


import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


A							:					str     						=     logging.getLogger(__name__)






class     UpperCamelCase(  __UpperCAmelCase   ):
					snake_case_		:				Any										=	"summarization"
					snake_case_		:				List[str]										=	["loss"]
					snake_case_		:				List[str]										=	ROUGE_KEYS
					snake_case_		:				int										=	"rouge2"


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    **SCREAMING_SNAKE_CASE	:		List[str]						)			->	Dict:




											'''simple docstring'''

											if hparams.sortish_sampler and hparams.gpus > 1:
																	__snake_case				=		False
											elif hparams.max_tokens_per_batch is not None:
																	if hparams.gpus > 1:
																							raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"						)
																	if hparams.sortish_sampler:
																							raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"						)

											super().__init__(_lowerCamelCase     ,    num_labels=_lowerCamelCase     ,    mode=self.mode     ,    **_lowerCamelCase						)
											use_task_specific_params(self.model     ,    "summarization"						)
											save_git_info(self.hparams.output_dir						)
											__snake_case				=		Path(self.output_dir						) / "metrics.json"
											__snake_case				=		Path(self.output_dir						) / "hparams.pkl"
											pickle_save(self.hparams     ,    self.hparams_save_path						)
											__snake_case				=		0
											__snake_case				=		defaultdict(_lowerCamelCase						)
											__snake_case				=		self.config.model_type
											__snake_case				=		self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

											__snake_case				=		{
											    "data_dir": self.hparams.data_dir,
											    "max_source_length": self.hparams.max_source_length,
											    "prefix": self.model.config.prefix or "",
											}
											__snake_case				=		{
											    "train": self.hparams.n_train,
											    "val": self.hparams.n_val,
											    "test": self.hparams.n_test,
											}
											__snake_case				=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

											__snake_case				=		{
											    "train": self.hparams.max_target_length,
											    "val": self.hparams.val_max_target_length,
											    "test": self.hparams.test_max_target_length,
											}
											assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
											assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
											if self.hparams.freeze_embeds:
																	freeze_embeds(self.model						)
											if self.hparams.freeze_encoder:
																	freeze_params(self.model.get_encoder()						)
																	assert_all_frozen(self.model.get_encoder()						)

											__snake_case				=		get_git_info()["repo_sha"]
											__snake_case				=		hparams.num_workers
											__snake_case				=		None  # default to config
											if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer     ,    _lowerCamelCase						):
																	__snake_case				=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	__snake_case				=		self.decoder_start_token_id
											__snake_case				=		(
											    SeqaSeqDataset if hasattr(self.tokenizer     ,    "prepare_seq2seq_batch"						) else LegacySeqaSeqDataset
											)
											__snake_case				=		False
											__snake_case				=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
											if self.hparams.eval_max_gen_length is not None:
																	__snake_case				=		self.hparams.eval_max_gen_length
											else:
																	__snake_case				=		self.model.config.max_length
											__snake_case				=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict[str, torch.Tensor]						)			->	Any:




											'''simple docstring'''

											__snake_case				=		{
											    k: self.tokenizer.batch_decode(v.tolist()						) if "mask" not in k else v.shape for k, v in batch.items()
											}
											save_json(_lowerCamelCase     ,    Path(self.output_dir						) / "text_batch.json"						)
											save_json({k: v.tolist() for k, v in batch.items()}     ,    Path(self.output_dir						) / "tok_batch.json"						)

											__snake_case				=		True
											return readable_batch


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    **SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




											'''simple docstring'''

											return self.model(_lowerCamelCase     ,    **_lowerCamelCase						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[int]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.tokenizer.batch_decode(
											    _lowerCamelCase     ,    skip_special_tokens=_lowerCamelCase     ,    clean_up_tokenization_spaces=_lowerCamelCase						)
											return lmap(str.strip     ,    _lowerCamelCase						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		dict						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.tokenizer.pad_token_id
											__snake_case		,					__snake_case				=		batch["input_ids"], batch["attention_mask"]
											__snake_case				=		batch["labels"]
											if isinstance(self.model     ,    _lowerCamelCase						):
																	__snake_case				=		self.model._shift_right(_lowerCamelCase						)
											else:
																	__snake_case				=		shift_tokens_right(_lowerCamelCase     ,    _lowerCamelCase						)
											if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	__snake_case				=		decoder_input_ids
																	self.save_readable_batch(_lowerCamelCase						)

											__snake_case				=		self(_lowerCamelCase     ,    attention_mask=_lowerCamelCase     ,    decoder_input_ids=_lowerCamelCase     ,    use_cache=_lowerCamelCase						)
											__snake_case				=		outputs["logits"]
											if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	__snake_case				=		nn.CrossEntropyLoss(ignore_index=_lowerCamelCase						)

																	assert lm_logits.shape[-1] == self.vocab_size
																	__snake_case				=		ce_loss_fct(lm_logits.view(-1     ,    lm_logits.shape[-1]						)     ,    tgt_ids.view(-1						)						)
											else:
																	__snake_case				=		nn.functional.log_softmax(_lowerCamelCase     ,    dim=-1						)
																	__snake_case		,					__snake_case				=		label_smoothed_nll_loss(
																	    _lowerCamelCase     ,    _lowerCamelCase     ,    self.hparams.label_smoothing     ,    ignore_index=_lowerCamelCase						)
											return (loss,)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Optional[int]:




