




'''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_rag import save_json




def     __magic_name__       (				A	:      Dict     ):






			'''simple docstring'''







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


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




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if metric == "rouge2":
						a					=    "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						a					=    "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						a					=    "{val_avg_em:.4f}-{step_count}"
			elif metric == "loss":
						a					=    "{val_avg_loss:.4f}-{step_count}"
			else:
						raise NotImplementedError(
						    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
						    " function."     )

			a					=    ModelCheckpoint(
			    dirpath=A,						filename=A,						monitor=F"""val_{metric}""",						mode="max",						save_top_k=1,						every_n_epochs=1,						)

			return checkpoint_callback




def     __magic_name__       (				A	:      Union[str, Any],						A	:      Optional[int]     ):






			'''simple docstring'''







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






class 					snake_case__							(pl.Callback     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Optional[int]:
							a					=    {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 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[int]=True							)      ->  None:
							logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
							a					=    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
							a					=    Path(pl_module.hparams.output_dir							)
							if type_path == "test":
										a					=    od / "test_results.txt"
										a					=    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.
										a					=    od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
										a					=    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
													a					=    metrics[key]
													if isinstance(__lowerCamelCase      ,   torch.Tensor							):
																a					=    val.item()
													a					=    f"""{key}: {val:.6f}\n"""
													writer.write(__lowerCamelCase							)

							if not save_generations:
										return

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





				@rank_zero_only
				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  str							)      ->  List[Any]:
							try:
										a					=    pl_module.model.model.num_parameters()
							except AttributeError:
										a					=    pl_module.model.num_parameters()

							a					=    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 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  int:
							save_json(pl_module.metrics      ,   pl_module.metrics_save_path							)
							return self._write_logs(__lowerCamelCase      ,   __lowerCamelCase      ,   "test"							)





				@rank_zero_only
				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str							)      ->  List[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     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			def get_matched_characters(A	:      str,						A	:      str     ) -> str:
						a					=    []
						a					=    min(len(_stra     ),						len(_stra     )     ) // 2
						for i, l in enumerate(_stra     ):
									a					=    int(max(0,						i - limit     )     )
									a					=    int(min(i + limit + 1,						len(_stra     )     )     )
									if l in _stra[left:right]:
												matched.append(A     )
												a					=    F"""{_stra[0:_stra.index(A     )]} {_stra[_stra.index(A     ) + 1:]}"""

						return "".join(A     )

			# matching characters
			a					=    get_matched_characters(A,						A     )
			a					=    get_matched_characters(A,						A     )
			a					=    len(A     )

			# transposition
			a					=    (
			    len([(ca, ca) for ca, ca in zip(A,						A     ) if ca != ca]     ) // 2
			)

			if not match_count:
						a					=    0.0
			else:
						a					=    (
						    1
						    / 3
						    * (
						        match_count / len(A     )
						        + match_count / len(A     )
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			a					=    0
			for ca, ca in zip(stra[:4],						stra[:4]     ):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))






