





'''simple docstring'''





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
				import transformers


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Optional[Any]:
		random.seed(SCREAMING_SNAKE_CASE_		)
		np.random.seed(SCREAMING_SNAKE_CASE_		)
		torch.manual_seed(SCREAMING_SNAKE_CASE_		)
		torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_		)
		# ^^ safe to call this function even if cuda is not available



class        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase = 0.9_9_9_9			,     __lowercase = 0.0			,     __lowercase = 0			,     __lowercase = False			,     __lowercase = 1.0			,     __lowercase = 2 / 3			,     __lowercase = None			,     __lowercase = None			,     **__lowercase			,     )				->   List[str]:

				if isinstance(__lowercase			,     torch.nn.Module   ):
						lowerCamelCase   : Union[str, Any]								=(
						    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
						    '''Please pass the parameters of the module instead.'''
						)
						deprecate(
						    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''			,     '''1.0.0'''			,     __lowercase			,     standard_warn=__lowercase			,     )
						lowerCamelCase   : int								=parameters.parameters()

						# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
						lowerCamelCase   : Optional[Any]								=True

				if kwargs.get('''max_value'''			,     __lowercase   ) is not None:
						lowerCamelCase   : Optional[int]								='''The `max_value` argument is deprecated. Please use `decay` instead.'''
						deprecate('''max_value'''			,     '''1.0.0'''			,     __lowercase			,     standard_warn=__lowercase   )
						lowerCamelCase   : Dict								=kwargs['''max_value''']

				if kwargs.get('''min_value'''			,     __lowercase   ) is not None:
						lowerCamelCase   : Dict								='''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
						deprecate('''min_value'''			,     '''1.0.0'''			,     __lowercase			,     standard_warn=__lowercase   )
						lowerCamelCase   : List[str]								=kwargs['''min_value''']

				lowerCamelCase   : List[Any]								=list(__lowercase   )
				lowerCamelCase   : int								=[p.clone().detach() for p in parameters]

				if kwargs.get('''device'''			,     __lowercase   ) is not None:
						lowerCamelCase   : Any								='''The `device` argument is deprecated. Please use `to` instead.'''
						deprecate('''device'''			,     '''1.0.0'''			,     __lowercase			,     standard_warn=__lowercase   )
						self.to(device=kwargs['''device''']   )

				lowerCamelCase   : List[Any]								=None

				lowerCamelCase   : Union[str, Any]								=decay
				lowerCamelCase   : Optional[Any]								=min_decay
				lowerCamelCase   : str								=update_after_step
				lowerCamelCase   : Optional[Any]								=use_ema_warmup
				lowerCamelCase   : Any								=inv_gamma
				lowerCamelCase   : Optional[Any]								=power
				lowerCamelCase   : List[Any]								=0
				lowerCamelCase   : Union[str, Any]								=None  # set in `step()`

				lowerCamelCase   : str								=model_cls
				lowerCamelCase   : Optional[Any]								=model_config







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase			,     __lowercase   )				->   "EMAModel":
				lowerCamelCase   : Tuple								=model_cls.load_config(__lowercase			,     return_unused_kwargs=__lowercase   )
				lowerCamelCase   : List[str]								=model_cls.from_pretrained(__lowercase   )

				lowerCamelCase   : List[str]								=cls(model.parameters()			,     model_cls=__lowercase			,     model_config=model.config   )

				ema_model.load_state_dict(__lowercase   )
				return ema_model







		def 			__lowercase   (		self			,     __lowercase   )				->   Union[str, Any]:
				if self.model_cls is None:
						raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

				if self.model_config is None:
						raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

				lowerCamelCase   : Tuple								=self.model_cls.from_config(self.model_config   )
				lowerCamelCase   : int								=self.state_dict()
				state_dict.pop('''shadow_params'''			,     __lowercase   )

				model.register_to_config(**__lowercase   )
				self.copy_to(model.parameters()   )
				model.save_pretrained(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   float:
				lowerCamelCase   : Optional[int]								=max(0			,     optimization_step - self.update_after_step - 1   )

				if step <= 0:
						return 0.0

				if self.use_ema_warmup:
						lowerCamelCase   : List[str]								=1 - (1 + step / self.inv_gamma) ** -self.power
				else:
						lowerCamelCase   : Union[str, Any]								=(1 + step) / (1_0 + step)

				lowerCamelCase   : int								=min(__lowercase			,     self.decay   )
				# make sure decay is not smaller than min_decay
				lowerCamelCase   : str								=max(__lowercase			,     self.min_decay   )
				return cur_decay_value







