


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A			=						{
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''NllbMoeForConditionalGeneration''',
       '''NllbMoeModel''',
       '''NllbMoePreTrainedModel''',
       '''NllbMoeTop2Router''',
       '''NllbMoeSparseMLP''',
   ]


if TYPE_CHECKING:
   from .configuration_nllb_moe import (
       NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
       NllbMoeConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_nllb_moe import (
          NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
          NllbMoeForConditionalGeneration,
          NllbMoeModel,
          NllbMoePreTrainedModel,
          NllbMoeSparseMLP,
          NllbMoeTopaRouter,
      )


else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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")





