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


__magic_name__  							=	logging.getLogger(__name__)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    if metric == "rouge2":
        snake_case__    =					"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        snake_case__    =					"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        snake_case__    =					"{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."				)

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





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

          if not save_generations:
              return

          if "preds" in metrics:
              snake_case__    =					"\n".join(metrics["preds"]			)
              generations_file.open("w+"			).write(lowerCamelCase			)
      @rank_zero_only
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          try:
              snake_case__    =					pl_module.model.model.num_parameters()
          except AttributeError:
              snake_case__    =					pl_module.model.num_parameters()

          snake_case__    =					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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          save_json(pl_module.metrics		,						pl_module.metrics_save_path			)
          return self._write_logs(lowerCamelCase		,						lowerCamelCase		,						"test"			)



      @rank_zero_only
      def A_      (      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")





import os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

    for issue in open_issues:
        snake_case__    =					sorted([comment for comment in issue.get_comments()]     ,		key=lambda __lowerCAmelCase				: i.created_at     ,		reverse=__lowerCAmelCase				)
        snake_case__    =					comments[0] if len(__lowerCAmelCase				) > 0 else None
        snake_case__    =					dt.utcnow()
        snake_case__    =					(current_time - issue.updated_at).days
        snake_case__    =					(current_time - issue.created_at).days
        if (
            last_comment is not None
            and last_comment.user.login == "github-actions[bot]"
            and days_since_updated > 7
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Close issue since it has been 7 days of inactivity since bot mention.
            issue.edit(state="closed"				)
        elif (
            days_since_updated > 23
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Add stale comment
            issue.create_comment(
                "This issue has been automatically marked as stale because it has not had "
                "recent activity. If you think this still needs to be addressed "
                "please comment on this thread.\n\nPlease note that issues that do not follow the "
                "[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
                "are likely to be ignored."				)


if __name__ == "__main__":
     main()



