


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 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






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


lowerCamelCase		=						logging.getLogger(__name__)





class    _a				(							pl.Callback):




	def 				UpperCAmelCase__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						Dict						,   _SCREAMING_SNAKE_CASE		:						Tuple						)->			List[Any]:
			lowerCAmelCase__  :     str       	=   {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE						)




	@rank_zero_only
	def 				UpperCAmelCase__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						pl.Trainer						,   _SCREAMING_SNAKE_CASE		:						pl.LightningModule						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=True						)->			None:
			logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'						)
			lowerCAmelCase__  :     List[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
			lowerCAmelCase__  :     List[str]       	=   Path(pl_module.hparams.output_dir						)
			if type_path == "test":
					lowerCAmelCase__  :     Optional[Any]       	=   od / '''test_results.txt'''
					lowerCAmelCase__  :     Tuple       	=   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__  :     List[Any]       	=   od / F'{type_path}_results/{trainer.global_step:05d}.txt'
					lowerCAmelCase__  :     Any       	=   od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
					results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)
					generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)
			with open(_SCREAMING_SNAKE_CASE						,   '''a+'''						) as writer:
					for key in sorted(_SCREAMING_SNAKE_CASE						):
							if key in ["log", "progress_bar", "preds"]:
									continue
							lowerCAmelCase__  :     Optional[Any]       	=   metrics[key]
							if isinstance(_SCREAMING_SNAKE_CASE						,   torch.Tensor						):
									lowerCAmelCase__  :     Tuple       	=   val.item()
							lowerCAmelCase__  :     Optional[Any]       	=   F'{key}: {val:.6f}\n'
							writer.write(_SCREAMING_SNAKE_CASE						)

			if not save_generations:
					return

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




	@rank_zero_only
	def 				UpperCAmelCase__(   self		:						Tuple						,   _SCREAMING_SNAKE_CASE		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						str						)->			Optional[Any]:
			try:
					lowerCAmelCase__  :     Dict       	=   pl_module.model.model.num_parameters()
			except AttributeError:
					lowerCAmelCase__  :     List[str]       	=   pl_module.model.num_parameters()

			lowerCAmelCase__  :     Dict       	=   count_trainable_parameters(_SCREAMING_SNAKE_CASE						)
			# 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		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						pl.Trainer						,   _SCREAMING_SNAKE_CASE		:						pl.LightningModule						)->			List[Any]:
			save_json(pl_module.metrics						,   pl_module.metrics_save_path						)
			return self._write_logs(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   '''test'''						)




	@rank_zero_only
	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						pl.Trainer						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						)->			Optional[Any]:
			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 	lowerCamelCase_       (			_a      ,  _a      ,  _a=1      ,  _a=False			):




		"""simple docstring"""






		if metric == "rouge2":
				lowerCAmelCase__  :     Optional[int]       	=   '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase__  :     List[Any]       	=   '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "loss":
				lowerCAmelCase__  :     Any       	=   '''{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.'''			)

		lowerCAmelCase__  :     List[str]       	=   ModelCheckpoint(
		    dirpath=_a      ,  filename=_a      ,  monitor=f'val_{metric}'      ,  mode='''min''' if '''loss''' in metric else '''max'''      ,  save_top_k=_a      ,  )
		return checkpoint_callback






def 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






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








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




