"""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   UpperCAmelCase					(	UpperCamelCase__				):






       """simple docstring"""





       A__	     =							filter(lambda UpperCamelCase__				: p.requires_grad       ,    model.parameters()				)
       A__	     =							sum([np.prod(p.size()				) for p in model_parameters]				)
       return params


__lowerCamelCase											=  logging.getLogger(__name__)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





       if metric == "rouge2":
              A__	     =							'{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              A__	     =							'{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              A__	     =							'{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              A__	     =							'{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.'				)

       A__	     =							ModelCheckpoint(
           dirpath=lowercase__       ,    filename=lowercase__       ,    monitor=F'''val_{metric}'''       ,    mode='max'       ,    save_top_k=1       ,    every_n_epochs=1       ,    )

       return checkpoint_callback






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






       """simple docstring"""





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


class     UpperCamelCase__(			pl.Callback ):





      def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				int:
             A__	     =							{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
             pl_module.logger.log_metrics(__UpperCamelCase     )





      @rank_zero_only
      def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=True     )					->				Optional[int]:
             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=__UpperCamelCase     )
                    generations_file.parent.mkdir(exist_ok=__UpperCamelCase     )
             with open(__UpperCamelCase		,'a+'     ) as writer:
                    for key in sorted(__UpperCamelCase     ):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           A__	     =							metrics[key]
                           if isinstance(__UpperCamelCase		,torch.Tensor     ):
                                  A__	     =							val.item()
                           A__	     =							f'''{key}: {val:.6f}\n'''
                           writer.write(__UpperCamelCase     )

             if not save_generations:
                    return

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





      @rank_zero_only
      def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:


             try:
                    A__	     =							pl_module.model.model.num_parameters()
             except AttributeError:
                    A__	     =							pl_module.model.num_parameters()

             A__	     =							count_trainable_parameters(__UpperCamelCase     )
             # 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     )					->				int:
             save_json(pl_module.metrics		,pl_module.metrics_save_path     )
             return self._write_logs(__UpperCamelCase		,__UpperCamelCase		,'test'     )





      @rank_zero_only
      def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				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")






"""simple docstring"""




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





