



'''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 import save_json


def 			snake_case_					( _lowerCAmelCase  :						Union[str, Any]  )				->       List[Any]:
							UpperCAmelCase			:	Tuple										=		filter(lambda _lowerCAmelCase  : p.requires_grad       ,				model.parameters()  )
							UpperCAmelCase			:	List[Any]										=		sum([np.prod(p.size()  ) for p in model_parameters]  )
							return params


UpperCamelCase__:       Optional[int]							     =      logging.getLogger(__name__)

class     SCREAMING_SNAKE_CASE(							pl.Callback      ):







	"""simple docstring"""






	def 					A     (						self      :					int		,      __snake_case      :					str		,      __snake_case      :					int		)      ->		Any:
								UpperCAmelCase			:	Optional[Any]										=		{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 					A     (						self      :					Dict		,      __snake_case      :					pl.Trainer		,      __snake_case      :					pl.LightningModule		,      __snake_case      :					str		,      __snake_case      :					int=True		)      ->		None:
								logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
								UpperCAmelCase			:	Union[str, 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
								UpperCAmelCase			:	Dict										=		Path(pl_module.hparams.output_dir		)
								if type_path == "test":
															UpperCAmelCase			:	List[str]										=		od / '''test_results.txt'''
															UpperCAmelCase			:	Union[str, 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.
															UpperCAmelCase			:	int										=		od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
															UpperCAmelCase			:	Union[str, Any]										=		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
																						UpperCAmelCase			:	int										=		metrics[key]
																						if isinstance(__snake_case		,      torch.Tensor		):
																													UpperCAmelCase			:	Union[str, Any]										=		val.item()
																						UpperCAmelCase			:	List[Any]										=		F"""{key}: {val:.6f}\n"""
																						writer.write(__snake_case		)

								if not save_generations:
															return

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







	@rank_zero_only
	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					Optional[Any]		,      __snake_case      :					int		)      ->		Optional[int]:
								try:
															UpperCAmelCase			:	Tuple										=		pl_module.model.model.num_parameters()
								except AttributeError:
															UpperCAmelCase			:	Union[str, Any]										=		pl_module.model.num_parameters()

								UpperCAmelCase			:	str										=		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 					A     (						self      :					str		,      __snake_case      :					pl.Trainer		,      __snake_case      :					pl.LightningModule		)      ->		Optional[int]:
								save_json(pl_module.metrics		,      pl_module.metrics_save_path		)
								return self._write_logs(__snake_case		,      __snake_case		,      '''test'''		)







	@rank_zero_only
	def 					A     (						self      :					Dict		,      __snake_case      :					pl.Trainer		,      __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 			snake_case_					( _lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						Tuple=1       ,				_lowerCAmelCase  :						List[Any]=False  )				->       List[str]:
							if metric == "rouge2":
														UpperCAmelCase			:	Union[str, Any]										=		'''{val_avg_rouge2:.4f}-{step_count}'''
							elif metric == "bleu":
														UpperCAmelCase			:	Any										=		'''{val_avg_bleu:.4f}-{step_count}'''
							elif metric == "loss":
														UpperCAmelCase			:	Union[str, 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.'''  )

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




def 			snake_case_					( _lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						Optional[Any]  )				->       Any:
							return EarlyStopping(
							    monitor=f"""val_{metric}"""       ,				mode='''min''' if '''loss''' in metric else '''max'''       ,				patience=_lowerCAmelCase       ,				verbose=_lowerCAmelCase       ,				)










'''simple docstring'''






import re


def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       Dict:
							def docstring_decorator(_lowerCAmelCase  :						List[str]  ):
														UpperCAmelCase			:	str										=		fn.__doc__
														UpperCAmelCase			:	List[str]										=		func_doc.split('''\n'''  )
														UpperCAmelCase			:	Optional[Any]										=		0
														while i < len(_lowerCAmelCase  ) and re.search(R'''^\s*Examples?:\s*$'''       ,				lines[i]  ) is None:
																					i += 1
														if i < len(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Union[str, Any]										=		example_docstring
																					UpperCAmelCase			:	Tuple										=		'''\n'''.join(_lowerCAmelCase  )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""  )
														UpperCAmelCase			:	Any										=		func_doc
														return fn

							return docstring_decorator