		@torch.no_grad()
		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				if isinstance(__lowercase			,     torch.nn.Module   ):
						lowerCamelCase   : Optional[int]								=(
						    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
						    '''Please pass the parameters of the module instead.'''
						)
						deprecate(
						    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''			,     '''1.0.0'''			,     __lowercase			,     standard_warn=__lowercase			,     )
						lowerCamelCase   : Any								=parameters.parameters()

				lowerCamelCase   : List[str]								=list(__lowercase   )

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				lowerCamelCase   : List[str]								=self.get_decay(self.optimization_step   )
				lowerCamelCase   : Any								=decay
				lowerCamelCase   : Optional[Any]								=1 - decay

				lowerCamelCase   : List[Any]								=contextlib.nullcontext
				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
						import deepspeed

				for s_param, param in zip(self.shadow_params			,     __lowercase   ):
						if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
								lowerCamelCase   : List[Any]								=deepspeed.zero.GatheredParameters(__lowercase			,     modifier_rank=__lowercase   )

						with context_manager():
								if param.requires_grad:
										s_param.sub_(one_minus_decay * (s_param - param)   )
								else:
										s_param.copy_(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Dict								=list(__lowercase   )
				for s_param, param in zip(self.shadow_params			,     __lowercase   ):
						param.data.copy_(s_param.to(param.device   ).data   )







		def 			__lowercase   (		self			,     __lowercase=None			,     __lowercase=None   )				->   None:
				lowerCamelCase   : Tuple								=[
				    p.to(device=__lowercase			,     dtype=__lowercase   ) if p.is_floating_point() else p.to(device=__lowercase   )
				    for p in self.shadow_params
				]







		def 			__lowercase   (		self   )				->   dict:
				return {
				    "decay": self.decay,
				    "min_decay": self.min_decay,
				    "optimization_step": self.optimization_step,
				    "update_after_step": self.update_after_step,
				    "use_ema_warmup": self.use_ema_warmup,
				    "inv_gamma": self.inv_gamma,
				    "power": self.power,
				    "shadow_params": self.shadow_params,
				}







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : List[str]								=[param.detach().cpu().clone() for param in parameters]







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				if self.temp_stored_params is None:
						raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
				for c_param, param in zip(self.temp_stored_params			,     __lowercase   ):
						param.data.copy_(c_param.data   )

				# Better memory-wise.
				lowerCamelCase   : List[str]								=None







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Union[str, Any]								=copy.deepcopy(__lowercase   )

				lowerCamelCase   : List[str]								=state_dict.get('''decay'''			,     self.decay   )
				if self.decay < 0.0 or self.decay > 1.0:
						raise ValueError('''Decay must be between 0 and 1'''   )

				lowerCamelCase   : int								=state_dict.get('''min_decay'''			,     self.min_decay   )
				if not isinstance(self.min_decay			,     __lowercase   ):
						raise ValueError('''Invalid min_decay'''   )

				lowerCamelCase   : Optional[int]								=state_dict.get('''optimization_step'''			,     self.optimization_step   )
				if not isinstance(self.optimization_step			,     __lowercase   ):
						raise ValueError('''Invalid optimization_step'''   )

				lowerCamelCase   : List[Any]								=state_dict.get('''update_after_step'''			,     self.update_after_step   )
				if not isinstance(self.update_after_step			,     __lowercase   ):
						raise ValueError('''Invalid update_after_step'''   )

				lowerCamelCase   : Optional[int]								=state_dict.get('''use_ema_warmup'''			,     self.use_ema_warmup   )
				if not isinstance(self.use_ema_warmup			,     __lowercase   ):
						raise ValueError('''Invalid use_ema_warmup'''   )

				lowerCamelCase   : Optional[Any]								=state_dict.get('''inv_gamma'''			,     self.inv_gamma   )
				if not isinstance(self.inv_gamma			,     (float, int)   ):
						raise ValueError('''Invalid inv_gamma'''   )

				lowerCamelCase   : List[Any]								=state_dict.get('''power'''			,     self.power   )
				if not isinstance(self.power			,     (float, int)   ):
						raise ValueError('''Invalid power'''   )

