






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json





def 						A_					(						lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		filter(lambda lowercase_			: p.requires_grad	,  model.parameters()			)
 SCREAMING_SNAKE_CASE				    =		sum([np.prod(p.size()			) for p in model_parameters]			)
 return params


__UpperCAmelCase      =				logging.getLogger(__name__)
class 	a_(		pl.Callback	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
      pl_module.logger.log_metrics(lowerCAmelCase__)





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Optional[Any]=True)							->	None:

      """simple docstring"""
      logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''')
      SCREAMING_SNAKE_CASE				    =		trainer.callback_metrics
      trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']})
      # Log results
      SCREAMING_SNAKE_CASE				    =		Path(pl_module.hparams.output_dir)
      if type_path == "test":
       SCREAMING_SNAKE_CASE				    =		od / 'test_results.txt'
       SCREAMING_SNAKE_CASE				    =		od / 'test_generations.txt'
      else:
       # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
       # If people want this it will be easy enough to add back.
       SCREAMING_SNAKE_CASE				    =		od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
       SCREAMING_SNAKE_CASE				    =		od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
       results_file.parent.mkdir(exist_ok=lowerCAmelCase__)
       generations_file.parent.mkdir(exist_ok=lowerCAmelCase__)
      with open(lowerCAmelCase__  ,  'a+') as writer:
       for key in sorted(lowerCAmelCase__):
        if key in ["log", "progress_bar", "preds"]:
         continue
        SCREAMING_SNAKE_CASE				    =		metrics[key]
        if isinstance(lowerCAmelCase__  ,  torch.Tensor):
         SCREAMING_SNAKE_CASE				    =		val.item()
        SCREAMING_SNAKE_CASE				    =		f'''{key}: {val:.6f}\n'''
        writer.write(lowerCAmelCase__)

      if not save_generations:
       return

      if "preds" in metrics:
       SCREAMING_SNAKE_CASE				    =		'\n'.join(metrics['preds'])
       generations_file.open('w+').write(lowerCAmelCase__)





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str)							->	Optional[int]:

      """simple docstring"""
      try:
       SCREAMING_SNAKE_CASE				    =		pl_module.model.model.num_parameters()
      except AttributeError:
       SCREAMING_SNAKE_CASE				    =		pl_module.model.num_parameters()

      SCREAMING_SNAKE_CASE				    =		count_trainable_parameters(lowerCAmelCase__)
      # mp stands for million parameters
      trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6})





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule)							->	int:

      """simple docstring"""
      save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
      return self._write_logs(lowerCAmelCase__  ,  lowerCAmelCase__  ,  'test')





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	Dict)							->	List[Any]:

      """simple docstring"""
      save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_=1	,  lowercase_=False			)				->Optional[Any]:







 """simple docstring"""

 if metric == "rouge2":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_bleu:.4f}-{step_count}'
 elif metric == "loss":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_loss:.4f}-{step_count}'
 else:
  raise NotImplementedError(
      f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
      ' this function.'			)

 SCREAMING_SNAKE_CASE				    =		ModelCheckpoint(
     dirpath=lowercase_	,  filename=lowercase_	,  monitor=f'''val_{metric}'''	,  mode='min' if 'loss' in metric else 'max'	,  save_top_k=lowercase_	,  )
 return checkpoint_callback





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 return EarlyStopping(
     monitor=f'''val_{metric}'''	,  mode='min' if 'loss' in metric else 'max'	,  patience=lowercase_	,  verbose=lowercase_	,  )











"""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		)		)}






