



import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__A             ="\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n"

__A             ="\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n"


__A             ="\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric(\"frugalscore\")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results[\"scores\"]])\n    [0.631, 0.645]\n"

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




			'''simple docstring'''






			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					return datasets.MetricInfo(
					    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
					        {
					            '''predictions''': datasets.Value('''string'''			),
					            '''references''': datasets.Value('''string'''			),
					        }			)	,	homepage='''https://github.com/moussaKam/FrugalScore'''	,	)




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Tuple			):
					'''simple docstring'''





					if self.config_name == "default":
							__UpperCAmelCase   :					str													=     '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
					else:
							__UpperCAmelCase   :					Dict													=     self.config_name
					__UpperCAmelCase   :					Union[str, Any]													=     AutoModelForSequenceClassification.from_pretrained(lowercase_			)
					__UpperCAmelCase   :					Tuple													=     AutoTokenizer.from_pretrained(lowercase_			)




			def 			snake_case__					(       self							:			int	,	a_							:			int	,	a_							:			Optional[int]	,	a_							:			Optional[Any]=32	,	a_							:			Optional[int]=1_28	,	a_							:			str=None	,	):
					'''simple docstring'''





					assert len(lowercase_			) == len(
					    lowercase_			), "predictions and references should have the same number of sentences."
					if device is not None:
							assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
					else:
							__UpperCAmelCase   :					Tuple													=     '''gpu''' if torch.cuda.is_available() else '''cpu'''
					__UpperCAmelCase   :					Tuple													=     TrainingArguments(
					    '''trainer'''	,	fpaa=(device == '''gpu''')	,	per_device_eval_batch_size=lowercase_	,	report_to='''all'''	,	no_cuda=(device == '''cpu''')	,	log_level='''warning'''	,	)
					__UpperCAmelCase   :					str													=     {'''sentence1''': predictions, '''sentence2''': references}
					__UpperCAmelCase   :					Optional[int]													=     datasets.Dataset.from_dict(lowercase_			)

					def tokenize_function(a_							:			str			):
							return self.tokenizer(
							    data['''sentence1''']	,	data['''sentence2''']	,	max_length=lowercase_	,	truncation=lowercase_	,	padding=lowercase_			)

					__UpperCAmelCase   :					int													=     raw_datasets.map(lowercase_	,	batched=lowercase_			)
					tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']			)
					__UpperCAmelCase   :					Any													=     Trainer(self.model	,	lowercase_	,	tokenizer=self.tokenizer			)
					__UpperCAmelCase   :					int													=     trainer.predict(lowercase_			)
					return {"scores": list(predictions.predictions.squeeze(-1			)			)}










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





