






"""simple docstring"""
# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


lowerCAmelCase__							= logging.get_logger(__name__)


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset

			from transformers import Trainer


			class _lowerCAmelCase	(						__UpperCAmelCase					):







									def __init__(						self      ,							lowerCAmelCase_ = 1_0_1       )		->       Optional[Any]:
													_SCREAMING_SNAKE_CASE      :		Tuple							=						length







									def __len__(						self       )		->       str:
													return self.length







									def __getitem__(						self      ,							lowerCAmelCase_       )		->       int:
													return i







			class _lowerCAmelCase	:







									def __call__(						self      ,							lowerCAmelCase_       )		->       Union[str, Any]:
													return {"input_ids": torch.tensor(lowerCAmelCase_       ), "labels": torch.tensor(lowerCAmelCase_       )}







			class _lowerCAmelCase	(						nn.Module					):







									def __init__(						self       )		->       Optional[Any]:
													super().__init__()
													# Add some (unused) params otherwise DDP will complain.
													_SCREAMING_SNAKE_CASE      :		str							=						nn.Linear(1_2_0      ,							8_0       )







									def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=None       )		->       Any:
													if labels is not None:
																	return torch.tensor(0.0      ,							device=input_ids.device       ), input_ids
													else:
																	return input_ids





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		int							=						HfArgumentParser((TrainingArguments,)					)
				sys.argv += ["--output_dir", "./examples"]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						parser.parse_args_into_dataclasses()[0]

				logger.warning(
				    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
				    f"""tpu_num_cores: {training_args.tpu_num_cores}""",	)

				# Essentially, what we want to verify in the distributed case is
				# that we get all samples back, in the right order.
				# (this is crucial for prediction for instance)
				for dataset_length in [1_001, 256, 15]:
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						DummyDataset(lowerCamelCase					)

								def compute_metrics(lowerCamelCase					) -> Dict:
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						list(range(len(lowerCamelCase					)					)					)
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
												return {"success": success}

								_SCREAMING_SNAKE_CASE      :		str							=						Trainer(
								    model=DummyModel(),	args=lowerCamelCase,	data_collator=DummyDataCollator(),	eval_dataset=lowerCamelCase,	compute_metrics=lowerCamelCase,	)
								_SCREAMING_SNAKE_CASE      :		List[str]							=						trainer.evaluate()
								logger.info(lowerCamelCase					)
								if metrics["eval_success"] is not True:
												logger.error(lowerCamelCase					)
												exit(1					)

								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						trainer.predict(lowerCamelCase					)
								logger.info(p.metrics					)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics					)
												exit(1					)

								_SCREAMING_SNAKE_CASE      :		str							=						2

								_SCREAMING_SNAKE_CASE      :		Any							=						trainer.evaluate()
								logger.info(lowerCamelCase					)
								if metrics["eval_success"] is not True:
												logger.error(lowerCamelCase					)
												exit(1					)

								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						trainer.predict(lowerCamelCase					)
								logger.info(p.metrics					)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics					)
												exit(1					)

								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						None

				logger.info('🔥 All distributed tests successful'					)





def 							lowercase__		(				lowerCamelCase					):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()









"""simple docstring"""
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase__							= '\\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'

lowerCAmelCase__							= '\\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'


lowerCAmelCase__							= '\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 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		int							=						[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE      :		Any							=						Counter(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		int							=						Counter()
				for sgram, scount in sgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						scount * numref

				_SCREAMING_SNAKE_CASE      :		Dict							=						Counter(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ccount * numref

				# KEEP
				_SCREAMING_SNAKE_CASE      :		List[str]							=						sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE      :		str							=						keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE      :		Tuple							=						sgramcounter_rep & rgramcounter

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

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

				# ADDITION
				_SCREAMING_SNAKE_CASE      :		Any							=						set(lowerCamelCase					) - set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						set(lowerCamelCase					) & set(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						set(lowerCamelCase					) - set(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						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.
				_SCREAMING_SNAKE_CASE      :		Tuple							=						1
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						1
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				if len(lowerCamelCase					) > 0:
								_SCREAMING_SNAKE_CASE      :		str							=						addtmpscore / len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						0
				if addscore_precision > 0 or addscore_recall > 0:
								_SCREAMING_SNAKE_CASE      :		Dict							=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						len(lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Tuple							=						ssent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						csent.split(' '					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		List[str]							=						[]
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[]

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

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





def 							lowercase__		(				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:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						sentence.lower()

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

				if not return_str:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						normalized_sent.split()

				return normalized_sent






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







						def  A			(						self       )		->       Dict:
										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_       )		->       Optional[int]:
										if not (len(lowerCAmelCase_       ) == len(lowerCAmelCase_       ) == len(lowerCAmelCase_       )):
														raise ValueError('Sources length must match predictions and references lengths.'       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						0
										for src, pred, refs in zip(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       ):
														sari_score += SARIsent(normalize(lowerCAmelCase_       )      ,							normalize(lowerCAmelCase_       )      ,							[normalize(lowerCAmelCase_       ) for sent in refs]       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						sari_score / len(lowerCAmelCase_       )
										return {"sari": 1_0_0 * sari_score}

