



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


lowercase_						=       logging.getLogger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):






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

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



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




class 			_snake_case     (   pl.Callback):




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

       if not save_generations:
          return

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




    @rank_zero_only
    def 							A__ (  self   :  Tuple,   __lowercase   :  str,   __lowercase   :  Optional[int]  ):




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

       lowercase__   					=			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[int],   __lowercase   :  List[Any],   __lowercase   :  Dict  ):
       save_json(pl_module.metrics,   pl_module.metrics_save_path  )
       return self._write_logs(a__,   a__,   "test"  )




    @rank_zero_only
    def 							A__ (  self   :  Optional[int],   __lowercase   :  int,   __lowercase   :  List[Any]  ):
       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

lowercase_						=       namedtuple("""covid_data""", """cases deaths recovered""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/"					):
			lowercase__   					=			"//div[@class = \"maincounter-number\"]/span/text()"
			return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_					).content					).xpath(SCREAMING_SNAKE_CASE_					)					)


lowercase_						=       """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()))




