




"""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__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  filter(lambda snake_case__    : p.requires_grad		,   model.parameters()    )
  _snake_case							:		str								=  sum([np.prod(p.size()    ) for p in model_parameters]    )
  return params


A_						=				logging.getLogger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  if metric == "rouge2":
    _snake_case							:		Any								=  """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    _snake_case							:		Tuple								=  """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    _snake_case							:		str								=  """{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."""    )

  _snake_case							:		str								=  ModelCheckpoint(
      dirpath=snake_case__		,   filename=snake_case__		,   monitor=F"val_{metric}"		,   mode="""max"""		,   save_top_k=3		,   every_n_epochs=1		,   )
  return checkpoint_callback


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

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






class 					lowercase(     pl.Callback ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[int],      a_:		Optional[int],      a_:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  {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  UpperCamelCase_   (				self:		int,      a_:		pl.Trainer,      a_:		pl.LightningModule,      a_:		str,      a_:		Tuple=True		):




   '''simple docstring'''



   logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
   _snake_case							:		int								=  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
   _snake_case							:		int								=  Path(pl_module.hparams.output_dir		)
   if type_path == "test":
     _snake_case							:		Tuple								=  od / """test_results.txt"""
     _snake_case							:		str								=  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.
     _snake_case							:		Optional[int]								=  od / f"{type_path}_results/{trainer.global_step:05d}.txt"
     _snake_case							:		Optional[Any]								=  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
       _snake_case							:		str								=  metrics[key]
       if isinstance(a_,      torch.Tensor		):
         _snake_case							:		int								=  val.item()
       _snake_case							:		int								=  f"{key}: {val:.6f}\n"
       writer.write(a_		)

   if not save_generations:
     return

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


 @rank_zero_only
 def  UpperCamelCase_   (				self:		Tuple,      a_:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   try:
     _snake_case							:		Dict								=  pl_module.model.model.num_parameters()
   except AttributeError:
     _snake_case							:		Tuple								=  pl_module.model.num_parameters()

   _snake_case							:		int								=  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  UpperCamelCase_   (				self:		List[str],      a_:		pl.Trainer,      a_:		pl.LightningModule		):




   '''simple docstring'''



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


 @rank_zero_only
 def  UpperCamelCase_   (				self:		Tuple,      a_:		pl.Trainer,      a_:		Any		):




   '''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")









lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
