
"""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 lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Dict									=       filter(lambda _snake_case						: p.requires_grad       ,model.parameters()						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       sum([np.prod(p.size()						) for p in model_parameters]						)
							return params


UpperCAmelCase__				:   List[str]   							=			logging.getLogger(__name__)



class      lowerCAmelCase_						(pl.Callback  ):


				"""simple docstring"""



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=True   ) ->		None:



											"""simple docstring"""




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

											if not save_generations:
																		return

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



				@rank_zero_only
				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											try:
																		SCREAMING_SNAKE_CASE__						:	str									=       pl_module.model.model.num_parameters()
											except AttributeError:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       pl_module.model.num_parameters()

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		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 lowercase_  (       _snake_case       ,_snake_case       ,_snake_case=1       ,_snake_case=False						):
							if metric == "rouge2":
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       """{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "loss":
														SCREAMING_SNAKE_CASE__						:	Optional[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."""						)

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       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 lowercase_  (       _snake_case       ,_snake_case						):
							return EarlyStopping(
							    monitor=f'''val_{metric}'''       ,mode="""min""" if """loss""" in metric else """max"""       ,patience=_snake_case       ,verbose=_snake_case       ,)






"""simple docstring"""




import re

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

							return docstring_decorator




