



"""simple docstring"""


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

import datasets


snake_case_         = """\
@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}
}
"""

snake_case_         = """\
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.
"""


snake_case_         = """
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    A_    (  datasets.Metric       ):



						"""simple docstring"""






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Optional[int]:
												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 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:Any      )       ->    Any:
												if self.config_name == "default":
																		UpperCAmelCase    =				'moussaKam/frugalscore_tiny_bert-base_bert-score'
												else:
																		UpperCAmelCase    =				self.config_name
												UpperCAmelCase    =				AutoModelForSequenceClassification.from_pretrained(lowercase_      )
												UpperCAmelCase    =				AutoTokenizer.from_pretrained(lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Any     ,						lowercase_				:List[str]     ,						lowercase_				:Dict=32     ,						lowercase_				:str=1_28     ,						lowercase_				:List[Any]=None     ,						)       ->    Dict:
												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    =				'gpu' if torch.cuda.is_available() else 'cpu'
												UpperCAmelCase    =				TrainingArguments(
												    'trainer'     ,						fpaa=(device == 'gpu')     ,						per_device_eval_batch_size=lowercase_     ,						report_to='all'     ,						no_cuda=(device == 'cpu')     ,						log_level='warning'     ,						)
												UpperCAmelCase    =				{'sentence1': predictions, 'sentence2': references}
												UpperCAmelCase    =				datasets.Dataset.from_dict(lowercase_      )

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

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










"""simple docstring"""


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase    =				mask_scale
																		UpperCAmelCase    =				mask_init
																		UpperCAmelCase    =				nn.Parameter(torch.empty(self.weight.size()      )      )
																		self.init_mask()






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores     ,						val=self.mask_scale      )
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores     ,						a=-self.mask_scale     ,						b=self.mask_scale      )
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores     ,						a=math.sqrt(5      )      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





