'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowercase				   =  '''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

__lowercase				   =  '''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


__lowercase				   =  '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''






def     snake_case__							(      _A: Any				,       _A: str				,       _A: Optional[int]				,       _A: int			)   ->     Tuple:

		'''simple docstring'''


		lowerCAmelCase        =  [rgram for rgrams in rgramslist for rgram in rgrams]
		lowerCAmelCase        =  Counter(_A			)

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

		lowerCAmelCase        =  Counter(_A			)
		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(_A			) > 0:
				lowerCAmelCase        =  keeptmpscorea / len(_A			)
		if len(_A			) > 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(_A			) > 0:
				lowerCAmelCase        =  deltmpscorea / len(_A			)

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

		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(_A			) > 0:
				lowerCAmelCase        =  addtmpscore / len(_A			)
		if len(_A			) > 0:
				lowerCAmelCase        =  addtmpscore / len(_A			)
		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     snake_case__							(      _A: int				,       _A: Optional[Any]				,       _A: str			)   ->     List[str]:

		'''simple docstring'''


		lowerCAmelCase        =  len(_A			)

		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(_A			)
				for i in range(0				,       len(_A			) - 1			):
						if i < len(_A			) - 1:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1]
								ragrams.append(_A			)
						if i < len(_A			) - 2:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
								ragrams.append(_A			)
						if i < len(_A			) - 3:
								lowerCAmelCase        =  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
								ragrams.append(_A			)
				ragramslist.append(_A			)
				ragramslist.append(_A			)
				ragramslist.append(_A			)

		for i in range(0				,       len(_A			) - 1			):
				if i < len(_A			) - 1:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1]
						sagrams.append(_A			)
				if i < len(_A			) - 2:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
						sagrams.append(_A			)
				if i < len(_A			) - 3:
						lowerCAmelCase        =  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
						sagrams.append(_A			)

		for i in range(0				,       len(_A			) - 1			):
				if i < len(_A			) - 1:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1]
						cagrams.append(_A			)
				if i < len(_A			) - 2:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
						cagrams.append(_A			)
				if i < len(_A			) - 3:
						lowerCAmelCase        =  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
						cagrams.append(_A			)

		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		((lowerCAmelCase) ,					(lowerCAmelCase) ,					(lowerCAmelCase))        =  SARIngram(_A				,       _A				,       _A				,       _A			)
		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     snake_case__							(      _A: Optional[int]				,       _A: bool = True				,       _A: str = "13a"				,       _A: bool = True			)   ->     Union[str, Any]:

		'''simple docstring'''


		if lowercase:
				lowerCAmelCase        =  sentence.lower()

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

		if not return_str:
				lowerCAmelCase        =  normalized_sent.split()

		return normalized_sent






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


			'''simple docstring'''






			def 							a_	(       self):




					"""simple docstring"""


					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	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					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 {"sari": 100 * sari_score}





'''simple docstring'''




from __future__ import annotations

__lowercase				   =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  -1
					lowerCAmelCase        =  -1
					lowerCAmelCase        =  [-1] * length
					lowerCAmelCase        =  None
					lowerCAmelCase        =  None







			def __repr__(       self):




					"""simple docstring"""


					return f"Node(min_value={self.minn} max_value={self.maxx})"







def     snake_case__							(      _A: list[int]			)   ->     Node | None:

		'''simple docstring'''


		lowerCAmelCase        =  Node(len(_A			)			)
		lowerCAmelCase ,					lowerCAmelCase        =  min(_A			), max(_A			)
		# Leaf node case where the node contains only one unique value
		if root.minn == root.maxx:
				return root
		lowerCAmelCase        =  (root.minn + root.maxx) // 2

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		for index, num in enumerate(_A			):
				if num <= pivot:
						left_arr.append(_A			)
				else:
						right_arr.append(_A			)
				lowerCAmelCase        =  len(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		return root






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index < 0 or node is None:
				return 0
		# Leaf node cases
		if node.minn == node.maxx:
				return index + 1 if node.minn == num else 0
		lowerCAmelCase        =  (node.minn + node.maxx) // 2
		if num <= pivot:
				# go the left subtree and map index to the left subtree
				return rank_till_index(node.left				,       _A				,       node.map_left[index] - 1			)
		else:
				# go to the right subtree and map index to the right subtree
				return rank_till_index(node.right				,       _A				,       index - node.map_left[index]			)






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if start > end:
				return 0
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       _A			)
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       start - 1			)
		return rank_till_end - rank_before_start






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index > (end - start) or start > end or node is None:
				return -1
		# Leaf node case
		if node.minn == node.maxx:
				return node.minn
		# Number of elements in the left subtree in interval [start, end]
		lowerCAmelCase        =  node.map_left[end] - (
		    node.map_left[start - 1] if start else 0
		)
		if num_elements_in_left_tree > index:
				return quantile(
				    node.left				,       _A				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       )
		else:
				return quantile(
				    node.right				,       index - num_elements_in_left_tree				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       )






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if (
		    start > end
		    or node is None
		    or start_num > end_num
		    or node.minn > end_num
		    or node.maxx < start_num
		):
				return 0
		if start_num <= node.minn and node.maxx <= end_num:
				return end - start + 1
		lowerCAmelCase        =  range_counting(
		    node.left				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       _A				,       _A				,       )
		lowerCAmelCase        =  range_counting(
		    node.right				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       _A				,       _A				,       )
		return left + right


if __name__ == "__main__":
						import doctest

						doctest.testmod()




