

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_a:      Union[str, Any]								=     """\
@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},
}
"""

_a:      List[Any]								=     """\
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
"""


_a:      str								=     """
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  __lowerCAmelCase (  A    ,       A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     [rgram for rgrams in rgramslist for rgram in rgrams]
	UpperCAmelCase_    			=     Counter(A      )

	UpperCAmelCase_    			=     Counter(A      )
	UpperCAmelCase_    			=     Counter()
	for sgram, scount in sgramcounter.items():
		UpperCAmelCase_    			=     scount * numref

	UpperCAmelCase_    			=     Counter(A      )
	UpperCAmelCase_    			=     Counter()
	for cgram, ccount in cgramcounter.items():
		UpperCAmelCase_    			=     ccount * numref

	# KEEP
	UpperCAmelCase_    			=     sgramcounter_rep & cgramcounter_rep
	UpperCAmelCase_    			=     keepgramcounter_rep & rgramcounter
	UpperCAmelCase_    			=     sgramcounter_rep & rgramcounter

	UpperCAmelCase_    			=     0
	UpperCAmelCase_    			=     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_    			=     1
	UpperCAmelCase_    			=     1
	if len(A      ) > 0:
		UpperCAmelCase_    			=     keeptmpscorea / len(A      )
	if len(A      ) > 0:
		# Fix an alleged bug [2] in the keep score computation.
		# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
		UpperCAmelCase_    			=     keeptmpscorea / sum(keepgramcounterall_rep.values()      )
	UpperCAmelCase_    			=     0
	if keepscore_precision > 0 or keepscore_recall > 0:
		UpperCAmelCase_    			=     2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

	# DELETION
	UpperCAmelCase_    			=     sgramcounter_rep - cgramcounter_rep
	UpperCAmelCase_    			=     delgramcounter_rep - rgramcounter
	UpperCAmelCase_    			=     sgramcounter_rep - rgramcounter
	UpperCAmelCase_    			=     0
	UpperCAmelCase_    			=     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_    			=     1
	if len(A      ) > 0:
		UpperCAmelCase_    			=     deltmpscorea / len(A      )

	# ADDITION
	UpperCAmelCase_    			=     set(A      ) - set(A      )
	UpperCAmelCase_    			=     set(A      ) & set(A      )
	UpperCAmelCase_    			=     set(A      ) - set(A      )

	UpperCAmelCase_    			=     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_    			=     1
	UpperCAmelCase_    			=     1
	if len(A      ) > 0:
		UpperCAmelCase_    			=     addtmpscore / len(A      )
	if len(A      ) > 0:
		UpperCAmelCase_    			=     addtmpscore / len(A      )
	UpperCAmelCase_    			=     0
	if addscore_precision > 0 or addscore_recall > 0:
		UpperCAmelCase_    			=     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)



def  __lowerCAmelCase (  A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     len(A      )

	UpperCAmelCase_    			=     ssent.split(" "      )
	UpperCAmelCase_    			=     csent.split(" "      )
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []

	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	UpperCAmelCase_    			=     []
	for rsent in rsents:
		UpperCAmelCase_    			=     rsent.split(" "      )
		UpperCAmelCase_    			=     []
		UpperCAmelCase_    			=     []
		UpperCAmelCase_    			=     []
		ragramslist.append(A      )
		for i in range(0    ,       len(A      ) - 1      ):
			if i < len(A      ) - 1:
				UpperCAmelCase_    			=     ragrams[i] + " " + ragrams[i + 1]
				ragrams.append(A      )
			if i < len(A      ) - 2:
				UpperCAmelCase_    			=     ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
				ragrams.append(A      )
			if i < len(A      ) - 3:
				UpperCAmelCase_    			=     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_    			=     sagrams[i] + " " + sagrams[i + 1]
			sagrams.append(A      )
		if i < len(A      ) - 2:
			UpperCAmelCase_    			=     sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
			sagrams.append(A      )
		if i < len(A      ) - 3:
			UpperCAmelCase_    			=     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_    			=     cagrams[i] + " " + cagrams[i + 1]
			cagrams.append(A      )
		if i < len(A      ) - 2:
			UpperCAmelCase_    			=     cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
			cagrams.append(A      )
		if i < len(A      ) - 3:
			UpperCAmelCase_    			=     cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
			cagrams.append(A      )

	((UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_))    			=     SARIngram(A    ,       A    ,       A    ,       A      )
	((UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_))    			=     SARIngram(A    ,       A    ,       A    ,       A      )
	((UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_))    			=     SARIngram(A    ,       A    ,       A    ,       A      )
	((UpperCAmelCase_)			,				(UpperCAmelCase_)			,				(UpperCAmelCase_))    			=     SARIngram(A    ,       A    ,       A    ,       A      )
	UpperCAmelCase_    			=     sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
	UpperCAmelCase_    			=     sum([delascore, delascore, delascore, delascore]      ) / 4
	UpperCAmelCase_    			=     sum([addascore, addascore, addascore, addascore]      ) / 4
	UpperCAmelCase_    			=     (avgkeepscore + avgdelscore + avgaddscore) / 3
	return finalscore



def  __lowerCAmelCase (  A    ,       A = True    ,       A = "13a"    ,       A = True      ):
	# 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_    			=     sentence.lower()

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

	if not return_str:
		UpperCAmelCase_    			=     normalized_sent.split()

	return normalized_sent




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




	def  __A   (						self		:  List[str]							):


		'''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		:  Union[str, Any]						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  int							):


		'''simple docstring'''







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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				_a:      Optional[Any]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)