				lowerCamelCase   : str								=state_dict.get('''shadow_params'''			,     __lowercase   )
				if shadow_params is not None:
						lowerCamelCase   : Any								=shadow_params
						if not isinstance(self.shadow_params			,     __lowercase   ):
								raise ValueError('''shadow_params must be a list'''   )
						if not all(isinstance(__lowercase			,     torch.Tensor   ) for p in self.shadow_params   ):
								raise ValueError('''shadow_params must all be Tensors'''   )








import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_							=			'''
@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
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

snake_case_							=			'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


snake_case_							=			'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Tuple:

		def remove_articles(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : Tuple								=re.compile(R'''\b(a|an|the)\b'''							,       re.UNICODE		)
				return re.sub(SCREAMING_SNAKE_CASE_							,       ''' '''							,       SCREAMING_SNAKE_CASE_		)

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_		)		)		)		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		return int(normalize_answer(SCREAMING_SNAKE_CASE_		) == normalize_answer(SCREAMING_SNAKE_CASE_		)		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		lowerCamelCase   : Union[str, Any]								=[any(compute_exact(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) for ref in refs		) for pred, refs in zip(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)]
		return (sum(SCREAMING_SNAKE_CASE_		) / len(SCREAMING_SNAKE_CASE_		)) * 1_0_0


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Any								=[rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCamelCase   : int								=Counter(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Dict								=Counter(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Any								=Counter()
		for sgram, scount in sgramcounter.items():
				lowerCamelCase   : Tuple								=scount * numref

		lowerCamelCase   : Optional[int]								=Counter(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Tuple								=Counter()
		for cgram, ccount in cgramcounter.items():
				lowerCamelCase   : Tuple								=ccount * numref

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

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

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

		# ADDITION
		lowerCamelCase   : List[Any]								=set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : int								=set(SCREAMING_SNAKE_CASE_		) & set(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)

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

		return (keepscore, delscore_precision, addscore)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Dict:
		lowerCamelCase   : Optional[int]								=len(SCREAMING_SNAKE_CASE_		)

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

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

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

		for i in range(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		):
				if i < len(SCREAMING_SNAKE_CASE_		) - 1:
						lowerCamelCase   : Optional[int]								=cagrams[i] + ''' ''' + cagrams[i + 1]
						cagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 2:
						lowerCamelCase   : List[str]								=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
						cagrams.append(SCREAMING_SNAKE_CASE_		)
				if i < len(SCREAMING_SNAKE_CASE_		) - 3:
						lowerCamelCase   : str								=cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
						cagrams.append(SCREAMING_SNAKE_CASE_		)

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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = "13a"							,       SCREAMING_SNAKE_CASE_ = True		)   ->     Any:
		# 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:
				lowerCamelCase   : Union[str, Any]								=sentence.lower()

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

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

		return normalized_sent


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:
		if not (len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		)):
				raise ValueError('''Sources length must match predictions and references lengths.'''		)
		lowerCamelCase   : Dict								=0
		for src, pred, refs in zip(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
				sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE_		)							,       normalize(SCREAMING_SNAKE_CASE_		)							,       [normalize(SCREAMING_SNAKE_CASE_		) for sent in refs]		)
		lowerCamelCase   : str								=sari_score / len(SCREAMING_SNAKE_CASE_		)
		return 1_0_0 * sari_score


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_="exp"							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=False							,       )   ->     Dict:
		lowerCamelCase   : Optional[int]								=len(references[0]		)
		if any(len(SCREAMING_SNAKE_CASE_		) != references_per_prediction for refs in references		):
				raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
		lowerCamelCase   : Optional[int]								=[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_		)]
		lowerCamelCase   : Union[str, Any]								=sacrebleu.corpus_bleu(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       smooth_method=SCREAMING_SNAKE_CASE_							,       smooth_value=SCREAMING_SNAKE_CASE_							,       force=SCREAMING_SNAKE_CASE_							,       lowercase=SCREAMING_SNAKE_CASE_							,       use_effective_order=SCREAMING_SNAKE_CASE_							,       )
		return output.score



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







		def 			__lowercase   (		self   )				->   List[str]:
				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			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Tuple:
				lowerCamelCase   : str								={}
				result.update({'''sari''': compute_sari(sources=__lowercase			,     predictions=__lowercase			,     references=__lowercase   )}   )
				result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowercase			,     references=__lowercase   )}   )
				result.update({'''exact''': compute_em(predictions=__lowercase			,     references=__lowercase   )}   )
				return result

