






'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase   :					List[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
},
@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",
}
'''

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


__lowerCamelCase   :					List[str]   							=					'''
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 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			def remove_articles(__magic_name__						):
						snake_case_				:					List[Any]   =       re.compile(r"\b(a|an|the)\b"     ,re.UNICODE						)
						return re.sub(__magic_name__     ," "     ,__magic_name__						)

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

			def remove_punc(__magic_name__						):
						snake_case_				:					Tuple   =       set(string.punctuation						)
						return "".join(ch for ch in text if ch not in exclude						)

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

			return white_space_fix(remove_articles(remove_punc(lower(__magic_name__						)						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Dict:



			"""simple docstring"""



			return int(normalize_answer(__magic_name__						) == normalize_answer(__magic_name__						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       [any(compute_exact(__magic_name__     ,__magic_name__						) for ref in refs						) for pred, refs in zip(__magic_name__     ,__magic_name__						)]
			return (sum(__magic_name__						) / len(__magic_name__						)) * 100

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Dict   =       [rgram for rgrams in rgramslist for rgram in rgrams]
			snake_case_				:					Any   =       Counter(__magic_name__						)

			snake_case_				:					Tuple   =       Counter(__magic_name__						)
			snake_case_				:					Optional[Any]   =       Counter()
			for sgram, scount in sgramcounter.items():
						snake_case_				:					Tuple   =       scount * numref

			snake_case_				:					List[str]   =       Counter(__magic_name__						)
			snake_case_				:					Tuple   =       Counter()
			for cgram, ccount in cgramcounter.items():
						snake_case_				:					Any   =       ccount * numref

			# KEEP
			snake_case_				:					Optional[int]   =       sgramcounter_rep & cgramcounter_rep
			snake_case_				:					str   =       keepgramcounter_rep & rgramcounter
			snake_case_				:					str   =       sgramcounter_rep & rgramcounter

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

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

			# ADDITION
			snake_case_				:					Dict   =       set(__magic_name__						) - set(__magic_name__						)
			snake_case_				:					Optional[int]   =       set(__magic_name__						) & set(__magic_name__						)
			snake_case_				:					Dict   =       set(__magic_name__						) - set(__magic_name__						)

			snake_case_				:					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.
			snake_case_				:					Dict   =       1
			snake_case_				:					Optional[Any]   =       1
			if len(__magic_name__						) > 0:
						snake_case_				:					Union[str, Any]   =       addtmpscore / len(__magic_name__						)
			if len(__magic_name__						) > 0:
						snake_case_				:					str   =       addtmpscore / len(__magic_name__						)
			snake_case_				:					Optional[int]   =       0
			if addscore_precision > 0 or addscore_recall > 0:
						snake_case_				:					int   =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       len(__magic_name__						)

			snake_case_				:					Any   =       ssent.split(" "						)
			snake_case_				:					Dict   =       csent.split(" "						)
			snake_case_				:					int   =       []
			snake_case_				:					Tuple   =       []
			snake_case_				:					Union[str, Any]   =       []
			snake_case_				:					int   =       []
			snake_case_				:					int   =       []
			snake_case_				:					int   =       []

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

			for i in range(0     ,len(__magic_name__						) - 1						):
						if i < len(__magic_name__						) - 1:
									snake_case_				:					Optional[Any]   =       sagrams[i] + " " + sagrams[i + 1]
									sagrams.append(__magic_name__						)
						if i < len(__magic_name__						) - 2:
									snake_case_				:					Optional[Any]   =       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
									sagrams.append(__magic_name__						)
						if i < len(__magic_name__						) - 3:
									snake_case_				:					List[str]   =       sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
									sagrams.append(__magic_name__						)

			for i in range(0     ,len(__magic_name__						) - 1						):
						if i < len(__magic_name__						) - 1:
									snake_case_				:					List[Any]   =       cagrams[i] + " " + cagrams[i + 1]
									cagrams.append(__magic_name__						)
						if i < len(__magic_name__						) - 2:
									snake_case_				:					str   =       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
									cagrams.append(__magic_name__						)
						if i < len(__magic_name__						) - 3:
									snake_case_				:					Union[str, Any]   =       cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
									cagrams.append(__magic_name__						)

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = True     ,__magic_name__ = "13a"     ,__magic_name__ = True						)->    List[str]:



			"""simple docstring"""



			if lowercase:
						snake_case_				:					List[str]   =       sentence.lower()

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

			if not return_str:
						snake_case_				:					List[Any]   =       normalized_sent.split()

			return normalized_sent

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			if not (len(__magic_name__						) == len(__magic_name__						) == len(__magic_name__						)):
						raise ValueError("Sources length must match predictions and references lengths."						)
			snake_case_				:					Union[str, Any]   =       0
			for src, pred, refs in zip(__magic_name__     ,__magic_name__     ,__magic_name__						):
						sari_score += SARIsent(normalize(__magic_name__						)     ,normalize(__magic_name__						)     ,[normalize(__magic_name__						) for sent in refs]						)
			snake_case_				:					str   =       sari_score / len(__magic_name__						)
			return 100 * sari_score

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__="exp"     ,__magic_name__=None     ,__magic_name__=False     ,__magic_name__=False     ,__magic_name__=False     ,)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					str   =       len(references[0]						)
			if any(len(__magic_name__						) != references_per_prediction for refs in references						):
						raise ValueError("Sacrebleu requires the same number of references for each prediction"						)
			snake_case_				:					List[Any]   =       [[refs[i] for refs in references] for i in range(__magic_name__						)]
			snake_case_				:					List[Any]   =       sacrebleu.corpus_bleu(
			    __magic_name__     ,__magic_name__     ,smooth_method=__magic_name__     ,smooth_value=__magic_name__     ,force=__magic_name__     ,lowercase=__magic_name__     ,use_effective_order=__magic_name__     ,)
			return output.score







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION							)
class    A_	(datasets.Metric							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  List[Any]:


										'''simple docstring'''




										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 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {}
										result.update({"sari": compute_sari(sources=lowerCAmelCase__       ,							predictions=lowerCAmelCase__       ,							references=lowerCAmelCase__				)}				)
										result.update({"sacrebleu": compute_sacrebleu(predictions=lowerCAmelCase__       ,							references=lowerCAmelCase__				)}				)
										result.update({"exact": compute_em(predictions=lowerCAmelCase__       ,							references=lowerCAmelCase__				)}				)
										return result












'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




