





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

import datasets


_UpperCAmelCase										=					'\\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'

_UpperCAmelCase										=					'\\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'


_UpperCAmelCase										=					'\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  ):

       def lowercase			(		self:			List[Any]		)							->			Tuple:




              """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 lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			str:




              """simple docstring"""


              if self.config_name == "default":
                     UpperCamelCase_ 							=	"moussaKam/frugalscore_tiny_bert-base_bert-score"
              else:
                     UpperCamelCase_ 							=	self.config_name
              UpperCamelCase_ 							=	AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict=32       , _SCREAMING_SNAKE_CASE:			str=128       , _SCREAMING_SNAKE_CASE:			Any=None       , )							->			Any:




              """simple docstring"""


              assert len(_SCREAMING_SNAKE_CASE		) == len(
                  _SCREAMING_SNAKE_CASE		), "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_ 							=	"gpu" if torch.cuda.is_available() else "cpu"
              UpperCamelCase_ 							=	TrainingArguments(
                  "trainer"       , fpaa=(device == "gpu")       , per_device_eval_batch_size=_SCREAMING_SNAKE_CASE       , report_to="all"       , no_cuda=(device == "cpu")       , log_level="warning"       , )
              UpperCamelCase_ 							=	{"sentence1": predictions, "sentence2": references}
              UpperCamelCase_ 							=	datasets.Dataset.from_dict(_SCREAMING_SNAKE_CASE		)

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

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
