





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 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[str]   =						filter(lambda SCREAMING_SNAKE_CASE__: p.requires_grad					,					model.parameters())
  __snake_case:						Tuple   =						sum([np.prod(p.size()) for p in model_parameters])
  return params


__UpperCAmelCase       :   str				       =							logging.getLogger(__name__)







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if metric == "rouge2":
    __snake_case:						int   =						"""{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    __snake_case:						Optional[Any]   =						"""{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    __snake_case:						str   =						"""{val_avg_em:.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.""")

  __snake_case:						Optional[Any]   =						ModelCheckpoint(
      dirpath=__SCREAMING_SNAKE_CASE					,					filename=__SCREAMING_SNAKE_CASE					,					monitor=F'''val_{metric}'''					,					mode="""max"""					,					save_top_k=3					,					every_n_epochs=1					,					)
  return checkpoint_callback







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  return EarlyStopping(
      monitor=F'''val_{metric}'''					,					mode="""min""" if """loss""" in metric else """max"""					,					patience=__SCREAMING_SNAKE_CASE					,					verbose=__SCREAMING_SNAKE_CASE					,					)







class     __snake_case       (							pl.Callback			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    List[str]							,				A :    List[Any]		):
         __snake_case:						List[Any]   =						{f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
         pl_module.logger.log_metrics(A		)




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

         if not save_generations:
           return

         if "preds" in metrics:
           __snake_case:						Union[str, Any]   =						"""\n""".join(metrics["""preds"""]		)
           generations_file.open("""w+"""		).write(A		)




       @rank_zero_only
       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    List[str]							,				A :    str		):
         try:
           __snake_case:						Tuple   =						pl_module.model.model.num_parameters()
         except AttributeError:
           __snake_case:						Any   =						pl_module.model.num_parameters()

         __snake_case:						str   =						count_trainable_parameters(A		)
         # 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 :    Tuple							,				A :    str							,				A :    List[Any]		):
         save_json(pl_module.metrics							,				pl_module.metrics_save_path		)
         return self._write_logs(A							,				A							,				"""test"""		)






       @rank_zero_only
       def 							UpperCAmelCase__ (  self :    str							,				A :    int							,				A :    Union[str, Any]		):
         save_json(pl_module.metrics							,				pl_module.metrics_save_path		)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

