code: 
  
  
  
  
  """simple docstring"""
  
  
  
  from transformers import ProcessorMixin
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''AutoFeatureExtractor'''
      lowercase__		      =   '''AutoTokenizer'''
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
   A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
   A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
   return params
  
  
  SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)
  
  
  
  
  class 			UpperCAmelCase_						(		pl.Callback							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
       pl_module.logger.log_metrics(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:
  
  
  
  
       '''simple docstring'''
  
       logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
       A__ =       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
       A__ =       Path(pl_module.hparams.output_dir   )
       if type_path == "test":
        A__ =       od / "test_results.txt"
        A__ =       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.
        A__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=snake_case_   )
        generations_file.parent.mkdir(exist_ok=snake_case_   )
       with open(snake_case_			,				"a+"   ) as writer:
        for key in sorted(snake_case_   ):
         if key in ["log", "progress_bar", "preds"]:
          continue
         A__ =       metrics[key]
         if isinstance(snake_case_			,				torch.Tensor   ):
          A__ =       val.item()
         A__ =       F"""{key}: {val:.6f}\n"""
         writer.write(snake_case_   )
  
       if not save_generations:
        return
  
       if "preds" in metrics:
        A__ =       "\n".join(metrics["preds"]   )
        generations_file.open("w+"   ).write(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       try:
        A__ =       pl_module.model.model.num_parameters()
       except AttributeError:
        A__ =       pl_module.model.num_parameters()
  
       A__ =       count_trainable_parameters(snake_case_   )
       # 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 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
       return self._write_logs(snake_case_			,				snake_case_			,				"test"   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
   if metric == "rouge2":
    A__ =       "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
    A__ =       "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
    A__ =       "{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."		)
  
   A__ =       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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)
  
  
  
  
  
  