



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


__lowerCamelCase  :				int					=					logging.getLogger(__name__)

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     )			->							Union[str, Any]:





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

						UpperCAmelCase								=  ModelCheckpoint(
						    dirpath=__snake_case     , filename=__snake_case     , monitor=F'val_{metric}'     , mode="max"     , save_top_k=1     , every_n_epochs=1     , )

						return checkpoint_callback

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     )			->							Any:
						return EarlyStopping(
						    monitor=F'val_{metric}'     , mode="min" if "loss" in metric else "max"     , patience=__snake_case     , verbose=__snake_case     , )





class 			__magic_name__ (				pl.Callback    ):






		def SCREAMING_SNAKE_CASE_      (    self   :  str					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Optional[int]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  {F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
								pl_module.logger.log_metrics(__a   )






		@rank_zero_only
		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Tuple=True   )					->       None:
								'''simple docstring'''







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

								if not save_generations:
														return

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






		@rank_zero_only
		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  int   )					->       str:
								'''simple docstring'''







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

								UpperCAmelCase								=  count_trainable_parameters(__a   )
								# 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 SCREAMING_SNAKE_CASE_      (    self   :  List[str]					,		UpperCamelCase__   :  Optional[int]					,		UpperCamelCase__   :  Any   )					->       Tuple:
								'''simple docstring'''







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







		@rank_zero_only
		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]					,		UpperCamelCase__   :  int					,		UpperCamelCase__   :  Union[str, Any]   )					->       Tuple:
								'''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")





"""simple docstring"""





from collections.abc import Callable

import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.array:


					"""simple docstring"""





					_UpperCamelCase									=    int(np.ceil((x_end - xa) / step_size      )      )
					_UpperCamelCase									=    np.zeros((n + 1,)      )
					_UpperCamelCase									=    ya
					_UpperCamelCase									=    xa

					for k in range(__snake_case      ):
										_UpperCamelCase									=    y[k] + step_size * ode_func(__snake_case,	y[k]      )
										_UpperCamelCase									=    y[k] + (
										    (step_size / 2) * (ode_func(__snake_case,	y[k]      ) + ode_func(x + step_size,	__snake_case      ))
										)
										x += step_size

					return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()






