"""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_rag import save_json





def 				a_     (      _lowerCAmelCase  : Tuple     ):





       '''simple docstring'''

       lowercase__			:		Any       =	filter(lambda _lowerCAmelCase     : p.requires_grad   , model.parameters()     )
       lowercase__			:		Union[str, Any]       =	sum([np.prod(p.size()     ) for p in model_parameters]     )
       return params


_UpperCamelCase   :						Union[str, Any]      =    logging.getLogger(__name__)





def 				a_     (      _lowerCAmelCase  : Any   , _lowerCAmelCase  : List[Any]     ):





       '''simple docstring'''

       if metric == "rouge2":
              lowercase__			:		Any       =	'{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              lowercase__			:		Tuple       =	'{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              lowercase__			:		Union[str, Any]       =	'{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              lowercase__			:		int       =	'{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__			:		Tuple       =	ModelCheckpoint(
           dirpath=A__   , filename=A__   , monitor=f"""val_{metric}"""   , mode='max'   , save_top_k=1   , every_n_epochs=1   , )

       return checkpoint_callback





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

       return EarlyStopping(
           monitor=f"""val_{metric}"""   , mode='min' if 'loss' in metric else 'max'   , patience=A__   , verbose=A__   , )






class      UpperCAmelCase_	(    pl.Callback):


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					Optional[int]:
             lowercase__			:		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		,			a		,			a		,			a		,			a=True							)       ->					None:
             logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
             lowercase__			:		Optional[Any]       =	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__			:		Any       =	Path(pl_module.hparams.output_dir							)
             if type_path == "test":
                    lowercase__			:		Dict       =	od / 'test_results.txt'
                    lowercase__			:		str       =	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__			:		Any       =	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    lowercase__			:		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
                           lowercase__			:		Dict       =	metrics[key]
                           if isinstance(a		,			torch.Tensor							):
                                  lowercase__			:		Optional[Any]       =	val.item()
                           lowercase__			:		Dict       =	f"""{key}: {val:.6f}\n"""
                           writer.write(a							)

             if not save_generations:
                    return

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


      @rank_zero_only
      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					int:

             try:
                    lowercase__			:		int       =	pl_module.model.model.num_parameters()
             except AttributeError:
                    lowercase__			:		str       =	pl_module.model.num_parameters()

             lowercase__			:		Union[str, Any]       =	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		,			a		,			a							)       ->					Any:
             save_json(pl_module.metrics		,			pl_module.metrics_save_path							)
             return self._write_logs(a		,			a		,			'test'							)


      @rank_zero_only
      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					Dict:
             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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


