

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 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				Dict:
 '''simple docstring'''


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


a_     :    Any     		=   logging.getLogger(__name__)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	List[Any]						)		->				List[Any]:
 '''simple docstring'''


 if metric == "rouge2":
  SCREAMING_SNAKE_CASE		    =      '{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  SCREAMING_SNAKE_CASE		    =      '{val_avg_bleu:.4f}-{step_count}'
 elif metric == "em":
  SCREAMING_SNAKE_CASE		    =      '{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.'						)

 SCREAMING_SNAKE_CASE		    =      ModelCheckpoint(
     dirpath=_UpperCamelCase		,				filename=_UpperCamelCase		,				monitor=f"""val_{metric}"""		,				mode='max'		,				save_top_k=3		,				every_n_epochs=1		,				)
 return checkpoint_callback


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						)		->				Union[str, Any]:
 '''simple docstring'''


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




class   UpperCamelCase			(  pl.Callback ):

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   int				,     snake_case__			:   List[str]     ):

     """simple docstring"""






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

    @rank_zero_only
    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   pl.Trainer				,     snake_case__			:   pl.LightningModule				,     snake_case__			:   str				,     snake_case__			:   Any=True     ):

     """simple docstring"""






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

     if not save_generations:
      return

     if "preds" in metrics:
      SCREAMING_SNAKE_CASE		    =      '\n'.join(metrics['preds']     )
      generations_file.open('w+'     ).write(snake_case__     )

    @rank_zero_only
    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Any				,     snake_case__			:   Tuple     ):

     """simple docstring"""






     try:
      SCREAMING_SNAKE_CASE		    =      pl_module.model.model.num_parameters()
     except AttributeError:
      SCREAMING_SNAKE_CASE		    =      pl_module.model.num_parameters()

     SCREAMING_SNAKE_CASE		    =      count_trainable_parameters(snake_case__     )
     # 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]				,     snake_case__			:   pl.Trainer				,     snake_case__			:   pl.LightningModule     ):

     """simple docstring"""






     save_json(pl_module.metrics				,     pl_module.metrics_save_path     )
     return self._write_logs(snake_case__				,     snake_case__				,     'test'     )




    @rank_zero_only
    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   pl.Trainer				,     snake_case__			:   Dict     ):

     """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")




import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

