





'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class        snake_case_		(			UpperCamelCase__):
		lowerCamelCase    :List[Any]						=     "gptj"
		lowerCamelCase    :Dict						=     {
		    "max_position_embeddings": "n_positions",
		    "hidden_size": "n_embd",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}







		def __init__(		self			,     __lowercase=5_0_4_0_0			,     __lowercase=2_0_4_8			,     __lowercase=4_0_9_6			,     __lowercase=2_8			,     __lowercase=1_6			,     __lowercase=6_4			,     __lowercase=None			,     __lowercase="gelu_new"			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=1e-5			,     __lowercase=0.0_2			,     __lowercase=True			,     __lowercase=5_0_2_5_6			,     __lowercase=5_0_2_5_6			,     __lowercase=False			,     **__lowercase			,     )				->   Optional[int]:
				lowerCamelCase   : Union[str, Any]								=vocab_size
				lowerCamelCase   : Optional[int]								=n_positions
				lowerCamelCase   : Union[str, Any]								=n_embd
				lowerCamelCase   : Any								=n_layer
				lowerCamelCase   : Tuple								=n_head
				lowerCamelCase   : List[str]								=n_inner
				lowerCamelCase   : Tuple								=rotary_dim
				lowerCamelCase   : Tuple								=activation_function
				lowerCamelCase   : Union[str, Any]								=resid_pdrop
				lowerCamelCase   : List[Any]								=embd_pdrop
				lowerCamelCase   : Any								=attn_pdrop
				lowerCamelCase   : str								=layer_norm_epsilon
				lowerCamelCase   : Optional[Any]								=initializer_range
				lowerCamelCase   : Optional[int]								=use_cache

				lowerCamelCase   : Optional[Any]								=bos_token_id
				lowerCamelCase   : Dict								=eos_token_id

				super().__init__(
				    bos_token_id=__A			,     eos_token_id=__A			,     tie_word_embeddings=__A			,     **__A   )

class        snake_case_		(			UpperCamelCase__):







		def __init__(		self			,     __lowercase			,     __lowercase = "default"			,     __lowercase = None			,     __lowercase = False			,     )				->   List[str]:
				super().__init__(__A			,     task=__A			,     patching_specs=__A			,     use_past=__A   )
				if not getattr(self._config			,     '''pad_token_id'''			,     __A   ):
						# TODO: how to do that better?
						lowerCamelCase   : Any								=0







		@property
		def 			__lowercase   (		self   )				->   Mapping[str, Mapping[int, str]]:
				lowerCamelCase   : str								=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
				if self.use_past:
						self.fill_with_past_key_values_(__A			,     direction='''inputs'''   )
						lowerCamelCase   : Union[str, Any]								={0: '''batch''', 1: '''past_sequence + sequence'''}
				else:
						lowerCamelCase   : Any								={0: '''batch''', 1: '''sequence'''}

				return common_inputs







		@property
		def 			__lowercase   (		self   )				->   int:
				return self._config.n_layer







		@property
		def 			__lowercase   (		self   )				->   int:
				return self._config.n_head







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = -1			,     __lowercase = -1			,     __lowercase = False			,     __lowercase = None			,     )				->   Mapping[str, Any]:
				lowerCamelCase   : Dict								=super(__A			,     self   ).generate_dummy_inputs(
				    __A			,     batch_size=__A			,     seq_length=__A			,     is_pair=__A			,     framework=__A   )

				# We need to order the input in the way they appears in the forward()
				lowerCamelCase   : Union[str, Any]								=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

				# Need to add the past_keys
				if self.use_past:
						if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
						else:
								import torch

								lowerCamelCase		,   lowerCamelCase   : Optional[int]								=common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCamelCase   : Tuple								=seqlen + 2
								lowerCamelCase   : List[str]								=(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCamelCase   : Dict								=[
								    (torch.zeros(__A   ), torch.zeros(__A   )) for _ in range(self.num_layers   )
								]

				lowerCamelCase   : int								=common_inputs['''attention_mask''']
				if self.use_past:
						lowerCamelCase   : int								=ordered_inputs['''attention_mask'''].dtype
						lowerCamelCase   : str								=torch.cat(
						    [ordered_inputs['''attention_mask'''], torch.ones(__A			,     __A			,     dtype=__A   )]			,     dim=1   )

				return ordered_inputs







		@property
		def 			__lowercase   (		self   )				->   int:
				return 1_3








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

