





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 import save_json


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[int] )      ->       Any:




		'''simple docstring'''
		lowercase__				:			Dict            =		filter(lambda _lowercase : p.requires_grad      ,						model.parameters() )
		lowercase__				:			int            =		sum([np.prod(p.size() ) for p in model_parameters] )
		return params


__UpperCamelCase:  List[Any]   =   logging.getLogger(__name__)


class 			__lowerCAmelCase				(					pl.Callback			):


	'''simple docstring'''





	def       snake_case__(	self:	Any,				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	Optional[int] ):
			lowercase__				:			Union[str, Any]            =		{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:	Tuple,				lowerCamelCase_:	pl.Trainer,				lowerCamelCase_:	pl.LightningModule,				lowerCamelCase_:	str,				lowerCamelCase_:	str=True ):
			logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
			lowercase__				:			Any            =		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
			lowercase__				:			Tuple            =		Path(pl_module.hparams.output_dir )
			if type_path == "test":
					lowercase__				:			Any            =		od / 'test_results.txt'
					lowercase__				:			Optional[Any]            =		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.
					lowercase__				:			Optional[int]            =		od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
					lowercase__				:			str            =		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
							lowercase__				:			Union[str, Any]            =		metrics[key]
							if isinstance(lowerCamelCase_,				torch.Tensor ):
									lowercase__				:			str            =		val.item()
							lowercase__				:			Any            =		F"""{key}: {val:.6f}\n"""
							writer.write(lowerCamelCase_ )

			if not save_generations:
					return

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

	@rank_zero_only
	def       snake_case__(	self:	str,				lowerCamelCase_:	Dict,				lowerCamelCase_:	Optional[int] ):
			try:
					lowercase__				:			List[str]            =		pl_module.model.model.num_parameters()
			except AttributeError:
					lowercase__				:			Optional[Any]            =		pl_module.model.num_parameters()

			lowercase__				:			str            =		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:	str,				lowerCamelCase_:	pl.Trainer,				lowerCamelCase_:	pl.LightningModule ):
			save_json(pl_module.metrics,				pl_module.metrics_save_path )
			return self._write_logs(lowerCamelCase_,				lowerCamelCase_,				'test' )





	@rank_zero_only
	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	pl.Trainer,				lowerCamelCase_:	str ):
			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 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[Any]      ,						_lowercase    :   Any      ,						_lowercase    :   List[Any]=1      ,						_lowercase    :   Optional[Any]=False )      ->       Dict:




		'''simple docstring'''
		if metric == "rouge2":
				lowercase__				:			Union[str, Any]            =		'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				lowercase__				:			Dict            =		'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "loss":
				lowercase__				:			List[str]            =		'{val_avg_loss:.4f}-{step_count}'
		else:
				raise NotImplementedError(
				    f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
				    ' this function.' )

		lowercase__				:			str            =		ModelCheckpoint(
		    dirpath=_lowercase      ,						filename=_lowercase      ,						monitor=f"""val_{metric}"""      ,						mode='min' if 'loss' in metric else 'max'      ,						save_top_k=_lowercase      ,						)
		return checkpoint_callback





def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple      ,						_lowercase    :   str )      ->       Union[str, Any]:




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









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


