


"""simple docstring"""






import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[str]:


   if isinstance(_SCREAMING_SNAKE_CASE       ,   (list, tuple)				):
      return honor_type(_SCREAMING_SNAKE_CASE       ,   (move_to_device(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE				) for t in state)				)
   elif isinstance(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE				):
      return type(_SCREAMING_SNAKE_CASE				)({k: move_to_device(_SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE				) for k, v in state.items()}				)
   elif isinstance(_SCREAMING_SNAKE_CASE       ,   torch.Tensor				):
      return state.to(_SCREAMING_SNAKE_CASE				)
   return state


class 	A_       (     torch.optim.Optimizer   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=None						)				->					int:
     a   :	List[str]						=    optimizer
     a   :	Any						=    scaler
     a   :	Union[str, Any]						=    AcceleratorState()
     a   :	Any						=    GradientState()
     a   :	Optional[Any]						=    device_placement
     a   :	int						=    False
     a   :	Dict						=    None

     # Handle device placement
     if device_placement:
        a   :	Tuple						=    self.optimizer.state_dict()
        if self.accelerator_state.distributed_type == DistributedType.TPU:
           xm.send_cpu_data_to_device(__a  ,					self.accelerator_state.device						)
        else:
           a   :	Tuple						=    move_to_device(__a  ,					self.accelerator_state.device						)
        self.optimizer.load_state_dict(__a						)
  @property
  def 							lowercase_     (						self						)				->					List[str]:
     return self.optimizer.state
  @state.setter
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Dict:
     a   :	Optional[Any]						=    state
  @property
  def 							lowercase_     (						self						)				->					str:
     return self.optimizer.param_groups
  @param_groups.setter
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Tuple:
     a   :	List[Any]						=    param_groups
  @property
  def 							lowercase_     (						self						)				->					str:
     return self.optimizer.defaults
  @defaults.setter
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     a   :	Optional[int]						=    defaults
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					int:
     self.optimizer.add_param_group(__a						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Union[str, Any]:
     if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
        xm.send_cpu_data_to_device(__a  ,					self.accelerator_state.device						)
     self.optimizer.load_state_dict(__a						)
  def 							lowercase_     (						self						)				->					Any:
     return self.optimizer.state_dict()
  def 							lowercase_     (						self  ,					__UpperCAmelCase=None						)				->					Any:
     if self.gradient_state.sync_gradients:
        a   :	Dict						=    'set_to_none' in inspect.signature(self.optimizer.zero_grad						).parameters
        if accept_arg:
           if set_to_none is None:
              a   :	Any						=    False
           self.optimizer.zero_grad(set_to_none=__a						)
        else:
           if set_to_none is not None:
              raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'						)
           self.optimizer.zero_grad()
  def 							lowercase_     (						self  ,					__UpperCAmelCase=None						)				->					List[str]:
     if self.gradient_state.sync_gradients:
        if self.accelerator_state.distributed_type == DistributedType.TPU:
           a   :	str						=    {'closure': closure} if closure is not None else {}
           xm.optimizer_step(self.optimizer  ,					optimizer_args=__a						)
        elif self.scaler is not None:
           a   :	List[Any]						=    False
           if self._last_scale is None:
              # `get_scale` is an async operation requiring full synchronization
              # on CPU and GPUs before finishing. As a result, we store away
              # the prior one to reduce the call overhead
              a   :	int						=    self.scaler.get_scale()
              a   :	Dict						=    True
           self.scaler.step(self.optimizer  ,					__a						)
           self.scaler.update()
           a   :	List[Any]						=    self.scaler.get_scale()
           if not new_scale:
              # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
              a   :	Union[str, Any]						=    scale_after < self._last_scale
           a   :	Union[str, Any]						=    scale_after

        else:
           self.optimizer.step(__a						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     for param_group in self.optimizer.param_groups:
        a   :	List[Any]						=    [parameters_map.get(__a  ,					__a						) for p in param_group['params']]
  @property
  def 							lowercase_     (						self						)				->					int:
     warnings.warn(
         'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
         '`optimizer.step_was_skipped` instead.'  ,					__a  ,					)
     return self._is_overflow
  @property
  def 							lowercase_     (						self						)				->					Dict:
     return self._is_overflow
  def __getstate__(						self						)				->					Any:
     return self.__dict__.copy()






  def __setstate__(						self  ,					__UpperCAmelCase						)				->					Dict:
     self.__dict__.update(__a						)












"""simple docstring"""





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

import datasets


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

lowerCAmelCase__        =      '''\
FrugalScore 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.
'''


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





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


						def      snake_case_ (self		)			-> Tuple:
								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 ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "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=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

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

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






