


'''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						)				->					str:




     '''simple docstring'''



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


a_   :      Optional[Any]				    =      logging.getLogger(__name__)






def 		_A			(lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:int						)				->					Any:




     '''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}'
     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=lowerCAmelCase__			,  filename=lowerCAmelCase__			,  monitor=f'val_{metric}'			,  mode='max'			,  save_top_k=3			,  every_n_epochs=1			,  )
     return checkpoint_callback






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:Optional[Any]						)				->					Optional[Any]:




     '''simple docstring'''



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






class 	a       (       pl.Callback    ):
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      _a      =							{f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
      pl_module.logger.log_metrics(__magic_name__  )
 @rank_zero_only
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=True  )   ->							None:
      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=__magic_name__  )
           generations_file.parent.mkdir(exist_ok=__magic_name__  )
      with open(__magic_name__ ,			'a+'  ) as writer:
           for key in sorted(__magic_name__  ):
                if key in ["log", "progress_bar", "preds"]:
                     continue
                _a      =							metrics[key]
                if isinstance(__magic_name__ ,			torch.Tensor  ):
                     _a      =							val.item()
                _a      =							f'{key}: {val:.6f}\n'
                writer.write(__magic_name__  )

      if not save_generations:
           return

      if "preds" in metrics:
           _a      =							'\n'.join(metrics['preds']  )
           generations_file.open('w+'  ).write(__magic_name__  )
 @rank_zero_only
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							List[str]:
      try:
           _a      =							pl_module.model.model.num_parameters()
      except AttributeError:
           _a      =							pl_module.model.num_parameters()

      _a      =							count_trainable_parameters(__magic_name__  )
      # 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 ,			__magic_name__ ,			__magic_name__  )   ->							Union[str, Any]:
      save_json(pl_module.metrics ,			pl_module.metrics_save_path  )
      return self._write_logs(__magic_name__ ,			__magic_name__ ,			'test'  )




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





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

