






'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase    :      Optional[int]  			=		'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCamelCase    :      Dict  			=		'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


UpperCamelCase    :      List[Any]  			=		'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def 						A__			(				__lowerCAmelCase		:			Optional[int]						):

						def remove_articles(__lowerCAmelCase		:			Optional[Any]						):
												lowerCamelCase__         =  re.compile(R"""\b(a|an|the)\b"""   ,    re.UNICODE						)
												return re.sub(__lowerCAmelCase   ,    """ """   ,    __lowerCAmelCase						)

						def white_space_fix(__lowerCAmelCase		:			int						):
												return " ".join(text.split()						)

						def remove_punc(__lowerCAmelCase		:			Optional[Any]						):
												lowerCamelCase__         =  set(string.punctuation						)
												return "".join(ch for ch in text if ch not in exclude						)

						def lower(__lowerCAmelCase		:			str						):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase						)						)						)						)
def 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			int						):
						return int(normalize_answer(__lowerCAmelCase						) == normalize_answer(__lowerCAmelCase						)						)
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  [any(compute_exact(__lowerCAmelCase   ,    __lowerCAmelCase						) for ref in refs						) for pred, refs in zip(__lowerCAmelCase   ,    __lowerCAmelCase						)]
						return (sum(__lowerCAmelCase						) / len(__lowerCAmelCase						)) * 100
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  [rgram for rgrams in rgramslist for rgram in rgrams]
						lowerCamelCase__         =  Counter(__lowerCAmelCase						)

						lowerCamelCase__         =  Counter(__lowerCAmelCase						)
						lowerCamelCase__         =  Counter()
						for sgram, scount in sgramcounter.items():
												lowerCamelCase__         =  scount * numref

						lowerCamelCase__         =  Counter(__lowerCAmelCase						)
						lowerCamelCase__         =  Counter()
						for cgram, ccount in cgramcounter.items():
												lowerCamelCase__         =  ccount * numref

						# KEEP
						lowerCamelCase__         =  sgramcounter_rep & cgramcounter_rep
						lowerCamelCase__         =  keepgramcounter_rep & rgramcounter
						lowerCamelCase__         =  sgramcounter_rep & rgramcounter

						lowerCamelCase__         =  0
						lowerCamelCase__         =  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__         =  1
						lowerCamelCase__         =  1
						if len(__lowerCAmelCase						) > 0:
												lowerCamelCase__         =  keeptmpscorea / len(__lowerCAmelCase						)
						if len(__lowerCAmelCase						) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												lowerCamelCase__         =  keeptmpscorea / sum(keepgramcounterall_rep.values()						)
						lowerCamelCase__         =  0
						if keepscore_precision > 0 or keepscore_recall > 0:
												lowerCamelCase__         =  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						lowerCamelCase__         =  sgramcounter_rep - cgramcounter_rep
						lowerCamelCase__         =  delgramcounter_rep - rgramcounter
						lowerCamelCase__         =  sgramcounter_rep - rgramcounter
						lowerCamelCase__         =  0
						lowerCamelCase__         =  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__         =  1
						if len(__lowerCAmelCase						) > 0:
												lowerCamelCase__         =  deltmpscorea / len(__lowerCAmelCase						)

						# ADDITION
						lowerCamelCase__         =  set(__lowerCAmelCase						) - set(__lowerCAmelCase						)
						lowerCamelCase__         =  set(__lowerCAmelCase						) & set(__lowerCAmelCase						)
						lowerCamelCase__         =  set(__lowerCAmelCase						) - set(__lowerCAmelCase						)

						lowerCamelCase__         =  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__         =  1
						lowerCamelCase__         =  1
						if len(__lowerCAmelCase						) > 0:
												lowerCamelCase__         =  addtmpscore / len(__lowerCAmelCase						)
						if len(__lowerCAmelCase						) > 0:
												lowerCamelCase__         =  addtmpscore / len(__lowerCAmelCase						)
						lowerCamelCase__         =  0
						if addscore_precision > 0 or addscore_recall > 0:
												lowerCamelCase__         =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)
def 						A__			(				__lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			int						):
						lowerCamelCase__         =  len(__lowerCAmelCase						)

						lowerCamelCase__         =  ssent.split(""" """						)
						lowerCamelCase__         =  csent.split(""" """						)
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []

