




"""simple docstring"""

import os
import sys
import time

from . import transposition_cipher as trans_cipher




def       snake_case__	(       ):
					__snake_case          =	"""Prehistoric Men.txt"""
					__snake_case          =	"""Output.txt"""
					__snake_case          =	int(input("""Enter key: """		)		)
					__snake_case          =	input("""Encrypt/Decrypt [e/d]: """		)

					if not os.path.exists(lowerCAmelCase		):
										print(F'''File {input_file} does not exist. Quitting...'''		)
										sys.exit()
					if os.path.exists(lowerCAmelCase		):
										print(F'''Overwrite {output_file}? [y/n]'''		)
										__snake_case          =	input("""> """		)
										if not response.lower().startswith("""y"""		):
															sys.exit()

					__snake_case          =	time.time()
					if mode.lower().startswith("""e"""		):
										with open(lowerCAmelCase		) as f:
															__snake_case          =	f.read()
										__snake_case          =	trans_cipher.encrypt_message(lowerCAmelCase				,					lowerCAmelCase		)
					elif mode.lower().startswith("""d"""		):
										with open(lowerCAmelCase		) as f:
															__snake_case          =	f.read()
										__snake_case          =	trans_cipher.decrypt_message(lowerCAmelCase				,					lowerCAmelCase		)

					with open(lowerCAmelCase				,					"""w"""		) as output_obj:
										output_obj.write(lowerCAmelCase		)

					__snake_case          =	round(time.time() - start_time				,					2		)
					print(("""Done (""", total_time, """seconds )""")		)


if __name__ == "__main__":
		main()








"""simple docstring"""

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase__ :    int 					=							"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

UpperCamelCase__ :    Union[str, Any] 					=							"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\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.\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\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 .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"




def       snake_case__	(       lowerCAmelCase   :		Optional[Any]				,					lowerCAmelCase   :		int				,					lowerCAmelCase   :		int				,					lowerCAmelCase   :		List[str]		):
					__snake_case          =	[rgram for rgrams in rgramslist for rgram in rgrams]
					__snake_case          =	Counter(lowerCAmelCase		)

					__snake_case          =	Counter(lowerCAmelCase		)
					__snake_case          =	Counter()
					for sgram, scount in sgramcounter.items():
										__snake_case          =	scount * numref

					__snake_case          =	Counter(lowerCAmelCase		)
					__snake_case          =	Counter()
					for cgram, ccount in cgramcounter.items():
										__snake_case          =	ccount * numref

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

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

					# DELETION
					__snake_case          =	sgramcounter_rep - cgramcounter_rep
					__snake_case          =	delgramcounter_rep - rgramcounter
					__snake_case          =	sgramcounter_rep - rgramcounter
					__snake_case          =	0
					__snake_case          =	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          =	1
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	deltmpscorea / len(lowerCAmelCase		)

					# ADDITION
					__snake_case          =	set(lowerCAmelCase		) - set(lowerCAmelCase		)
					__snake_case          =	set(lowerCAmelCase		) & set(lowerCAmelCase		)
					__snake_case          =	set(lowerCAmelCase		) - set(lowerCAmelCase		)

					__snake_case          =	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          =	1
					__snake_case          =	1
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	addtmpscore / len(lowerCAmelCase		)
					if len(lowerCAmelCase		) > 0:
										__snake_case          =	addtmpscore / len(lowerCAmelCase		)
					__snake_case          =	0
					if addscore_precision > 0 or addscore_recall > 0:
										__snake_case          =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

					return (keepscore, delscore_precision, addscore)




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		str				,					lowerCAmelCase   :		Union[str, Any]		):
					__snake_case          =	len(lowerCAmelCase		)

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

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

					((__snake_case)      ,      (__snake_case)      ,      (__snake_case))          =	SARIngram(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
					((__snake_case)      ,      (__snake_case)      ,      (__snake_case))          =	SARIngram(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
					((__snake_case)      ,      (__snake_case)      ,      (__snake_case))          =	SARIngram(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
					((__snake_case)      ,      (__snake_case)      ,      (__snake_case))          =	SARIngram(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
					__snake_case          =	sum([keepascore, keepascore, keepascore, keepascore]		) / 4
					__snake_case          =	sum([delascore, delascore, delascore, delascore]		) / 4
					__snake_case          =	sum([addascore, addascore, addascore, addascore]		) / 4
					__snake_case          =	(avgkeepscore + avgdelscore + avgaddscore) / 3
					return finalscore




def       snake_case__	(       lowerCAmelCase   :		List[str]				,					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:
										__snake_case          =	sentence.lower()

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

					if not return_str:
										__snake_case          =	normalized_sent.split()

					return normalized_sent

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




	def 							a_		(   self  :	Tuple	)					-> str:
						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(
						        {
						            """sources""": datasets.Value("""string"""      ,    id="""sequence"""	),
						            """predictions""": datasets.Value("""string"""      ,    id="""sequence"""	),
						            """references""": datasets.Sequence(datasets.Value("""string"""      ,    id="""sequence"""	)      ,    id="""references"""	),
						        }	)      ,    codebase_urls=[
						        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
						        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
						    ]      ,    reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]      ,    )




	def 							a_		(   self  :	Dict      ,    lowercase  :	Optional[int]      ,    lowercase  :	Tuple      ,    lowercase  :	Union[str, Any]	)					-> Tuple:
						if not (len(lowercase	) == len(lowercase	) == len(lowercase	)):
											raise ValueError("""Sources length must match predictions and references lengths."""	)
						__snake_case          =	0
						for src, pred, refs in zip(lowercase      ,    lowercase      ,    lowercase	):
											sari_score += SARIsent(normalize(lowercase	)      ,    normalize(lowercase	)      ,    [normalize(lowercase	) for sent in refs]	)
						__snake_case          =	sari_score / len(lowercase	)
						return {"sari": 100 * sari_score}


