



'''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_     (							_lowerCAmelCase       )      ->      Any:
  __lowerCamelCase       :       List[Any]           = filter(lambda _lowerCAmelCase       : p.requires_grad       ,model.parameters()       )
  __lowerCamelCase       :       Any           = sum([np.prod(p.size()       ) for p in model_parameters]       )
  return params


_UpperCamelCase											=						logging.getLogger(__name__)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  if metric == "rouge2":
    __lowerCamelCase       :       Dict           = '{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    __lowerCamelCase       :       int           = '{val_avg_bleu:.4f}-{step_count}'
  elif metric == "em":
    __lowerCamelCase       :       Any           = '{val_avg_em:.4f}-{step_count}'
  elif metric == "loss":
    __lowerCamelCase       :       Union[str, Any]           = '{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.'       )

  __lowerCamelCase       :       Any           = ModelCheckpoint(
      dirpath=_lowerCAmelCase       ,filename=_lowerCAmelCase       ,monitor=F'val_{metric}'       ,mode='max'       ,save_top_k=1       ,every_n_epochs=1       ,)

  return checkpoint_callback

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  return EarlyStopping(
      monitor=F'val_{metric}'       ,mode='min' if 'loss' in metric else 'max'       ,patience=_lowerCAmelCase       ,verbose=_lowerCAmelCase       ,)






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




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

    if not save_generations:
      return

    if "preds" in metrics:
      __lowerCamelCase       :       Union[str, Any]           = '\n'.join(metrics['preds']   )
      generations_file.open('w+'   ).write(_a   )




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

    __lowerCamelCase       :       Any           = 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 						_lowercase     ( self      :	Dict			,	_a      :	pl.Trainer			,	_a      :	pl.LightningModule   )					->					Optional[Any]:
    save_json(pl_module.metrics			,	pl_module.metrics_save_path   )
    return self._write_logs(_a			,	_a			,	'test'   )




  @rank_zero_only
  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	pl.Trainer			,	_a      :	str   )					->					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_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






