





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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Any:



       """simple docstring"""

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


_lowerCamelCase			:	Optional[int]							 =     logging.getLogger(__name__)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       if metric == "rouge2":
              A__   				=					'{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              A__   				=					'{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              A__   				=					'{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__   				=					ModelCheckpoint(
           dirpath=lowerCamelCase_				,       filename=lowerCamelCase_				,       monitor=f"""val_{metric}"""				,       mode='''max'''				,       save_top_k=3				,       every_n_epochs=1				,       )
       return checkpoint_callback







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Any:



       """simple docstring"""

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


class        UpperCamelCase_					(				pl.Callback  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any)		->List[Any]:





           '''simple docstring'''

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


    @rank_zero_only
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			pl.Trainer      ,				UpperCAmelCase__   :			pl.LightningModule      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			List[Any]=True)		->str:





           '''simple docstring'''

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

           if not save_generations:
                  return

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


    @rank_zero_only
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Union[str, Any])		->str:





           '''simple docstring'''

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

           A__   				=					count_trainable_parameters(UpperCAmelCase_)
           # 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  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			pl.Trainer      ,				UpperCAmelCase__   :			pl.LightningModule)		->Optional[Any]:





           '''simple docstring'''

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





    @rank_zero_only
    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			pl.Trainer      ,				UpperCAmelCase__   :			Optional[int])		->List[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")








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))


