'''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 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__      ,   a__   ):


						'''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 				UpperCamelCase__      (      a__      ,   a__ = True      ,   a__ = "13a"      ,   a__ = True   ):


						'''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 	SCREAMING_SNAKE_CASE      (    datasets.Metric):







		"""simple docstring"""






		def 		UpperCamelCase__							( 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 		UpperCamelCase__							( self		,    __A		,    __A		,    __A					)			->  str:
								if not (len(__A					) == len(__A					) == len(__A					)):
														raise ValueError('Sources length must match predictions and references lengths.'					)
								_lowerCAmelCase					    =0
								for src, pred, refs in zip(__A		,    __A		,    __A					):
														sari_score += SARIsent(normalize(__A					)		,    normalize(__A					)		,    [normalize(__A					) for sent in refs]					)
								_lowerCAmelCase					    =sari_score / len(__A					)
								return {"sari": 100 * sari_score}






'''simple docstring'''

import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


lowercase_  		=			namedtuple('''ExampleNamedTuple''', '''a b c''')
class 	SCREAMING_SNAKE_CASE      (    unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.randn(5		,    2					)
								_lowerCAmelCase					    =torch.device('cuda'					) if torch.cuda.is_available() else torch.device('cpu'					)

								_lowerCAmelCase					    =send_to_device(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.cpu()		,    __A					)					)

								_lowerCAmelCase					    =send_to_device((tensor, [tensor, tensor], 1)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta[0].cpu()		,    __A					)					)
								self.assertIsInstance(resulta[1]		,    __A					)
								self.assertTrue(torch.equal(resulta[1][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta[1][1].cpu()		,    __A					)					)
								self.assertEqual(resulta[2]		,    1					)

								_lowerCAmelCase					    =send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)

								_lowerCAmelCase					    =send_to_device(ExampleNamedTuple(a=__A		,    b=[tensor, tensor]		,    c=1					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.a.cpu()		,    __A					)					)
								self.assertIsInstance(resulta.b		,    __A					)
								self.assertTrue(torch.equal(resulta.b[0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta.b[1].cpu()		,    __A					)					)
								self.assertEqual(resulta.c		,    1					)

								_lowerCAmelCase					    =send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								with self.assertRaises(__A					) as cm:
														_lowerCAmelCase					    =recursively_apply(torch.tensor		,    (torch.tensor(1					), 1)		,    error_on_other_type=__A					)
								self.assertEqual(
								    str(cm.exception					)		,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'		,    )






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.tensor([1, 2, 3, 4, 5]					)
								self.assertEqual(listify(__A					)		,    [1, 2, 3, 4, 5]					)

								_lowerCAmelCase					    =torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
								self.assertEqual(listify(__A					)		,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

								_lowerCAmelCase					    =torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
								self.assertEqual(
								    listify(__A					)		,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								with patch_environment(aa=1		,    BB=2					):
														self.assertEqual(os.environ.get('AA'					)		,    '1'					)
														self.assertEqual(os.environ.get('BB'					)		,    '2'					)

								self.assertNotIn('AA'		,    os.environ					)
								self.assertNotIn('BB'		,    os.environ					)






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		def 		UpperCamelCase__							( self					)			->  Dict:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =torch.compile(model.forward		,    backend='inductor'					)
								_lowerCAmelCase					    =torch.randn(4		,    10					).cuda()
								_lowerCAmelCase					    =model(__A					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(__A		,    __A					)






		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  str:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =torch.compile(__A					)

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =torch.compile(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(compiled_model._orig_mod		,    compiled_model_unwrapped._orig_mod					)






		def 		UpperCamelCase__							( self					)			->  Tuple:
								self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]					)]					)		,    torch.device('cpu'					)					)
								self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]					)}					)		,    torch.device('cpu'					)					)
								self.assertIsNone(find_device([1, 'a']					)					)





