





'''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 				_lowerCamelCase(	UpperCamelCase__     :			Dict		)		->	str:
    A							:							Tuple				=    filter(lambda UpperCamelCase__		: p.requires_grad     ,							model.parameters()		)
    A							:							Dict				=    sum([np.prod(p.size()		) for p in model_parameters]		)
    return params


snake_case_     			=						logging.getLogger(__name__)


class 	_lowercase    (				pl.Callback					):


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
       A							:							Optional[int]				=    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							:							Any				=    Path(pl_module.hparams.output_dir       )
       if type_path == "test":
           A							:							Tuple				=    od / '''test_results.txt'''
           A							:							Optional[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.
           A							:							Optional[Any]				=    od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
           A							:							str				=    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
               A							:							str				=    metrics[key]
               if isinstance(_lowercase     , torch.Tensor       ):
                   A							:							Union[str, Any]				=    val.item()
               A							:							Optional[Any]				=    f'''{key}: {val:.6f}\n'''
               writer.write(_lowercase       )

       if not save_generations:
           return

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


   @rank_zero_only
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       try:
           A							:							str				=    pl_module.model.model.num_parameters()
       except AttributeError:
           A							:							Optional[int]				=    pl_module.model.num_parameters()

       A							:							int				=    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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       save_json(pl_module.metrics     , pl_module.metrics_save_path       )
       return self._write_logs(_lowercase     , _lowercase     , '''test'''       )


   @rank_zero_only
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       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 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Dict=1     ,							UpperCamelCase__     :			Union[str, Any]=False		)		->	Union[str, Any]:
    if metric == "rouge2":
        A							:							int				=    '''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        A							:							List[str]				=    '''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "loss":
        A							:							str				=    '''{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							:							int				=    ModelCheckpoint(
        dirpath=_snake_case     ,							filename=_snake_case     ,							monitor=F'''val_{metric}'''     ,							mode='''min''' if '''loss''' in metric else '''max'''     ,							save_top_k=_snake_case     ,							)
    return checkpoint_callback





def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Union[str, Any]		)		->	Optional[Any]:
    return EarlyStopping(
        monitor=F'''val_{metric}'''     ,							mode='''min''' if '''loss''' in metric else '''max'''     ,							patience=_snake_case     ,							verbose=_snake_case     ,							)

"""simple docstring"""


from ..utils import DummyObject, requires_backends
class   UpperCAmelCase_	(				metaclass=lowercase__   ):
    snake_case_											=      ["""torch""", """transformers""", """k_diffusion"""]
    def __init__(	self			:       Optional[Any]   ,						*_lowercase			:       Optional[int]   ,						**_lowercase			:       List[str]		)						->    Dict:
       requires_backends(self   ,						["torch", "transformers", "k_diffusion"]		)
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Union[str, Any]   ,						*_lowercase			:       Any   ,						**_lowercase			:       List[Any]		)						->    str:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)




    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						*_lowercase			:       List[Any]   ,						**_lowercase			:       Any		)						->    Any:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)