



'''simple docstring'''
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCamelCase											=			'''\
@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},
}
'''

_UpperCamelCase											=			'''\
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
'''


_UpperCamelCase											=			'''
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_	(   lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  List[Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[str]										=     [rgram for rgrams in rgramslist for rgram in rgrams]
			__UpperCAmelCase   :      int										=     Counter(lowerCAmelCase__ )

			__UpperCAmelCase   :      List[Any]										=     Counter(lowerCAmelCase__ )
			__UpperCAmelCase   :      Union[str, Any]										=     Counter()
			for sgram, scount in sgramcounter.items():
						__UpperCAmelCase   :      Any										=     scount * numref

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

			# KEEP
			__UpperCAmelCase   :      Optional[int]										=     sgramcounter_rep & cgramcounter_rep
			__UpperCAmelCase   :      Union[str, Any]										=     keepgramcounter_rep & rgramcounter
			__UpperCAmelCase   :      Union[str, Any]										=     sgramcounter_rep & rgramcounter

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

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

			# ADDITION
			__UpperCAmelCase   :      int										=     set(lowerCAmelCase__ ) - set(lowerCAmelCase__ )
			__UpperCAmelCase   :      Union[str, Any]										=     set(lowerCAmelCase__ ) & set(lowerCAmelCase__ )
			__UpperCAmelCase   :      List[Any]										=     set(lowerCAmelCase__ ) - set(lowerCAmelCase__ )

			__UpperCAmelCase   :      List[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   :      int										=     1
			__UpperCAmelCase   :      int										=     1
			if len(lowerCAmelCase__ ) > 0:
						__UpperCAmelCase   :      Optional[Any]										=     addtmpscore / len(lowerCAmelCase__ )
			if len(lowerCAmelCase__ ) > 0:
						__UpperCAmelCase   :      List[str]										=     addtmpscore / len(lowerCAmelCase__ )
			__UpperCAmelCase   :      Any										=     0
			if addscore_precision > 0 or addscore_recall > 0:
						__UpperCAmelCase   :      Any										=     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)


def   lowercase_	(   lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  Tuple ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     len(lowerCAmelCase__ )

			__UpperCAmelCase   :      int										=     ssent.split(""" """ )
			__UpperCAmelCase   :      Optional[int]										=     csent.split(""" """ )
			__UpperCAmelCase   :      List[Any]										=     []
			__UpperCAmelCase   :      Optional[int]										=     []
			__UpperCAmelCase   :      List[str]										=     []
			__UpperCAmelCase   :      List[Any]										=     []
			__UpperCAmelCase   :      Tuple										=     []
			__UpperCAmelCase   :      str										=     []

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

			((__UpperCAmelCase)				,  (__UpperCAmelCase)				,  (__UpperCAmelCase))   :      List[Any]										=     SARIngram(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )
			((__UpperCAmelCase)				,  (__UpperCAmelCase)				,  (__UpperCAmelCase))   :      Any										=     SARIngram(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )
			((__UpperCAmelCase)				,  (__UpperCAmelCase)				,  (__UpperCAmelCase))   :      int										=     SARIngram(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )
			((__UpperCAmelCase)				,  (__UpperCAmelCase)				,  (__UpperCAmelCase))   :      Union[str, Any]										=     SARIngram(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )
			__UpperCAmelCase   :      Union[str, Any]										=     sum([keepascore, keepascore, keepascore, keepascore] ) / 4
			__UpperCAmelCase   :      Union[str, Any]										=     sum([delascore, delascore, delascore, delascore] ) / 4
			__UpperCAmelCase   :      Union[str, Any]										=     sum([addascore, addascore, addascore, addascore] ) / 4
			__UpperCAmelCase   :      List[Any]										=     (avgkeepscore + avgdelscore + avgaddscore) / 3
			return finalscore


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  bool = True			,							lowerCAmelCase__	:  str = "13a"			,							lowerCAmelCase__	:  bool = True ):

			"""simple docstring"""





			if lowercase:
						__UpperCAmelCase   :      Optional[int]										=     sentence.lower()

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

			if not return_str:
						__UpperCAmelCase   :      str										=     normalized_sent.split()

			return normalized_sent






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





		def 							__A       (     self				)       ->     Dict:

					'''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 ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					if not (len(__UpperCAmelCase				) == len(__UpperCAmelCase				) == len(__UpperCAmelCase				)):
								raise ValueError("""Sources length must match predictions and references lengths."""				)
					__UpperCAmelCase   :      Optional[int]										=     0
					for src, pred, refs in zip(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				):
								sari_score += SARIsent(normalize(__UpperCAmelCase				) ,       normalize(__UpperCAmelCase				) ,       [normalize(__UpperCAmelCase				) for sent in refs]				)
					__UpperCAmelCase   :      Dict										=     sari_score / len(__UpperCAmelCase				)
					return {"sari": 100 * sari_score}









'''simple docstring'''
import torch

from transformers import AutoTokenizer



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     AutoTokenizer.from_pretrained(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      Dict										=     None
								for l in x:
											__UpperCAmelCase   :      Dict										=     self.tokenizer(
											    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )
											__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor([len(__UpperCAmelCase				)]				)
											if d is not None:
														for k in d.keys():
																	__UpperCAmelCase   :      Dict										=     torch.cat((d[k], t[k]) ,       0				)
											else:
														__UpperCAmelCase   :      List[str]										=     t

								__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""				)				)
								__UpperCAmelCase   :      Any										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""				)				)

					elif isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      int										=     self.tokenizer(
								    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )

					else:
								raise Exception(
								    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
								    """ strings` for supports are supported."""				)

					return d





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=0.70				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     []
					for idx in range(len(W_query["""input_ids"""]				)				):
								__UpperCAmelCase   :      Optional[int]										=     range(p_start.shape[1]				)

								__UpperCAmelCase   :      Union[str, Any]										=     []
								for start_id in start_indexes:
											for end_id in end_indexes:
														if start_id < end_id:
																	output.append(
																	    (
																	        start_id,
																	        end_id,
																	        p_start[idx][start_id].item(),
																	        p_end[idx][end_id].item(),
																	    )				)

								output.sort(key=lambda __UpperCAmelCase				: (tup[2] * tup[3]) ,       reverse=__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[Any]										=     []
								for k in range(len(__UpperCAmelCase				)				):
											if output[k][2] * output[k][3] >= thresh:
														__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     output[k][0], output[k][1]
														__UpperCAmelCase   :      int										=     self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]				)
														temp.append((decoded, output[k][2] * output[k][3])				)

								final_outputs.append(__UpperCAmelCase				)

					return final_outputs




