code: 
  
  
  
  
  
  
  """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 import save_json
  
  
  def        A	(							__snake_case:   str     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						filter(lambda __snake_case     : p.requires_grad   ,    model.parameters()     )
         __magic_name__										=						sum([np.prod(p.size()     ) for p in model_parameters]     )
         return params
  
  
  snake_case   :   List[Any]    		=		logging.getLogger(__name__)
  
  
  
  class 					UpperCamelCase__   (							pl.Callback):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						{f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
             pl_module.logger.log_metrics(_lowerCAmelCase   )
  
  
  
  
  
  
  
      @rank_zero_only
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Dict=True   ):
  
  
  
  
  
             '''simple docstring'''
  
             logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
             __magic_name__										=						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
             __magic_name__										=						Path(pl_module.hparams.output_dir   )
             if type_path == "test":
                    __magic_name__										=						od / """test_results.txt"""
                    __magic_name__										=						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.
                    __magic_name__										=						od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    __magic_name__										=						od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                    results_file.parent.mkdir(exist_ok=_lowerCAmelCase   )
                    generations_file.parent.mkdir(exist_ok=_lowerCAmelCase   )
             with open(_lowerCAmelCase							,						'a+'   ) as writer:
                    for key in sorted(_lowerCAmelCase   ):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           __magic_name__										=						metrics[key]
                           if isinstance(_lowerCAmelCase							,						torch.Tensor   ):
                                  __magic_name__										=						val.item()
                           __magic_name__										=						f"""{key}: {val:.6f}\n"""
                           writer.write(_lowerCAmelCase   )
  
             if not save_generations:
                    return
  
             if "preds" in metrics:
                    __magic_name__										=						"""\n""".join(metrics['preds']   )
                    generations_file.open('w+'   ).write(_lowerCAmelCase   )
  
  
  
  
  
  
  
      @rank_zero_only
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             try:
                    __magic_name__										=						pl_module.model.model.num_parameters()
             except AttributeError:
                    __magic_name__										=						pl_module.model.num_parameters()
  
             __magic_name__										=						count_trainable_parameters(_lowerCAmelCase   )
             # 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   :  List[Any]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             save_json(pl_module.metrics							,						pl_module.metrics_save_path   )
             return self._write_logs(_lowerCAmelCase							,						_lowerCAmelCase							,						'test'   )
  
  
  
  
  
  
  
      @rank_zero_only
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''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")
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   Any   ,    __snake_case:   Dict=1   ,    __snake_case:   List[Any]=False     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         if metric == "rouge2":
                __magic_name__										=						"""{val_avg_rouge2:.4f}-{step_count}"""
         elif metric == "bleu":
                __magic_name__										=						"""{val_avg_bleu:.4f}-{step_count}"""
         elif metric == "loss":
                __magic_name__										=						"""{val_avg_loss:.4f}-{step_count}"""
         else:
                raise NotImplementedError(
                    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
                    ' this function.'     )
  
         __magic_name__										=						ModelCheckpoint(
             dirpath=__lowerCamelCase   ,    filename=__lowerCamelCase   ,    monitor=F"""val_{metric}"""   ,    mode='min' if 'loss' in metric else 'max'   ,    save_top_k=__lowerCamelCase   ,    )
         return checkpoint_callback
  
  
  
  
  
  
  def        A	(							__snake_case:   Tuple   ,    __snake_case:   List[Any]     )						->		Tuple:
  
         """simple docstring"""
  
  
  
  
  
         return EarlyStopping(
             monitor=F"""val_{metric}"""   ,    mode='min' if 'loss' in metric else 'max'   ,    patience=__lowerCamelCase   ,    verbose=__lowerCamelCase   ,    )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)