"""simple docstring"""




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase											=  "\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											=  "\\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											=  "\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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__				)				)				)				)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							return int(normalize_answer(UpperCamelCase__				) == normalize_answer(UpperCamelCase__				)				)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[any(compute_exact(UpperCamelCase__       ,    UpperCamelCase__				) for ref in refs				) for pred, refs in zip(UpperCamelCase__       ,    UpperCamelCase__				)]
							return (sum(UpperCamelCase__				) / len(UpperCamelCase__				)) * 100






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[rgram for rgrams in rgramslist for rgram in rgrams]
							A__	     =							Counter(UpperCamelCase__				)

							A__	     =							Counter(UpperCamelCase__				)
							A__	     =							Counter()
							for sgram, scount in sgramcounter.items():
														A__	     =							scount * numref

							A__	     =							Counter(UpperCamelCase__				)
							A__	     =							Counter()
							for cgram, ccount in cgramcounter.items():
														A__	     =							ccount * numref

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

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

							# DELETION
							A__	     =							sgramcounter_rep - cgramcounter_rep
							A__	     =							delgramcounter_rep - rgramcounter
							A__	     =							sgramcounter_rep - rgramcounter
							A__	     =							0
							A__	     =							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.
							A__	     =							1
							if len(UpperCamelCase__				) > 0:
														A__	     =							deltmpscorea / len(UpperCamelCase__				)

							# ADDITION
							A__	     =							set(UpperCamelCase__				) - set(UpperCamelCase__				)
							A__	     =							set(UpperCamelCase__				) & set(UpperCamelCase__				)
							A__	     =							set(UpperCamelCase__				) - set(UpperCamelCase__				)

							A__	     =							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.
							A__	     =							1
							A__	     =							1
							if len(UpperCamelCase__				) > 0:
														A__	     =							addtmpscore / len(UpperCamelCase__				)
							if len(UpperCamelCase__				) > 0:
														A__	     =							addtmpscore / len(UpperCamelCase__				)
							A__	     =							0
							if addscore_precision > 0 or addscore_recall > 0:
														A__	     =							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							len(UpperCamelCase__				)

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

							A__	     =							[]
							A__	     =							[]
							A__	     =							[]
							A__	     =							[]
							for rsent in rsents:
														A__	     =							rsent.split(' '				)
														A__	     =							[]
														A__	     =							[]
														A__	     =							[]
														ragramslist.append(UpperCamelCase__				)
														for i in range(0       ,    len(UpperCamelCase__				) - 1				):
																					if i < len(UpperCamelCase__				) - 1:
																												A__	     =							ragrams[i] + ' ' + ragrams[i + 1]
																												ragrams.append(UpperCamelCase__				)
																					if i < len(UpperCamelCase__				) - 2:
																												A__	     =							ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																												ragrams.append(UpperCamelCase__				)
																					if i < len(UpperCamelCase__				) - 3:
																												A__	     =							ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																												ragrams.append(UpperCamelCase__				)
														ragramslist.append(UpperCamelCase__				)
														ragramslist.append(UpperCamelCase__				)
														ragramslist.append(UpperCamelCase__				)

							for i in range(0       ,    len(UpperCamelCase__				) - 1				):
														if i < len(UpperCamelCase__				) - 1:
																					A__	     =							sagrams[i] + ' ' + sagrams[i + 1]
																					sagrams.append(UpperCamelCase__				)
														if i < len(UpperCamelCase__				) - 2:
																					A__	     =							sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
																					sagrams.append(UpperCamelCase__				)
														if i < len(UpperCamelCase__				) - 3:
																					A__	     =							sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
																					sagrams.append(UpperCamelCase__				)

							for i in range(0       ,    len(UpperCamelCase__				) - 1				):
														if i < len(UpperCamelCase__				) - 1:
																					A__	     =							cagrams[i] + ' ' + cagrams[i + 1]
																					cagrams.append(UpperCamelCase__				)
														if i < len(UpperCamelCase__				) - 2:
																					A__	     =							cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
																					cagrams.append(UpperCamelCase__				)
														if i < len(UpperCamelCase__				) - 3:
																					A__	     =							cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
																					cagrams.append(UpperCamelCase__				)

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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__ = True       ,    UpperCamelCase__ = "13a"       ,    UpperCamelCase__ = True				):






							"""simple docstring"""





							if lowercase:
														A__	     =							sentence.lower()

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

							if not return_str:
														A__	     =							normalized_sent.split()

							return normalized_sent






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__="exp"       ,    UpperCamelCase__=None       ,    UpperCamelCase__=False       ,    UpperCamelCase__=False       ,    UpperCamelCase__=False       ,    ):






							"""simple docstring"""





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


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





						def 				snake_case__	(       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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Any:
													A__	     =							{}
													result.update({'sari': compute_sari(sources=__UpperCAmelCase		,predictions=__UpperCAmelCase		,references=__UpperCAmelCase     )}     )
													result.update({'sacrebleu': compute_sacrebleu(predictions=__UpperCAmelCase		,references=__UpperCAmelCase     )}     )
													result.update({'exact': compute_em(predictions=__UpperCAmelCase		,references=__UpperCAmelCase     )}     )
													return result






"""simple docstring"""




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				float:
													return 0.0






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							A__	     =							max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.abs(np.fft.fft(UpperCamelCase__				)				)
							A__	     =							20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24       ,    samplerate / 2 - 1				)
							plt.xlabel('Frequency (Hz)'				)
							plt.xscale('log'				)

							# Display within reasonable bounds
							A__	     =							get_bounds(UpperCamelCase__       ,    UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)       ,    min([80, bounds[1]]				)				)
							plt.ylabel('Gain (dB)'				)

							plt.plot(UpperCamelCase__				)
							plt.show()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24       ,    samplerate / 2 - 1				)
							plt.xlabel('Frequency (Hz)'				)
							plt.xscale('log'				)

							plt.ylim(-2 * pi       ,    2 * pi				)
							plt.ylabel('Phase shift (Radians)'				)
							plt.plot(np.unwrap(UpperCamelCase__       ,    -2 * pi				)				)
							plt.show()





