




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_rag import save_json



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :List[Any]      	=						filter(lambda lowerCamelCase   : p.requires_grad,			model.parameters()   )
  lowercase    :str      	=						sum([np.prod(p.size()   ) for p in model_parameters]   )
  return params


_UpperCAmelCase	:						Dict			=     logging.getLogger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):



  if metric == "rouge2":
    lowercase    :Optional[Any]      	=						'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    lowercase    :Any      	=						'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    lowercase    :Optional[int]      	=						'''{val_avg_em:.4f}-{step_count}'''
  elif metric == "loss":
    lowercase    :Union[str, Any]      	=						'''{val_avg_loss:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
        " function."   )

  lowercase    :List[str]      	=						ModelCheckpoint(
      dirpath=lowerCAmelCase_,			filename=lowerCAmelCase_,			monitor=F"val_{metric}",			mode="max",			save_top_k=1,			every_n_epochs=1,			)

  return checkpoint_callback



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  return EarlyStopping(
      monitor=F"val_{metric}",			mode="min" if "loss" in metric else "max",			patience=lowerCAmelCase_,			verbose=lowerCAmelCase_,			)







class    __lowerCAmelCase       (							pl.Callback):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					int				):
         lowercase    :Tuple      	=						{F"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
         pl_module.logger.log_metrics(lowercase__				)






       @rank_zero_only
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					Optional[int]=True				):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"				)
         lowercase    :Dict      	=						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
         lowercase    :int      	=						Path(pl_module.hparams.output_dir				)
         if type_path == "test":
           lowercase    :str      	=						od / '''test_results.txt'''
           lowercase    :Union[str, Any]      	=						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.
           lowercase    :Optional[int]      	=						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase    :Optional[int]      	=						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=lowercase__				)
           generations_file.parent.mkdir(exist_ok=lowercase__				)
         with open(lowercase__       ,					"a+"				) as writer:
           for key in sorted(lowercase__				):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase    :Optional[Any]      	=						metrics[key]
             if isinstance(lowercase__       ,					torch.Tensor				):
               lowercase    :Optional[int]      	=						val.item()
             lowercase    :int      	=						F"{key}: {val:.6f}\n"
             writer.write(lowercase__				)

         if not save_generations:
           return

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






       @rank_zero_only
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					List[str]				):





         try:
           lowercase    :Dict      	=						pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase    :str      	=						pl_module.model.num_parameters()

         lowercase    :Any      	=						count_trainable_parameters(lowercase__				)
         # 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 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					Union[str, Any]       ,					_lowerCAmelCase:					List[Any]				):
         save_json(pl_module.metrics       ,					pl_module.metrics_save_path				)
         return self._write_logs(lowercase__       ,					lowercase__       ,					"test"				)






       @rank_zero_only
       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Optional[int]				):
         save_json(pl_module.metrics       ,					pl_module.metrics_save_path				)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")









from abc import ABC, abstractmethod
from argparse import ArgumentParser




class    __lowerCAmelCase       (							lowerCAmelCase):






       @staticmethod
       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					ArgumentParser				):
         raise NotImplementedError()






       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         raise NotImplementedError()