											'''simple docstring'''

											return self.tokenizer.pad_token_id


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self._step(_lowerCamelCase						)

											__snake_case				=		dict(zip(self.loss_names     ,    _lowerCamelCase						)						)
											# tokens per batch
											__snake_case				=		batch["input_ids"].ne(self.pad						).sum() + batch["labels"].ne(self.pad						).sum()
											__snake_case				=		batch["input_ids"].shape[0]
											__snake_case				=		batch["input_ids"].eq(self.pad						).sum()
											__snake_case				=		batch["input_ids"].eq(self.pad						).float().mean()
											# TODO(SS): make a wandb summary metric for this
											return {"loss": loss_tensors[0], "log": logs}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	str:




											'''simple docstring'''

											return self._generative_step(_lowerCamelCase						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[int]="val"						)			->	str:




											'''simple docstring'''

											self.step_count += 1
											__snake_case				=		{k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
											__snake_case				=		losses["loss"]
											__snake_case				=		{
											    k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ["gen_time", "gen_len"]
											}
											__snake_case				=		(
											    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
											)
											__snake_case				=		torch.tensor(_lowerCamelCase						).type_as(_lowerCamelCase						)
											generative_metrics.update({k: v.item() for k, v in losses.items()}						)
											losses.update(_lowerCamelCase						)
											__snake_case				=		{f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
											__snake_case				=		self.step_count
											self.metrics[prefix].append(_lowerCamelCase						)  # callback writes this to self.metrics_save_path
											__snake_case				=		flatten_list([x["preds"] for x in outputs]						)
											return {
											    "log": all_metrics,
											    "preds": preds,
											    f'''{prefix}_loss''': loss,
											    f'''{prefix}_{self.val_metric}''': metric_tensor,
											}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




											'''simple docstring'''

											return calculate_rouge(_lowerCamelCase     ,    _lowerCamelCase						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		time.time()

											# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
											__snake_case				=		self.model.generate(
											    batch["input_ids"]     ,    attention_mask=batch["attention_mask"]     ,    use_cache=_lowerCamelCase     ,    decoder_start_token_id=self.decoder_start_token_id     ,    num_beams=self.eval_beams     ,    max_length=self.eval_max_length     ,    )
											__snake_case				=		(time.time() - ta) / batch["input_ids"].shape[0]
											__snake_case				=		self.ids_to_clean_text(_lowerCamelCase						)
											__snake_case				=		self.ids_to_clean_text(batch["labels"]						)
											__snake_case				=		self._step(_lowerCamelCase						)
											__snake_case				=		dict(zip(self.loss_names     ,    _lowerCamelCase						)						)
											__snake_case				=		self.calc_generative_metrics(_lowerCamelCase     ,    _lowerCamelCase						)
											__snake_case				=		np.mean(lmap(_lowerCamelCase     ,    _lowerCamelCase						)						)
											base_metrics.update(gen_time=_lowerCamelCase     ,    gen_len=_lowerCamelCase     ,    preds=_lowerCamelCase     ,    target=_lowerCamelCase     ,    **_lowerCamelCase						)
											return base_metrics


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




											'''simple docstring'''

											return self._generative_step(_lowerCamelCase						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Optional[int]:




											'''simple docstring'''

											return self.validation_epoch_end(_lowerCamelCase     ,    prefix="test"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.n_obs[type_path]
											__snake_case				=		self.target_lens[type_path]
											__snake_case				=		self.dataset_class(
											    self.tokenizer     ,    type_path=_lowerCamelCase     ,    n_obs=_lowerCamelCase     ,    max_target_length=_lowerCamelCase     ,    **self.dataset_kwargs     ,    )
											return dataset


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		bool = False						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.get_dataset(_lowerCamelCase						)

											if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																	__snake_case				=		dataset.make_sortish_sampler(_lowerCamelCase     ,    distributed=self.hparams.gpus > 1						)
																	return DataLoader(
																	    _lowerCamelCase     ,    batch_size=_lowerCamelCase     ,    collate_fn=dataset.collate_fn     ,    shuffle=_lowerCamelCase     ,    num_workers=self.num_workers     ,    sampler=_lowerCamelCase     ,    )

											elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
																	__snake_case				=		dataset.make_dynamic_sampler(
																	    self.hparams.max_tokens_per_batch     ,    distributed=self.hparams.gpus > 1						)
																	return DataLoader(
																	    _lowerCamelCase     ,    batch_sampler=_lowerCamelCase     ,    collate_fn=dataset.collate_fn     ,    num_workers=self.num_workers     ,    )
											else:
																	return DataLoader(
																	    _lowerCamelCase     ,    batch_size=_lowerCamelCase     ,    collate_fn=dataset.collate_fn     ,    shuffle=_lowerCamelCase     ,    num_workers=self.num_workers     ,    sampler=_lowerCamelCase     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.get_dataloader("train"     ,    batch_size=self.hparams.train_batch_size     ,    shuffle=_lowerCamelCase						)
											return dataloader


