

"""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   _A  (  UpperCamelCase_     :       Any)	->						List[Any]:
   '''simple docstring'''


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


_a       =							logging.getLogger(__name__)




def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Union[str, Any])	->						Optional[int]:
   '''simple docstring'''


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

   __lowercase								=						ModelCheckpoint(
       dirpath=UpperCamelCase_,			filename=UpperCamelCase_,			monitor=F"""val_{metric}""",			mode="max",			save_top_k=3,			every_n_epochs=1,			)
   return checkpoint_callback




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       List[str])	->						int:
   '''simple docstring'''


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




class 					_lowerCAmelCase  (	pl.Callback   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			str	):
    __lowercase								=						{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
    pl_module.logger.log_metrics(UpperCAmelCase__	)


 @rank_zero_only
 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			pl.Trainer,					UpperCAmelCase__							:			pl.LightningModule,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[int]=True	):
    logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
    __lowercase								=						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
    __lowercase								=						Path(pl_module.hparams.output_dir	)
    if type_path == "test":
       __lowercase								=						od / "test_results.txt"
       __lowercase								=						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.
       __lowercase								=						od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
       __lowercase								=						od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
       results_file.parent.mkdir(exist_ok=UpperCAmelCase__	)
       generations_file.parent.mkdir(exist_ok=UpperCAmelCase__	)
    with open(UpperCAmelCase__,					"a+"	) as writer:
       for key in sorted(UpperCAmelCase__	):
          if key in ["log", "progress_bar", "preds"]:
             continue
          __lowercase								=						metrics[key]
          if isinstance(UpperCAmelCase__,					torch.Tensor	):
             __lowercase								=						val.item()
          __lowercase								=						F"""{key}: {val:.6f}\n"""
          writer.write(UpperCAmelCase__	)

    if not save_generations:
       return

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


 @rank_zero_only
 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    try:
       __lowercase								=						pl_module.model.model.num_parameters()
    except AttributeError:
       __lowercase								=						pl_module.model.num_parameters()

    __lowercase								=						count_trainable_parameters(UpperCAmelCase__	)
    # 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 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			pl.Trainer,					UpperCAmelCase__							:			pl.LightningModule	):
    save_json(pl_module.metrics,					pl_module.metrics_save_path	)
    return self._write_logs(UpperCAmelCase__,					UpperCAmelCase__,					"test"	)



 @rank_zero_only
 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			pl.Trainer,					UpperCAmelCase__							:			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")







"""simple docstring"""





from collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




