






"""simple docstring"""
import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCAmelCase__							= 1_000


class _lowerCAmelCase	:







						def __init__(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=1.2      ,							lowerCAmelCase_="g"      ,							lowerCAmelCase_=0.5      ,							lowerCAmelCase_="-"      ,							lowerCAmelCase_="test_out.jpg"      ,							lowerCAmelCase_=True      ,							lowerCAmelCase_=False      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=0.7      ,							)		->       List[Any]:
										if isinstance(lowerCAmelCase_      ,							torch.Tensor       ):
														_SCREAMING_SNAKE_CASE      :		int							=						img.numpy().astype('np.uint8'       )
										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						img_tensorize(lowerCAmelCase_       )
										assert isinstance(lowerCAmelCase_      ,							np.ndarray       )

										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[int]							=						img.shape[1], img.shape[0]
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						mplfigure.Figure(frameon=lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						fig.get_dpi()
										_SCREAMING_SNAKE_CASE      :		Any							=						(width * scale + 1e-2) / dpi
										_SCREAMING_SNAKE_CASE      :		Dict							=						(height * scale + 1e-2) / dpi
										fig.set_size_inches(lowerCAmelCase_      ,							lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						fig.add_axes([0.0, 0.0, 1.0, 1.0]       )
										ax.axis('off'       )
										ax.set_xlim(0.0      ,							lowerCAmelCase_       )
										ax.set_ylim(lowerCAmelCase_       )

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						saveas
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						rgb
										_SCREAMING_SNAKE_CASE      :		Dict							=						pynb
										_SCREAMING_SNAKE_CASE      :		Tuple							=						img
										_SCREAMING_SNAKE_CASE      :		str							=						edgecolor
										_SCREAMING_SNAKE_CASE      :		Tuple							=						0.5
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						linestyle
										_SCREAMING_SNAKE_CASE      :		str							=						int(np.sqrt(min(lowerCAmelCase_      ,							lowerCAmelCase_       )       ) * scale // 3       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						width
										_SCREAMING_SNAKE_CASE      :		Tuple							=						height
										_SCREAMING_SNAKE_CASE      :		str							=						scale
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						fig
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ax
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						pad
										_SCREAMING_SNAKE_CASE      :		Dict							=						idaobj
										_SCREAMING_SNAKE_CASE      :		int							=						idaattr
										_SCREAMING_SNAKE_CASE      :		str							=						FigureCanvasAgg(lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=None       )		->       Optional[int]:
										if color is None:
														_SCREAMING_SNAKE_CASE      :		List[str]							=						self.edgecolor
										((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))      :		Optional[Any]							=						box
										_SCREAMING_SNAKE_CASE      :		Tuple							=						xa - xa
										_SCREAMING_SNAKE_CASE      :		Any							=						ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya)      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							fill=lowerCAmelCase_      ,							edgecolor=lowerCAmelCase_      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							)       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None       )		->       Optional[Any]:
										if len(boxes.shape       ) > 2:
														_SCREAMING_SNAKE_CASE      :		int							=						boxes[0]
										if len(obj_ids.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		Tuple							=						obj_ids[0]
										if len(obj_scores.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						obj_scores[0]
										if len(attr_ids.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		str							=						attr_ids[0]
										if len(attr_scores.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		int							=						attr_scores[0]
										if isinstance(lowerCAmelCase_      ,							torch.Tensor       ):
														_SCREAMING_SNAKE_CASE      :		Any							=						boxes.numpy()
										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		int							=						np.array(lowerCAmelCase_       )
										assert isinstance(lowerCAmelCase_      ,							np.ndarray       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						np.prod(boxes[:, 2:] - boxes[:, :2]      ,							axis=1       )
										_SCREAMING_SNAKE_CASE      :		List[str]							=						np.argsort(-areas       ).tolist()
										_SCREAMING_SNAKE_CASE      :		str							=						boxes[sorted_idxs] if boxes is not None else None
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						obj_ids[sorted_idxs] if obj_ids is not None else None
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						obj_scores[sorted_idxs] if obj_scores is not None else None
										_SCREAMING_SNAKE_CASE      :		int							=						attr_ids[sorted_idxs] if attr_ids is not None else None
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						attr_scores[sorted_idxs] if attr_scores is not None else None

