Any      							= [any(compute_exact(lowerCAmelCase__						,   lowerCAmelCase__) for ref in refs) for pred, refs in zip(lowerCAmelCase__						,   lowerCAmelCase__)]
 return (sum(lowerCAmelCase__) / len(lowerCAmelCase__)) * 100
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Dict      							= [rgram for rgrams in rgramslist for rgram in rgrams]
 snake_case__				:							Dict      							= Counter(lowerCAmelCase__)

 snake_case__				:							Dict      							= Counter(lowerCAmelCase__)
 snake_case__				:							Dict      							= Counter()
 for sgram, scount in sgramcounter.items():
  snake_case__				:							int      							= scount * numref

 snake_case__				:							Optional[int]      							= Counter(lowerCAmelCase__)
 snake_case__				:							str      							= Counter()
 for cgram, ccount in cgramcounter.items():
  snake_case__				:							Any      							= ccount * numref

 # KEEP
 snake_case__				:							List[str]      							= sgramcounter_rep & cgramcounter_rep
 snake_case__				:							Any      							= keepgramcounter_rep & rgramcounter
 snake_case__				:							Dict      							= sgramcounter_rep & rgramcounter

 snake_case__				:							List[str]      							= 0
 snake_case__				:							str      							= 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.
 snake_case__				:							Tuple      							= 1
 snake_case__				:							str      							= 1
 if len(lowerCAmelCase__) > 0:
  snake_case__				:							List[str]      							= keeptmpscorea / len(lowerCAmelCase__)
 if len(lowerCAmelCase__) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  snake_case__				:							Dict      							= keeptmpscorea / sum(keepgramcounterall_rep.values())
 snake_case__				:							int      							= 0
 if keepscore_precision > 0 or keepscore_recall > 0:
  snake_case__				:							Tuple      							= 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 snake_case__				:							Any      							= sgramcounter_rep - cgramcounter_rep
 snake_case__				:							Union[str, Any]      							= delgramcounter_rep - rgramcounter
 snake_case__				:							List[str]      							= sgramcounter_rep - rgramcounter
 snake_case__				:							Optional[Any]      							= 0
 snake_case__				:							Tuple      							= 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.
 snake_case__				:							Optional[int]      							= 1
 if len(lowerCAmelCase__) > 0:
  snake_case__				:							List[Any]      							= deltmpscorea / len(lowerCAmelCase__)

 # ADDITION
 snake_case__				:							Tuple      							= set(lowerCAmelCase__) - set(lowerCAmelCase__)
 snake_case__				:							Optional[Any]      							= set(lowerCAmelCase__) & set(lowerCAmelCase__)
 snake_case__				:							str      							= set(lowerCAmelCase__) - set(lowerCAmelCase__)

 snake_case__				:							Optional[int]      							= 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.
 snake_case__				:							Any      							= 1
 snake_case__				:							Union[str, Any]      							= 1
 if len(lowerCAmelCase__) > 0:
  snake_case__				:							Tuple      							= addtmpscore / len(lowerCAmelCase__)
 if len(lowerCAmelCase__) > 0:
  snake_case__				:							Optional[Any]      							= addtmpscore / len(lowerCAmelCase__)
 snake_case__				:							Optional[Any]      							= 0
 if addscore_precision > 0 or addscore_recall > 0:
  snake_case__				:							Tuple      							= 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Optional[Any]      							= len(lowerCAmelCase__)

 snake_case__				:							List[str]      							= ssent.split(""" """)
 snake_case__				:							Dict      							= csent.split(""" """)
 snake_case__				:							int      							= []
 snake_case__				:							Optional[int]      							= []
 snake_case__				:							Union[str, Any]      							= []
 snake_case__				:							Tuple      							= []
 snake_case__				:							int      							= []
 snake_case__				:							Union[str, Any]      							= []

