






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     (						snake_case__						)				->    Union[str, Any]:






						"""simple docstring"""





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


UpperCamelCase												=      logging.getLogger(__name__)



class  __UpperCAmelCase						(pl.Callback       ):







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    Dict ,      UpperCAmelCase_:    Dict	):







									'''simple docstring'''




									_SCREAMING_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(_snake_case	)







			@rank_zero_only
			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[Any]=True	):







									'''simple docstring'''




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

									if not save_generations:
															return

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







			@rank_zero_only
			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    count_trainable_parameters(_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:    str ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




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







			@rank_zero_only
			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    Any ,      UpperCAmelCase_:    Union[str, Any]	):







									'''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 			__lowerCamelCase     (						snake_case__			,snake_case__			,snake_case__=1			,snake_case__=False						)				->    int:






						"""simple docstring"""





						if metric == "rouge2":
												_SCREAMING_SNAKE_CASE       =    """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												_SCREAMING_SNAKE_CASE       =    """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "loss":
												_SCREAMING_SNAKE_CASE       =    """{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."""						)

						_SCREAMING_SNAKE_CASE       =    ModelCheckpoint(
						    dirpath=snake_case__			,filename=snake_case__			,monitor=F'val_{metric}'			,mode="""min""" if """loss""" in metric else """max"""			,save_top_k=snake_case__			,)
						return checkpoint_callback




def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    int:






						"""simple docstring"""





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






def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       str(snake_case     ).strip()
					if not bin_string:
										raise ValueError("""Empty string was passed to the function"""     )
					_lowerCAmelCase         =       bin_string[0] == """-"""
					if is_negative:
										_lowerCAmelCase         =       bin_string[1:]
					if not all(char in """01""" for char in bin_string     ):
										raise ValueError("""Non-binary value was passed to the function"""     )
					_lowerCAmelCase         =       0
					for char in bin_string:
										_lowerCAmelCase         =       2 * decimal_number + int(snake_case     )
					return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()



