




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


_A     =			logging.getLogger(__name__)





def __UpperCamelCase			(       _A ,  _A  ):
						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}'''
						elif metric == "loss":
												lowerCAmelCase_      			=  '''{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.'''  )

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

						return checkpoint_callback





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






class  A		(  pl.Callback     ):



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""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(UpperCamelCase__   )



		@rank_zero_only
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=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=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
																				lowerCAmelCase_      			=  metrics[key]
																				if isinstance(UpperCamelCase__,	torch.Tensor   ):
																										lowerCAmelCase_      			=  val.item()
																				lowerCAmelCase_      			=  f"{key}: {val:.6f}\n"
																				writer.write(UpperCamelCase__   )

								if not save_generations:
														return

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



		@rank_zero_only
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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







		@rank_zero_only
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""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")










def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




