




"""simple docstring"""

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowerCamelCase	:      Optional[int]   =							"""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

_lowerCamelCase	:      Dict   =							"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


_lowerCamelCase	:      Union[str, Any]   =							"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[str]						 =		[rgram for rgrams in rgramslist for rgram in rgrams]
	A_	:					str						 =		Counter(_UpperCAmelCase						)

	A_	:					int						 =		Counter(_UpperCAmelCase						)
	A_	:					str						 =		Counter()
	for sgram, scount in sgramcounter.items():
		A_	:					str						 =		scount * numref

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

	# KEEP
	A_	:					str						 =		sgramcounter_rep & cgramcounter_rep
	A_	:					Dict						 =		keepgramcounter_rep & rgramcounter
	A_	:					Dict						 =		sgramcounter_rep & rgramcounter

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

	# DELETION
	A_	:					List[Any]						 =		sgramcounter_rep - cgramcounter_rep
	A_	:					List[str]						 =		delgramcounter_rep - rgramcounter
	A_	:					Dict						 =		sgramcounter_rep - rgramcounter
	A_	:					Optional[Any]						 =		0
	A_	:					Union[str, 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.
	A_	:					Dict						 =		1
	if len(_UpperCAmelCase						) > 0:
		A_	:					List[Any]						 =		deltmpscorea / len(_UpperCAmelCase						)

	# ADDITION
	A_	:					Union[str, Any]						 =		set(_UpperCAmelCase						) - set(_UpperCAmelCase						)
	A_	:					List[Any]						 =		set(_UpperCAmelCase						) & set(_UpperCAmelCase						)
	A_	:					List[str]						 =		set(_UpperCAmelCase						) - set(_UpperCAmelCase						)

	A_	:					Optional[Any]						 =		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.
	A_	:					Dict						 =		1
	A_	:					Dict						 =		1
	if len(_UpperCAmelCase						) > 0:
		A_	:					Optional[int]						 =		addtmpscore / len(_UpperCAmelCase						)
	if len(_UpperCAmelCase						) > 0:
		A_	:					List[str]						 =		addtmpscore / len(_UpperCAmelCase						)
	A_	:					Union[str, Any]						 =		0
	if addscore_precision > 0 or addscore_recall > 0:
		A_	:					Optional[int]						 =		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Dict						 =		len(_UpperCAmelCase						)

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

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

	for i in range(0	,   len(_UpperCAmelCase						) - 1						):
		if i < len(_UpperCAmelCase						) - 1:
			A_	:					Optional[Any]						 =		sagrams[i] + ''' ''' + sagrams[i + 1]
			sagrams.append(_UpperCAmelCase						)
		if i < len(_UpperCAmelCase						) - 2:
			A_	:					Any						 =		sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
			sagrams.append(_UpperCAmelCase						)
		if i < len(_UpperCAmelCase						) - 3:
			A_	:					Optional[int]						 =		sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
			sagrams.append(_UpperCAmelCase						)

	for i in range(0	,   len(_UpperCAmelCase						) - 1						):
		if i < len(_UpperCAmelCase						) - 1:
			A_	:					str						 =		cagrams[i] + ''' ''' + cagrams[i + 1]
			cagrams.append(_UpperCAmelCase						)
		if i < len(_UpperCAmelCase						) - 2:
			A_	:					Optional[Any]						 =		cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
			cagrams.append(_UpperCAmelCase						)
		if i < len(_UpperCAmelCase						) - 3:
			A_	:					Tuple						 =		cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
			cagrams.append(_UpperCAmelCase						)

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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "13a"	,   _UpperCAmelCase = True						):




	"""simple docstring"""






	if lowercase:
		A_	:					Any						 =		sentence.lower()

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

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

	return normalized_sent

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







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					return datasets.MetricInfo(
					    description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
					        {
					            '''sources''': datasets.Value('''string'''   ,  id='''sequence'''							),
					            '''predictions''': datasets.Value('''string'''   ,  id='''sequence'''							),
					            '''references''': datasets.Sequence(datasets.Value('''string'''   ,  id='''sequence'''							)   ,  id='''references'''							),
					        }							)   ,  codebase_urls=[
					        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
					        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
					    ]   ,  reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf''']   ,  )







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Dict							):



					"""simple docstring"""

					if not (len(__A							) == len(__A							) == len(__A							)):
						raise ValueError('''Sources length must match predictions and references lengths.'''							)
					A_	:					Optional[int]						 =		0
					for src, pred, refs in zip(__A   ,  __A   ,  __A							):
						sari_score += SARIsent(normalize(__A							)   ,  normalize(__A							)   ,  [normalize(__A							) for sent in refs]							)
					A_	:					Tuple						 =		sari_score / len(__A							)
					return {"sari": 1_00 * sari_score}









"""simple docstring"""







import argparse
import json
import re
import string
import sys
from collections import Counter
def _A		(		lowercase						):







    """simple docstring"""

    def remove_articles(lowercase						):
        return re.sub(R'''\b(a|an|the)\b'''			,       ''' '''			,       lowercase						)

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

    def remove_punc(lowercase						):
        a											=set(string.punctuation						)
        return "".join(ch for ch in text if ch not in exclude						)

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

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=a											=a											=0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    a											='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(lowercase			,       file=sys.stderr						)
                    continue
                a											=[x['''text'''] for x in qa['''answers''']]
                a											=predictions[qa['''id''']]
                exact_match += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)
                fa += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

    return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
       lowerCamelCase_  :  List[Any]     		=    """1.1"""
       lowerCamelCase_  :  Optional[Any]     		=    argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
       parser.add_argument("""dataset_file""", help="""Dataset file""")
       parser.add_argument("""prediction_file""", help="""Prediction File""")
       lowerCamelCase_  :  Optional[Any]     		=    parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase_  :  List[str]     		=    json.load(dataset_file)
              if dataset_json["version"] != expected_version:
                     print(
                         """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
                         file=sys.stderr,
                     )
              lowerCamelCase_  :  Dict     		=    dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase_  :  Optional[Any]     		=    json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))