
'''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        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Tuple:





  """simple docstring"""

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


A__   :		Union[str, Any] 		=logging.getLogger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Dict )				->       List[Any]:





  """simple docstring"""

  if metric == "rouge2":
    __A		:     Tuple												=		"""{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    __A		:     Optional[int]												=		"""{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    __A		:     Optional[Any]												=		"""{val_avg_em:.4f}-{step_count}"""
  elif metric == "loss":
    __A		:     str												=		"""{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.""" )

  __A		:     List[Any]												=		ModelCheckpoint(
      dirpath=__SCREAMING_SNAKE_CASE			,     filename=__SCREAMING_SNAKE_CASE			,     monitor=F"val_{metric}"			,     mode="""max"""			,     save_top_k=1			,     every_n_epochs=1			,     )

  return checkpoint_callback







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       List[Any]:





  """simple docstring"""

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







class  __A				( pl.Callback			):
 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    str       ):



   """simple docstring"""





   __A		:     Dict												=		{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 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    pl.Trainer				,   lowerCamelCase			:    pl.LightningModule				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Union[str, Any]=True       ):



   """simple docstring"""





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

   if not save_generations:
     return

   if "preds" in metrics:
     __A		:     Optional[Any]												=		"""\n""".join(metrics["""preds"""]       )
     generations_file.open("""w+"""       ).write(lowerCamelCase       )
 @rank_zero_only
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   try:
     __A		:     Optional[Any]												=		pl_module.model.model.num_parameters()
   except AttributeError:
     __A		:     List[str]												=		pl_module.model.num_parameters()

   __A		:     Optional[Any]												=		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 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    pl.Trainer				,   lowerCamelCase			:    pl.LightningModule       ):



   """simple docstring"""





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






 @rank_zero_only
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    pl.Trainer				,   lowerCamelCase			:    Tuple       ):



   """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 argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

    for i in range(__SCREAMING_SNAKE_CASE ):
      __A		:     int												=		requests.get(url + F"&page={i + 2}"			,     headers=__SCREAMING_SNAKE_CASE ).json()
      job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )

    return job_time
  except Exception:
    print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )

  return {}


if __name__ == "__main__":
     A__   :		Any 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
     A__   :		Any 		=parser.parse_args()

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"{k}: {v['duration']}")



