"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__  :  List[Any]						     =	'\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'

lowerCAmelCase__  :  Union[str, Any]						     =	'\\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'


lowerCAmelCase__  :  Union[str, 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       ):

				def remove_articles(lowerCamelCase       ):
								UpperCAmelCase__             =							re.compile(r'\b(a|an|the)\b'					,			re.UNICODE       )
								return re.sub(__lowerCamelCase					,			' '					,			__lowerCamelCase       )

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase       )       )       )       )



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				return int(normalize_answer(__lowerCamelCase       ) == normalize_answer(__lowerCamelCase       )       )



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							[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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							[rgram for rgrams in rgramslist for rgram in rgrams]
				UpperCAmelCase__             =							Counter(__lowerCamelCase       )

				UpperCAmelCase__             =							Counter(__lowerCamelCase       )
				UpperCAmelCase__             =							Counter()
				for sgram, scount in sgramcounter.items():
								UpperCAmelCase__             =							scount * numref

				UpperCAmelCase__             =							Counter(__lowerCamelCase       )
				UpperCAmelCase__             =							Counter()
				for cgram, ccount in cgramcounter.items():
								UpperCAmelCase__             =							ccount * numref

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

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

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

				# ADDITION
				UpperCAmelCase__             =							set(__lowerCamelCase       ) - set(__lowerCamelCase       )
				UpperCAmelCase__             =							set(__lowerCamelCase       ) & set(__lowerCamelCase       )
				UpperCAmelCase__             =							set(__lowerCamelCase       ) - set(__lowerCamelCase       )

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

				return (keepscore, delscore_precision, addscore)



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(__lowerCamelCase       )

				UpperCAmelCase__             =							ssent.split(' '       )
				UpperCAmelCase__             =							csent.split(' '       )
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]

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

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



def   a_						(					lowerCamelCase					,			lowerCamelCase = True					,			lowerCamelCase = "13a"					,			lowerCamelCase = 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:
								UpperCAmelCase__             =							sentence.lower()

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

				if not return_str:
								UpperCAmelCase__             =							normalized_sent.split()

				return normalized_sent



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				if not (len(__lowerCamelCase       ) == len(__lowerCamelCase       ) == len(__lowerCamelCase       )):
								raise ValueError('Sources length must match predictions and references lengths.'       )
				UpperCAmelCase__             =							0
				for src, pred, refs in zip(__lowerCamelCase					,			__lowerCamelCase					,			__lowerCamelCase       ):
								sari_score += SARIsent(normalize(__lowerCamelCase       )					,			normalize(__lowerCamelCase       )					,			[normalize(__lowerCamelCase       ) for sent in refs]       )
				UpperCAmelCase__             =							sari_score / len(__lowerCamelCase       )
				return 1_0_0 * sari_score



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase="exp"					,			lowerCamelCase=None					,			lowerCamelCase=False					,			lowerCamelCase=False					,			lowerCamelCase=False					,			):
				UpperCAmelCase__             =							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__             =							[[refs[i] for refs in references] for i in range(__lowerCamelCase       )]
				UpperCAmelCase__             =							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 				snake_case       ( datasets.Metric				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									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 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[Any]			):
									UpperCAmelCase__             =							{}
									result.update({'sari': compute_sari(sources=snake_case__    ,predictions=snake_case__    ,references=snake_case__			)}			)
									result.update({'sacrebleu': compute_sacrebleu(predictions=snake_case__    ,references=snake_case__			)}			)
									result.update({'exact': compute_em(predictions=snake_case__    ,references=snake_case__			)}			)
									return result





def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]
       snake_case       :   int							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1 or len(__lowerCamelCase    ) <= key:
              return input_string

       for position, character in enumerate(__lowerCamelCase    ):
              snake_case       :   Any							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append(__lowerCamelCase    )
       snake_case       :   List[str]							=      ["".join(__lowerCamelCase    ) for row in temp_grid]
       snake_case       :   Tuple							=      "".join(__lowerCamelCase    )

       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int    ):
       snake_case       :   Dict							=      []
       snake_case       :   Union[str, Any]							=      key - 1

       if key <= 0:
              raise ValueError("Height of grid can't be 0 or negative"    )
       if key == 1:
              return input_string

       snake_case       :   list[list[str]]							=      [[] for _ in range(__lowerCamelCase    )]  # generates template
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   List[str]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Optional[int]							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              temp_grid[num].append("*"    )

       snake_case       :   Tuple							=      0
       for row in temp_grid:  # fills in the characters
              snake_case       :   Union[str, Any]							=      input_string[counter : counter + len(__lowerCamelCase    )]
              grid.append(list(__lowerCamelCase    )    )
              counter += len(__lowerCamelCase    )

       snake_case       :   str							=      ""  # reads as zigzag
       for position in range(len(__lowerCamelCase    )    ):
              snake_case       :   Optional[int]							=      position % (lowest * 2)  # puts it in bounds
              snake_case       :   Tuple							=      min(__lowerCamelCase				,					lowest * 2 - num    )  # creates zigzag pattern
              output_string += grid[num][0]
              grid[num].pop(0    )
       return output_string


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Tuple							=      {}
       for key_guess in range(1				,					len(__lowerCamelCase    )    ):  # tries every key
              snake_case       :   Any							=      decrypt(__lowerCamelCase				,					__lowerCamelCase    )
       return results


if __name__ == "__main__":
   import doctest

   doctest.testmod()





