


'''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      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]):
      A_						:  Tuple          =				filter(lambda lowerCamelCase: p.requires_grad							,						model.parameters())
      A_						:  List[str]          =				sum([np.prod(p.size()) for p in model_parameters])
      return params


__magic_name__			=      logging.getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      str):
      if metric == "rouge2":
            A_						:  List[str]          =				"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            A_						:  List[str]          =				"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            A_						:  Dict          =				"""{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.""")

      A_						:  Any          =				ModelCheckpoint(
          dirpath=lowerCamelCase							,						filename=lowerCamelCase							,						monitor=F'val_{metric}'							,						mode="""max"""							,						save_top_k=3							,						every_n_epochs=1							,						)
      return checkpoint_callback




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[int]):
      return EarlyStopping(
          monitor=F'val_{metric}'							,						mode="""min""" if """loss""" in metric else """max"""							,						patience=lowerCamelCase							,						verbose=lowerCamelCase							,						)






class 	__lowerCAmelCase		(							pl.Callback    ):


  '''simple docstring'''







  def        _a							(							self :       str	,_a :       Optional[Any]	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  int          =				{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 :       Dict	,_a :       pl.Trainer	,_a :       pl.LightningModule	,_a :       str	,_a :       Union[str, Any]=True ):

        '''simple docstring'''




        logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
        A_						:  Dict          =				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
        A_						:  str          =				Path(pl_module.hparams.output_dir )
        if type_path == "test":
              A_						:  Dict          =				od / """test_results.txt"""
              A_						:  Optional[Any]          =				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.
              A_						:  Union[str, Any]          =				od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              A_						:  Optional[Any]          =				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
                    A_						:  Optional[int]          =				metrics[key]
                    if isinstance(_a	,torch.Tensor ):
                          A_						:  Union[str, Any]          =				val.item()
                    A_						:  Dict          =				f'{key}: {val:.6f}\n'
                    writer.write(_a )

        if not save_generations:
              return

        if "preds" in metrics:
              A_						:  int          =				"""\n""".join(metrics["""preds"""] )
              generations_file.open("""w+""" ).write(_a )






  @rank_zero_only
  def        _a							(							self :       Optional[int]	,_a :       List[Any]	,_a :       str ):

        '''simple docstring'''




        try:
              A_						:  Union[str, Any]          =				pl_module.model.model.num_parameters()
        except AttributeError:
              A_						:  Tuple          =				pl_module.model.num_parameters()

        A_						:  str          =				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 :       Optional[Any]	,_a :       pl.Trainer	,_a :       pl.LightningModule ):

        '''simple docstring'''




        save_json(pl_module.metrics	,pl_module.metrics_save_path )
        return self._write_logs(_a	,_a	,"""test""" )






  @rank_zero_only
  def        _a							(							self :       Union[str, Any]	,_a :       pl.Trainer	,_a :       List[Any] ):

        '''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")




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     __magic_name__			=      'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     __magic_name__			=      requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(10_000):
               out_file.write(data)
     __magic_name__			=      BeautifulSoup(res.text, 'html.parser')
     __magic_name__			=      list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f"""https://google.com{link.get('href')}""")
