



'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a   :			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\",
}
"""

a   :			Optional[int]					  =    """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


a   :			str					  =    """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. 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       __lowerCamelCase  (					_lowercase		)      ->			str:

							def remove_articles(_lowercase		):
														UpperCAmelCase     :		Tuple							     =		re.compile(R"""\b(a|an|the)\b"""	,				re.UNICODE		)
														return re.sub(_lowerCAmelCase	,				""" """	,				_lowerCAmelCase		)

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase		)		)		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Any:
							return int(normalize_answer(_lowerCAmelCase		) == normalize_answer(_lowerCAmelCase		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Tuple							     =		[any(compute_exact(_lowerCAmelCase	,				_lowerCAmelCase		) for ref in refs		) for pred, refs in zip(_lowerCAmelCase	,				_lowerCAmelCase		)]
							return (sum(_lowerCAmelCase		) / len(_lowerCAmelCase		)) * 1_0_0



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Union[str, Any]							     =		[rgram for rgrams in rgramslist for rgram in rgrams]
							UpperCAmelCase     :		Union[str, Any]							     =		Counter(_lowerCAmelCase		)

							UpperCAmelCase     :		Optional[int]							     =		Counter(_lowerCAmelCase		)
							UpperCAmelCase     :		List[Any]							     =		Counter()
							for sgram, scount in sgramcounter.items():
														UpperCAmelCase     :		Tuple							     =		scount * numref

							UpperCAmelCase     :		Union[str, Any]							     =		Counter(_lowerCAmelCase		)
							UpperCAmelCase     :		Tuple							     =		Counter()
							for cgram, ccount in cgramcounter.items():
														UpperCAmelCase     :		Dict							     =		ccount * numref

							# KEEP
							UpperCAmelCase     :		List[Any]							     =		sgramcounter_rep & cgramcounter_rep
							UpperCAmelCase     :		Union[str, Any]							     =		keepgramcounter_rep & rgramcounter
							UpperCAmelCase     :		Dict							     =		sgramcounter_rep & rgramcounter

							UpperCAmelCase     :		Optional[int]							     =		0
							UpperCAmelCase     :		Tuple							     =		0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							UpperCAmelCase     :		Any							     =		1
							UpperCAmelCase     :		Any							     =		1
							if len(_lowerCAmelCase		) > 0:
														UpperCAmelCase     :		Dict							     =		keeptmpscorea / len(_lowerCAmelCase		)
							if len(_lowerCAmelCase		) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														UpperCAmelCase     :		Union[str, Any]							     =		keeptmpscorea / sum(keepgramcounterall_rep.values()		)
							UpperCAmelCase     :		Any							     =		0
							if keepscore_precision > 0 or keepscore_recall > 0:
														UpperCAmelCase     :		List[str]							     =		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

							# ADDITION
							UpperCAmelCase     :		List[str]							     =		set(_lowerCAmelCase		) - set(_lowerCAmelCase		)
							UpperCAmelCase     :		List[str]							     =		set(_lowerCAmelCase		) & set(_lowerCAmelCase		)
							UpperCAmelCase     :		Dict							     =		set(_lowerCAmelCase		) - set(_lowerCAmelCase		)

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

							return (keepscore, delscore_precision, addscore)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		int							     =		len(_lowerCAmelCase		)

							UpperCAmelCase     :		Optional[Any]							     =		ssent.split(""" """		)
							UpperCAmelCase     :		Dict							     =		csent.split(""" """		)
							UpperCAmelCase     :		str							     =		[]
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Union[str, Any]							     =		[]
							UpperCAmelCase     :		str							     =		[]
							UpperCAmelCase     :		str							     =		[]

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

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



def       __lowerCamelCase  (					_lowercase	,				_lowercase = True	,				_lowercase = "13a"	,				_lowercase = True		)      ->			Optional[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:
														UpperCAmelCase     :		Dict							     =		sentence.lower()

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

							if not return_str:
														UpperCAmelCase     :		Tuple							     =		normalized_sent.split()

							return normalized_sent



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							if not (len(_lowerCAmelCase		) == len(_lowerCAmelCase		) == len(_lowerCAmelCase		)):
														raise ValueError("""Sources length must match predictions and references lengths."""		)
							UpperCAmelCase     :		Optional[Any]							     =		0
							for src, pred, refs in zip(_lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase		):
														sari_score += SARIsent(normalize(_lowerCAmelCase		)	,				normalize(_lowerCAmelCase		)	,				[normalize(_lowerCAmelCase		) for sent in refs]		)
							UpperCAmelCase     :		Optional[int]							     =		sari_score / len(_lowerCAmelCase		)
							return 1_0_0 * sari_score



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


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





	def  _lowercase(  self    )			->			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		,		A		,		A		,		A    )			->			Dict:
								UpperCAmelCase     :		Optional[Any]							     =		{}
								result.update({"""sari""": compute_sari(sources=A_		,		predictions=A_		,		references=A_    )}    )
								result.update({"""sacrebleu""": compute_sacrebleu(predictions=A_		,		references=A_    )}    )
								result.update({"""exact""": compute_em(predictions=A_		,		references=A_    )}    )
								return result








def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					List[Any]       	=   ""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups



def 		A_				(     _lowerCAmelCase     )						-> dict[str, str]:
 UpperCamelCase	:					Optional[Any]       	=   [chr(i + 65     ) for i in range(26     )]
 # Remove duplicate characters from key
 UpperCamelCase	:					Tuple       	=   remove_duplicates(key.upper()     )
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )
 # First fill cipher with key characters
 UpperCamelCase	:					int       	=   {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase     )}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_lowerCAmelCase     )							,   26     ):
  UpperCamelCase	:					Optional[Any]       	=   alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   UpperCamelCase	:					List[str]       	=   alphabet[i - offset]
  UpperCamelCase	:					List[Any]       	=   char
 return cipher_alphabet



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
