

"""simple docstring"""





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__UpperCamelCase						:   Union[str, Any]					   =	'''\\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						:   Dict					   =	'''\\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						:   Optional[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 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Union[str, Any]						       =			[rgram for rgrams in rgramslist for rgram in rgrams]
	lowerCAmelCase__			:		Any						       =			Counter(UpperCamelCase__				)

	lowerCAmelCase__			:		List[Any]						       =			Counter(UpperCamelCase__				)
	lowerCAmelCase__			:		Optional[Any]						       =			Counter()
	for sgram, scount in sgramcounter.items():
		lowerCAmelCase__			:		Optional[Any]						       =			scount * numref

	lowerCAmelCase__			:		Any						       =			Counter(UpperCamelCase__				)
	lowerCAmelCase__			:		Optional[int]						       =			Counter()
	for cgram, ccount in cgramcounter.items():
		lowerCAmelCase__			:		Optional[int]						       =			ccount * numref

	# KEEP
	lowerCAmelCase__			:		int						       =			sgramcounter_rep & cgramcounter_rep
	lowerCAmelCase__			:		Optional[Any]						       =			keepgramcounter_rep & rgramcounter
	lowerCAmelCase__			:		Optional[int]						       =			sgramcounter_rep & rgramcounter

	lowerCAmelCase__			:		List[str]						       =			0
	lowerCAmelCase__			:		Optional[int]						       =			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__			:		str						       =			1
	lowerCAmelCase__			:		str						       =			1
	if len(UpperCamelCase__				) > 0:
		lowerCAmelCase__			:		Dict						       =			keeptmpscorea / len(UpperCamelCase__				)
	if len(UpperCamelCase__				) > 0:
		# Fix an alleged bug [2] in the keep score computation.
		# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
		lowerCAmelCase__			:		Tuple						       =			keeptmpscorea / sum(keepgramcounterall_rep.values()				)
	lowerCAmelCase__			:		Optional[int]						       =			0
	if keepscore_precision > 0 or keepscore_recall > 0:
		lowerCAmelCase__			:		Optional[int]						       =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

	# DELETION
	lowerCAmelCase__			:		Union[str, Any]						       =			sgramcounter_rep - cgramcounter_rep
	lowerCAmelCase__			:		str						       =			delgramcounter_rep - rgramcounter
	lowerCAmelCase__			:		int						       =			sgramcounter_rep - rgramcounter
	lowerCAmelCase__			:		Tuple						       =			0
	lowerCAmelCase__			:		Optional[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.
	lowerCAmelCase__			:		Optional[int]						       =			1
	if len(UpperCamelCase__				) > 0:
		lowerCAmelCase__			:		Union[str, Any]						       =			deltmpscorea / len(UpperCamelCase__				)

	# ADDITION
	lowerCAmelCase__			:		Optional[Any]						       =			set(UpperCamelCase__				) - set(UpperCamelCase__				)
	lowerCAmelCase__			:		Any						       =			set(UpperCamelCase__				) & set(UpperCamelCase__				)
	lowerCAmelCase__			:		Optional[Any]						       =			set(UpperCamelCase__				) - set(UpperCamelCase__				)

	lowerCAmelCase__			:		Dict						       =			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__			:		int						       =			1
	lowerCAmelCase__			:		str						       =			1
	if len(UpperCamelCase__				) > 0:
		lowerCAmelCase__			:		Dict						       =			addtmpscore / len(UpperCamelCase__				)
	if len(UpperCamelCase__				) > 0:
		lowerCAmelCase__			:		Dict						       =			addtmpscore / len(UpperCamelCase__				)
	lowerCAmelCase__			:		str						       =			0
	if addscore_precision > 0 or addscore_recall > 0:
		lowerCAmelCase__			:		Optional[int]						       =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Dict						       =			len(UpperCamelCase__				)

	lowerCAmelCase__			:		Dict						       =			ssent.split(''' '''				)
	lowerCAmelCase__			:		Union[str, Any]						       =			csent.split(''' '''				)
	lowerCAmelCase__			:		List[str]						       =			[]
	lowerCAmelCase__			:		Union[str, Any]						       =			[]
	lowerCAmelCase__			:		List[str]						       =			[]
	lowerCAmelCase__			:		Any						       =			[]
	lowerCAmelCase__			:		Dict						       =			[]
	lowerCAmelCase__			:		Dict						       =			[]

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

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

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_ = True		,  A_ = "13a"		,  A_ = 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__			:		Optional[int]						       =			sentence.lower()

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

	if not return_str:
		lowerCAmelCase__			:		Dict						       =			normalized_sent.split()

	return normalized_sent







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






					"""simple docstring"""



					def  __lowerCAmelCase				( self    :       int	):
						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  __lowerCAmelCase				( self    :       str     ,lowercase_    :       Tuple     ,lowercase_    :       Union[str, Any]     ,lowercase_    :       str	):
						if not (len(__lowerCamelCase	) == len(__lowerCamelCase	) == len(__lowerCamelCase	)):
							raise ValueError('''Sources length must match predictions and references lengths.'''	)
						lowerCAmelCase__			:		List[Any]						       =			0
						for src, pred, refs in zip(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase	):
							sari_score += SARIsent(normalize(__lowerCamelCase	)     ,normalize(__lowerCamelCase	)     ,[normalize(__lowerCamelCase	) for sent in refs]	)
						lowerCAmelCase__			:		Union[str, Any]						       =			sari_score / len(__lowerCamelCase	)
						return {"sari": 1_0_0 * sari_score}







"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


if __name__ == "__main__":
							# read original image
							__UpperCamelCase						:   Dict							   =	imread('''../image_data/lena.jpg''')
							# turn image in gray scale value
							__UpperCamelCase						:   Optional[Any]							   =	cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)