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[self._random_color(maximum=1       ) for _ in range(len(lowerCAmelCase_       )       )]
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
														_SCREAMING_SNAKE_CASE      :		str							=						self._create_text_labels_attr(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )
														for i in range(len(lowerCAmelCase_       )       ):
																		_SCREAMING_SNAKE_CASE      :		Dict							=						assigned_colors[i]
																		self.add_box(boxes[i]      ,							lowerCAmelCase_       )
																		self.draw_labels(labels[i]      ,							boxes[i]      ,							lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       int:
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Dict							=						box
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						(xa, ya)
										_SCREAMING_SNAKE_CASE      :		Dict							=						(ya - ya) * (xa - xa)
										_SCREAMING_SNAKE_CASE      :		List[str]							=						_SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 4_0 * self.scale:
														if ya >= self.height - 5:
																		_SCREAMING_SNAKE_CASE      :		List[str]							=						(xa, ya)
														else:
																		_SCREAMING_SNAKE_CASE      :		str							=						(xa, ya)

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						(ya - ya) / np.sqrt(self.height * self.width       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						self._change_color_brightness(lowerCAmelCase_      ,							brightness_factor=0.7       )
										_SCREAMING_SNAKE_CASE      :		Any							=						np.clip((height_ratio - 0.02) / 0.08 + 1      ,							1.2      ,							2       )
										font_size *= 0.75 * self.font_size

										self.draw_text(
										    text=lowerCAmelCase_      ,							position=lowerCAmelCase_      ,							color=lowerCAmelCase_      ,							)







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_="g"      ,							lowerCAmelCase_="left"      ,							)		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						0
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.font_size
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						np.maximum(list(mplc.to_rgb(lowerCAmelCase_       )       )      ,							0.2       )
										_SCREAMING_SNAKE_CASE      :		Dict							=						max(0.8      ,							np.max(lowerCAmelCase_       )       )
										_SCREAMING_SNAKE_CASE      :		int							=						{
										    'facecolor': 'black',
										    'alpha': self.alpha,
										    'pad': self.pad,
										    'edgecolor': 'none',
										}
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		str							=						position
										self.ax.text(
										    lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							size=font_size * self.scale      ,							family='sans-serif'      ,							bbox=lowerCAmelCase_      ,							verticalalignment='top'      ,							horizontalalignment=lowerCAmelCase_      ,							color=lowerCAmelCase_      ,							zorder=1_0      ,							rotation=lowerCAmelCase_      ,							)







						def  A			(						self      ,							lowerCAmelCase_=None       )		->       List[Any]:
										if saveas is None:
														_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						self.saveas
										if saveas.lower().endswith('.jpg'       ) or saveas.lower().endswith('.png'       ):
														cva.imwrite(
														    lowerCAmelCase_      ,							self._get_buffer()[:, :, ::-1]      ,							)
										else:
														self.fig.savefig(lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       List[str]:
										_SCREAMING_SNAKE_CASE      :		Tuple							=						[self.idaobj[i] for i in classes]
										_SCREAMING_SNAKE_CASE      :		Tuple							=						[self.idaattr[i] for i in attr_classes]
										_SCREAMING_SNAKE_CASE      :		str							=						[
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )
										]
										return labels







