'''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 snake_case_      (					__snake_case  :							List[Any])			->					Union[str, Any]:
   lowerCAmelCase_			=      filter(lambda __snake_case: p.requires_grad	,   model.parameters())
   lowerCAmelCase_			=      sum([np.prod(p.size()) for p in model_parameters])
   return params


A_     :						List[str]    							=logging.getLogger(__name__)



def snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							List[str])			->					str:
   if metric == "rouge2":
      lowerCAmelCase_			=      '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      lowerCAmelCase_			=      '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      lowerCAmelCase_			=      '''{val_avg_em:.4f}-{step_count}'''
   elif metric == "loss":
      lowerCAmelCase_			=      '''{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.''')

   lowerCAmelCase_			=      ModelCheckpoint(
       dirpath=__snake_case	,   filename=__snake_case	,   monitor=F'''val_{metric}'''	,   mode='''max'''	,   save_top_k=1	,   every_n_epochs=1	,   )

   return checkpoint_callback



def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							List[str])			->					int:
   return EarlyStopping(
       monitor=F'''val_{metric}'''	,   mode='''min''' if '''loss''' in metric else '''max'''	,   patience=__snake_case	,   verbose=__snake_case	,   )






class     __UpperCAmelCase       ( pl.Callback		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      {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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=True ):
     logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
     lowerCAmelCase_			=      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
     lowerCAmelCase_			=      Path(pl_module.hparams.output_dir )
     if type_path == "test":
        lowerCAmelCase_			=      od / '''test_results.txt'''
        lowerCAmelCase_			=      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.
        lowerCAmelCase_			=      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
        lowerCAmelCase_			=      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
           lowerCAmelCase_			=      metrics[key]
           if isinstance(_lowerCamelCase    ,     torch.Tensor ):
              lowerCAmelCase_			=      val.item()
           lowerCAmelCase_			=      F'''{key}: {val:.6f}\n'''
           writer.write(_lowerCamelCase )

     if not save_generations:
        return

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





  @rank_zero_only
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     try:
        lowerCAmelCase_			=      pl_module.model.model.num_parameters()
     except AttributeError:
        lowerCAmelCase_			=      pl_module.model.num_parameters()

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     save_json(pl_module.metrics    ,     pl_module.metrics_save_path )
     return self._write_logs(_lowerCamelCase    ,     _lowerCamelCase    ,     '''test''' )







  @rank_zero_only
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     save_json(pl_module.metrics    ,     pl_module.metrics_save_path )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")






'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





