'''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  UpperCamelCase__					(					_lowercase    :				Optional[int]     )							->	Dict:

  '''simple docstring'''





  __UpperCAmelCase:					List[str]		= filter(lambda _lowercase     : p.requires_grad   ,    model.parameters()     )
  __UpperCAmelCase:					Dict		= sum([np.prod(p.size()     ) for p in model_parameters]     )
  return params


SCREAMING_SNAKE_CASE_	   =			logging.getLogger(__name__)






def  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				Optional[Any]     )							->	Union[str, Any]:

  '''simple docstring'''





  if metric == "rouge2":
    __UpperCAmelCase:					Optional[int]		= """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    __UpperCAmelCase:					str		= """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    __UpperCAmelCase:					List[Any]		= """{val_avg_em:.4f}-{step_count}"""
  elif metric == "loss":
    __UpperCAmelCase:					List[str]		= """{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:					Dict		= ModelCheckpoint(
      dirpath=UpperCAmelCase__   ,    filename=UpperCAmelCase__   ,    monitor=F'''val_{metric}'''   ,    mode="""max"""   ,    save_top_k=1   ,    every_n_epochs=1   ,    )

  return checkpoint_callback






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Optional[int]     )							->	Tuple:

  '''simple docstring'''





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







class    a (       pl.Callback				):



  """simple docstring"""







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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







  @rank_zero_only
  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_=True					):

    '''simple docstring'''







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

    if not save_generations:
      return

    if "preds" in metrics:
      __UpperCAmelCase:					List[str]		= """\n""".join(metrics["""preds"""]					)
      generations_file.open("""w+"""					).write(lowercase_					)







  @rank_zero_only
  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    try:
      __UpperCAmelCase:					List[Any]		= pl_module.model.model.num_parameters()
    except AttributeError:
      __UpperCAmelCase:					List[Any]		= pl_module.model.num_parameters()

    __UpperCAmelCase:					Optional[Any]		= count_trainable_parameters(lowercase_					)
    # 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   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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







  @rank_zero_only
  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''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'''






def  UpperCamelCase__					(					_lowercase    :				int     )							->	int:

  if not isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					List[str]		= F'''Input value of [number={number}] must be an integer'''
    raise TypeError(_lowercase     )

  if number < 1:
    __UpperCAmelCase:					Dict		= F'''Input value of [number={number}] must be > 0'''
    raise ValueError(_lowercase     )

  __UpperCAmelCase:					int		= 1

  for i in range(1   ,    _lowercase     ):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()