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						[self.idaobj[i] for i in classes]
										if scores is not None:
														if labels is None:
																		_SCREAMING_SNAKE_CASE      :		List[Any]							=						['{:.0f}%'.format(s * 1_0_0       ) for s in scores]
														else:
																		_SCREAMING_SNAKE_CASE      :		Any							=						['{} {:.0f}%'.format(lowerCAmelCase_      ,							s * 1_0_0       ) for li, s in zip(lowerCAmelCase_      ,							lowerCAmelCase_       )]
										return labels







						def  A			(						self      ,							lowerCAmelCase_=2_5_5       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		int							=						np.random.randint(0      ,							len(_COLORS       )       )
										_SCREAMING_SNAKE_CASE      :		Any							=						_COLORS[idx] * maximum
										if not self.rgb:
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ret[::-1]
										return ret







						def  A			(						self       )		->       Any:
										if not self.pynb:
														_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Dict							=						self.canvas.print_to_buffer()
														if (width, height) != (self.width, self.height):
																		_SCREAMING_SNAKE_CASE      :		Dict							=						cva.resize(self.img      ,							(width, height)       )
														else:
																		_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.img
										else:
														_SCREAMING_SNAKE_CASE      :		Tuple							=						io.BytesIO()  # works for cairo backend
														self.canvas.print_rgba(lowerCAmelCase_       )
														_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.width, self.height
														_SCREAMING_SNAKE_CASE      :		Any							=						buf.getvalue()
														_SCREAMING_SNAKE_CASE      :		Dict							=						self.img

										_SCREAMING_SNAKE_CASE      :		Any							=						np.frombuffer(lowerCAmelCase_      ,							dtype='uint8'       )
										_SCREAMING_SNAKE_CASE      :		Any							=						buffer.reshape(lowerCAmelCase_      ,							lowerCAmelCase_      ,							4       )
										_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Tuple							=						np.split(lowerCAmelCase_      ,							[3]      ,							axis=2       )

										try:
														import numexpr as ne  # fuse them with numexpr

														_SCREAMING_SNAKE_CASE      :		List[str]							=						ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'       )
										except ImportError:
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						alpha.astype('float32'       ) / 255.0
														_SCREAMING_SNAKE_CASE      :		int							=						img * (1 - alpha) + rgb * alpha

										return visualized_image.astype('uint8'       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Optional[int]:
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										_SCREAMING_SNAKE_CASE      :		Any							=						mplc.to_rgb(lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		Dict							=						colorsys.rgb_to_hls(*mplc.to_rgb(lowerCAmelCase_       )       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						polygon_color[1] + (brightness_factor * polygon_color[1])
										_SCREAMING_SNAKE_CASE      :		str							=						0.0 if modified_lightness < 0.0 else modified_lightness
										_SCREAMING_SNAKE_CASE      :		Dict							=						1.0 if modified_lightness > 1.0 else modified_lightness
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						colorsys.hls_to_rgb(polygon_color[0]      ,							lowerCAmelCase_      ,							polygon_color[2]       )
										return modified_color







lowerCAmelCase__							= (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









"""simple docstring"""
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__							= '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

lowerCAmelCase__							= '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


lowerCAmelCase__							= '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		int							=						[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE      :		Any							=						Counter(lowerCamelCase					)

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

				_SCREAMING_SNAKE_CASE      :		Dict							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ccount * numref

				# KEEP
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		str							=						keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						sgramcounter_rep & rgramcounter

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

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

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

				_SCREAMING_SNAKE_CASE      :		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.
				_SCREAMING_SNAKE_CASE      :		Tuple							=						1
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						0
				if addscore_precision > 0 or addscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						ssent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						csent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]

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

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





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase = True,	lowerCamelCase = "13a",	lowerCamelCase = 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:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						sentence.lower()

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

				if not return_str:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						normalized_sent.split()

				return normalized_sent






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







						def  A			(						self       )		->       Dict:
										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      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Optional[int]:
										if not (len(lowerCAmelCase_       ) == len(lowerCAmelCase_       ) == len(lowerCAmelCase_       )):
														raise ValueError('Sources length must match predictions and references lengths.'       )
										_SCREAMING_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]       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						sari_score / len(lowerCAmelCase_       )
										return {"sari": 1_0_0 * sari_score}