						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						lowerCamelCase__         =  []
						for rsent in rsents:
												lowerCamelCase__         =  rsent.split(""" """						)
												lowerCamelCase__         =  []
												lowerCamelCase__         =  []
												lowerCamelCase__         =  []
												ragramslist.append(__lowerCAmelCase						)
												for i in range(0   ,    len(__lowerCAmelCase						) - 1						):
																		if i < len(__lowerCAmelCase						) - 1:
																								lowerCamelCase__         =  ragrams[i] + """ """ + ragrams[i + 1]
																								ragrams.append(__lowerCAmelCase						)
																		if i < len(__lowerCAmelCase						) - 2:
																								lowerCamelCase__         =  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																								ragrams.append(__lowerCAmelCase						)
																		if i < len(__lowerCAmelCase						) - 3:
																								lowerCamelCase__         =  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:
																		lowerCamelCase__         =  sagrams[i] + """ """ + sagrams[i + 1]
																		sagrams.append(__lowerCAmelCase						)
												if i < len(__lowerCAmelCase						) - 2:
																		lowerCamelCase__         =  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																		sagrams.append(__lowerCAmelCase						)
												if i < len(__lowerCAmelCase						) - 3:
																		lowerCamelCase__         =  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:
																		lowerCamelCase__         =  cagrams[i] + """ """ + cagrams[i + 1]
																		cagrams.append(__lowerCAmelCase						)
												if i < len(__lowerCAmelCase						) - 2:
																		lowerCamelCase__         =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																		cagrams.append(__lowerCAmelCase						)
												if i < len(__lowerCAmelCase						) - 3:
																		lowerCamelCase__         =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																		cagrams.append(__lowerCAmelCase						)

						((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  SARIngram(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  SARIngram(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  SARIngram(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						((lowerCamelCase__)       ,			(lowerCamelCase__)       ,			(lowerCamelCase__))         =  SARIngram(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						lowerCamelCase__         =  sum([keepascore, keepascore, keepascore, keepascore]						) / 4
						lowerCamelCase__         =  sum([delascore, delascore, delascore, delascore]						) / 4
						lowerCamelCase__         =  sum([addascore, addascore, addascore, addascore]						) / 4
						lowerCamelCase__         =  (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			bool = True   ,    __lowerCAmelCase		:			str = "13a"   ,    __lowerCAmelCase		:			bool = 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:
												lowerCamelCase__         =  sentence.lower()

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

						if not return_str:
												lowerCamelCase__         =  normalized_sent.split()

						return normalized_sent
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Union[str, Any]						):
						if not (len(__lowerCAmelCase						) == len(__lowerCAmelCase						) == len(__lowerCAmelCase						)):
												raise ValueError("""Sources length must match predictions and references lengths."""						)
						lowerCamelCase__         =  0
						for src, pred, refs in zip(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						):
												sari_score += SARIsent(normalize(__lowerCAmelCase						)   ,    normalize(__lowerCAmelCase						)   ,    [normalize(__lowerCAmelCase						) for sent in refs]						)
						lowerCamelCase__         =  sari_score / len(__lowerCAmelCase						)
						return 100 * sari_score
def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			List[str]="exp"   ,    __lowerCAmelCase		:			Optional[Any]=None   ,    __lowerCAmelCase		:			Dict=False   ,    __lowerCAmelCase		:			List[str]=False   ,    __lowerCAmelCase		:			str=False   ,    ):
						lowerCamelCase__         =  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"""						)
						lowerCamelCase__         =  [[refs[i] for refs in references] for i in range(__lowerCAmelCase						)]
						lowerCamelCase__         =  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			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  {}
								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 __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

UpperCamelCase    :      str  			=		(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase    :      list[int]  			=		[ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase    :      set[int]  			=		{ord(char) for char in VALID_CHARS}

UpperCamelCase    :      list[str]  			=		["the", "be", "to", "of", "and", "in", "that", "have"]
def 						A__			(				__lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			tuple[int, ...]						):
						lowerCamelCase__         =  ""
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42

						for keychar, cipherchar in zip(cycle(__lowerCAmelCase						)   ,    __lowerCAmelCase						):
												lowerCamelCase__         =  cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowerCAmelCase						)

						return decoded
def 						A__			(				__lowerCAmelCase		:			list[int]						):
						lowerCamelCase__         =  []
						for key in product(__lowerCAmelCase   ,    repeat=3						):
												lowerCamelCase__         =  try_key(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if encoded is not None:
																		possibles.append(__lowerCAmelCase						)
						return possibles
def 						A__			(				__lowerCAmelCase		:			list[str]   ,    __lowerCAmelCase		:			str						):
						return [possible for possible in possibles if common_word in possible.lower()]
def 						A__			(				__lowerCAmelCase		:			str = "p059_cipher.txt"						):
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  Path(__lowerCAmelCase						).parent.joinpath(__lowerCAmelCase						).read_text(encoding="""utf-8"""						)

						lowerCamelCase__         =  [int(__lowerCAmelCase						) for number in data.strip().split(""","""						)]

						lowerCamelCase__         =  filter_valid_chars(__lowerCAmelCase						)
						for common_word in COMMON_WORDS:
												lowerCamelCase__         =  filter_common_word(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if len(__lowerCAmelCase						) == 1:
																		break

						lowerCamelCase__         =  possibles[0]
						return sum(ord(__lowerCAmelCase						) for char in decoded_text						)


if __name__ == "__main__":
							print(F'{solution() = }')

