"""simple docstring"""





import logging
import os

from .state import PartialState


class      UpperCAmelCase_	(    logging.LoggerAdapter):


      @staticmethod
      def 		_UpperCAmelCase       (   a							)       ->					Dict:
             lowercase__			:		Any       =	PartialState()
             return not main_process_only or (main_process_only and state.is_main_process)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			*a		,			**a							)       ->					Union[str, Any]:
             if PartialState._shared_state == {}:
                    raise RuntimeError(
                        'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'							)
             lowercase__			:		str       =	kwargs.pop('main_process_only'		,			a							)
             lowercase__			:		Optional[int]       =	kwargs.pop('in_order'		,			a							)

             if self.isEnabledFor(a							):
                    if self._should_log(a							):
                           lowercase__    ,      lowercase__			:		int       =	self.process(a		,			a							)
                           self.logger.log(a		,			a		,			*a		,			**a							)

                    elif in_order:
                           lowercase__			:		Dict       =	PartialState()
                           for i in range(state.num_processes							):
                                  if i == state.process_index:
                                         lowercase__    ,      lowercase__			:		Optional[Any]       =	self.process(a		,			a							)
                                         self.logger.log(a		,			a		,			*a		,			**a							)
                                  state.wait_for_everyone()





def 				a_     (      _lowerCAmelCase  : str   , _lowerCAmelCase  : str = None     ):





       '''simple docstring'''

       if log_level is None:
              lowercase__			:		Optional[Any]       =	os.environ.get('ACCELERATE_LOG_LEVEL'   , _lowerCAmelCase     )
       lowercase__			:		List[Any]       =	logging.getLogger(_lowerCAmelCase     )
       if log_level is not None:
              logger.setLevel(log_level.upper()     )
              logger.root.setLevel(log_level.upper()     )
       return MultiProcessAdapter(_lowerCAmelCase   , {}     )







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_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{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."     )

					__lowerCAmelCase				   =       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_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       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.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       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
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											return self._write_logs(_A		,			_A		,			"test"			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")




