"""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 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






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


SCREAMING_SNAKE_CASE__      =					logging.getLogger(__name__)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			List[Any]   )   ->			Dict:





						"""simple docstring"""






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

						snake_case        =     ModelCheckpoint(
						    dirpath=_UpperCamelCase	,     filename=_UpperCamelCase	,     monitor=f"""val_{metric}"""	,     mode='max'	,     save_top_k=1	,     every_n_epochs=1	,     )

						return checkpoint_callback





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[str]   )   ->			int:





						"""simple docstring"""






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



class 					lowerCAmelCase_      (     pl.Callback   ):



				"""simple docstring"""

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
										pl_module.logger.log_metrics(lowerCAmelCase      )

				@rank_zero_only
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=True      ):






										"""simple docstring"""

										logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
										snake_case        =     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
										snake_case        =     Path(pl_module.hparams.output_dir      )
										if type_path == "test":
																snake_case        =     od / 'test_results.txt'
																snake_case        =     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.
																snake_case        =     od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
																snake_case        =     od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=lowerCAmelCase      )
																generations_file.parent.mkdir(exist_ok=lowerCAmelCase      )
										with open(lowerCAmelCase						,      'a+'      ) as writer:
																for key in sorted(lowerCAmelCase      ):
																						if key in ["log", "progress_bar", "preds"]:
																												continue
																						snake_case        =     metrics[key]
																						if isinstance(lowerCAmelCase						,      torch.Tensor      ):
																												snake_case        =     val.item()
																						snake_case        =     F"""{key}: {val:.6f}\n"""
																						writer.write(lowerCAmelCase      )

										if not save_generations:
																return

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

				@rank_zero_only
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

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

										snake_case        =     count_trainable_parameters(lowerCAmelCase      )
										# 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						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										save_json(pl_module.metrics						,      pl_module.metrics_save_path      )
										return self._write_logs(lowerCAmelCase						,      lowerCAmelCase						,      'test'      )

				@rank_zero_only
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""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")



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


