


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A			=						re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		str
    UpperCAmelCase__						:		Optional[str]              =  None
    UpperCAmelCase__						:		Optional[Union[str, int]]              =  None
    UpperCAmelCase__						:		Optional[Union[str, int]]              =  None
    UpperCAmelCase__						:		Optional[Union[str, int]]              =  None






    def _a (      self   )  ->	Any:
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=_str_to_version_tuple(self.version_str   )






    def __repr__(      self   )  ->	List[Any]:
         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'






    @property
    def _a (      self   )  ->	Dict:
         return self.major, self.minor, self.patch






    def _a (      self ,       A_   )  ->	Any:
         if isinstance(A_ ,       A_   ):
              return Version(A_   )
         elif isinstance(A_ ,       A_   ):
              return other
         raise TypeError(f'{other} (type {type(A_   )}) cannot be compared to version.'   )






    def __eq__(      self ,       A_   )  ->	List[str]:
         try:
              __UpperCamelCase   							=self._validate_operand(A_   )
         except (TypeError, ValueError):
              return False
         else:
              return self.tuple == other.tuple






    def __lt__(      self ,       A_   )  ->	Dict:
         __UpperCamelCase   							=self._validate_operand(A_   )
         return self.tuple < other.tuple






    def __hash__(      self   )  ->	Tuple:
         return hash(_version_tuple_to_str(self.tuple   )   )






    @classmethod
    def _a (      cls ,       A_   )  ->	str:
         __UpperCamelCase   							={f.name for f in dataclasses.fields(cls   )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}   )






    def _a (      self   )  ->	str:
         return self.version_str

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any    ):
     __UpperCamelCase   							=_VERSION_REG.match(SCREAMING_SNAKE_CASE__    )
     if not res:
          raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'    )
     return tuple(int(SCREAMING_SNAKE_CASE__    ) for v in [res.group('major'    ), res.group('minor'    ), res.group('patch'    )]    )





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     return ".".join(str(SCREAMING_SNAKE_CASE__    ) for v in version_tuple    )









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


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[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=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __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 _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         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 _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         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 _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





