




"""simple docstring"""



from transformers import ProcessorMixin




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''AutoFeatureExtractor'''
    lowercase__		      =   '''AutoTokenizer'''











"""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_    ,				)