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

 for i in range(0						,   len(lowerCAmelCase__) - 1):
  if i < len(lowerCAmelCase__) - 1:
   snake_case__				:							Any      							= sagrams[i] + """ """ + sagrams[i + 1]
   sagrams.append(lowerCAmelCase__)
  if i < len(lowerCAmelCase__) - 2:
   snake_case__				:							Tuple      							= sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
   sagrams.append(lowerCAmelCase__)
  if i < len(lowerCAmelCase__) - 3:
   snake_case__				:							Union[str, Any]      							= sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
   sagrams.append(lowerCAmelCase__)

 for i in range(0						,   len(lowerCAmelCase__) - 1):
  if i < len(lowerCAmelCase__) - 1:
   snake_case__				:							Tuple      							= cagrams[i] + """ """ + cagrams[i + 1]
   cagrams.append(lowerCAmelCase__)
  if i < len(lowerCAmelCase__) - 2:
   snake_case__				:							Optional[int]      							= cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
   cagrams.append(lowerCAmelCase__)
  if i < len(lowerCAmelCase__) - 3:
   snake_case__				:							int      							= cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
   cagrams.append(lowerCAmelCase__)

 ((snake_case__)							,		(snake_case__)							,		(snake_case__))				:							Dict      							= SARIngram(lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__)
 ((snake_case__)							,		(snake_case__)							,		(snake_case__))				:							Tuple      							= SARIngram(lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__)
 ((snake_case__)							,		(snake_case__)							,		(snake_case__))				:							Union[str, Any]      							= SARIngram(lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__)
 ((snake_case__)							,		(snake_case__)							,		(snake_case__))				:							str      							= SARIngram(lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__)
 snake_case__				:							List[str]      							= sum([keepascore, keepascore, keepascore, keepascore]) / 4
 snake_case__				:							int      							= sum([delascore, delascore, delascore, delascore]) / 4
 snake_case__				:							Optional[int]      							= sum([addascore, addascore, addascore, addascore]) / 4
 snake_case__				:							Any      							= (avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_ = True						,   UpperCAmelCase_ = "13a"						,   UpperCAmelCase_ = True):




 """simple docstring"""




 if lowercase:
  snake_case__				:							Tuple      							= sentence.lower()

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

 if not return_str:
  snake_case__				:							int      							= normalized_sent.split()

 return normalized_sent
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 if not (len(lowerCAmelCase__) == len(lowerCAmelCase__) == len(lowerCAmelCase__)):
  raise ValueError("""Sources length must match predictions and references lengths.""")
 snake_case__				:							Union[str, Any]      							= 0
 for src, pred, refs in zip(lowerCAmelCase__						,   lowerCAmelCase__						,   lowerCAmelCase__):
  sari_score += SARIsent(normalize(lowerCAmelCase__)						,   normalize(lowerCAmelCase__)						,   [normalize(lowerCAmelCase__) for sent in refs])
 snake_case__				:							Optional[int]      							= sari_score / len(lowerCAmelCase__)
 return 100 * sari_score
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_="exp"						,   UpperCAmelCase_=None						,   UpperCAmelCase_=False						,   UpperCAmelCase_=False						,   UpperCAmelCase_=False						,   ):




 """simple docstring"""




 snake_case__				:							Tuple      							= len(references[0])
 if any(len(lowerCAmelCase__) != references_per_prediction for refs in references):
  raise ValueError("""Sacrebleu requires the same number of references for each prediction""")
 snake_case__				:							Union[str, Any]      							= [[refs[i] for refs in references] for i in range(lowerCAmelCase__)]
 snake_case__				:							str      							= sacrebleu.corpus_bleu(
     lowerCAmelCase__						,   lowerCAmelCase__						,   smooth_method=lowerCAmelCase__						,   smooth_value=lowerCAmelCase__						,   force=lowerCAmelCase__						,   lowercase=lowerCAmelCase__						,   use_effective_order=lowerCAmelCase__						,   )
 return output.score



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,      _KWARGS_DESCRIPTION						)
class 						lowercase__      (datasets.Metric						):

     """simple docstring"""






     def 		lowercase   ( self :	int ):
      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 		lowercase   ( self :	Optional[Any]   ,			__a :	Tuple   ,			__a :	Tuple   ,			__a :	Any ):
      snake_case__				:							List[str]      							= {}
      result.update({"""sari""": compute_sari(sources=__UpperCamelCase   ,			predictions=__UpperCamelCase   ,			references=__UpperCamelCase )} )
      result.update({"""sacrebleu""": compute_sacrebleu(predictions=__UpperCamelCase   ,			references=__UpperCamelCase )} )
      result.update({"""exact""": compute_em(predictions=__UpperCamelCase   ,			references=__UpperCamelCase )} )
      return result









import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))