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Tuple:




											'''simple docstring'''

											return self.get_dataloader("val"     ,    batch_size=self.hparams.eval_batch_size						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	Any:




											'''simple docstring'''

											return self.get_dataloader("test"     ,    batch_size=self.hparams.eval_batch_size						)






					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	str:




											'''simple docstring'''

											BaseTransformer.add_model_specific_args(_lowerCamelCase     ,    _lowerCamelCase						)
											add_generic_args(_lowerCamelCase     ,    _lowerCamelCase						)
											parser.add_argument(
											    "--max_source_length"     ,    default=1_0_2_4     ,    type=_lowerCamelCase     ,    help=(
											        "The maximum total input sequence length after tokenization. Sequences longer "
											        "than this will be truncated, sequences shorter will be padded."
											    )     ,    )
											parser.add_argument(
											    "--max_target_length"     ,    default=5_6     ,    type=_lowerCamelCase     ,    help=(
											        "The maximum total input sequence length after tokenization. Sequences longer "
											        "than this will be truncated, sequences shorter will be padded."
											    )     ,    )
											parser.add_argument(
											    "--val_max_target_length"     ,    default=1_4_2     ,    type=_lowerCamelCase     ,    help=(
											        "The maximum total input sequence length after tokenization. Sequences longer "
											        "than this will be truncated, sequences shorter will be padded."
											    )     ,    )
											parser.add_argument(
											    "--test_max_target_length"     ,    default=1_4_2     ,    type=_lowerCamelCase     ,    help=(
											        "The maximum total input sequence length after tokenization. Sequences longer "
											        "than this will be truncated, sequences shorter will be padded."
											    )     ,    )
											parser.add_argument("--freeze_encoder"     ,    action="store_true"						)
											parser.add_argument("--freeze_embeds"     ,    action="store_true"						)
											parser.add_argument("--sortish_sampler"     ,    action="store_true"     ,    default=_lowerCamelCase						)
											parser.add_argument("--overwrite_output_dir"     ,    action="store_true"     ,    default=_lowerCamelCase						)
											parser.add_argument("--max_tokens_per_batch"     ,    type=_lowerCamelCase     ,    default=_lowerCamelCase						)
											parser.add_argument("--logger_name"     ,    type=_lowerCamelCase     ,    choices=["default", "wandb", "wandb_shared"]     ,    default="default"						)
											parser.add_argument("--n_train"     ,    type=_lowerCamelCase     ,    default=-1     ,    required=_lowerCamelCase     ,    help="# examples. -1 means use all."						)
											parser.add_argument("--n_val"     ,    type=_lowerCamelCase     ,    default=5_0_0     ,    required=_lowerCamelCase     ,    help="# examples. -1 means use all."						)
											parser.add_argument("--n_test"     ,    type=_lowerCamelCase     ,    default=-1     ,    required=_lowerCamelCase     ,    help="# examples. -1 means use all."						)
											parser.add_argument(
											    "--task"     ,    type=_lowerCamelCase     ,    default="summarization"     ,    required=_lowerCamelCase     ,    help="# examples. -1 means use all."						)
											parser.add_argument("--label_smoothing"     ,    type=_lowerCamelCase     ,    default=0.0     ,    required=_lowerCamelCase						)
											parser.add_argument("--src_lang"     ,    type=_lowerCamelCase     ,    default=""     ,    required=_lowerCamelCase						)
											parser.add_argument("--tgt_lang"     ,    type=_lowerCamelCase     ,    default=""     ,    required=_lowerCamelCase						)
											parser.add_argument("--eval_beams"     ,    type=_lowerCamelCase     ,    default=_lowerCamelCase     ,    required=_lowerCamelCase						)
											parser.add_argument(
											    "--val_metric"     ,    type=_lowerCamelCase     ,    default=_lowerCamelCase     ,    required=_lowerCamelCase     ,    choices=["bleu", "rouge2", "loss", None]						)
											parser.add_argument("--eval_max_gen_length"     ,    type=_lowerCamelCase     ,    default=_lowerCamelCase     ,    help="never generate more than n tokens"						)
											parser.add_argument("--save_top_k"     ,    type=_lowerCamelCase     ,    default=1     ,    required=_lowerCamelCase     ,    help="How many checkpoints to save"						)
											parser.add_argument(
											    "--early_stopping_patience"     ,    type=_lowerCamelCase     ,    default=-1     ,    required=_lowerCamelCase     ,    help=(
											        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
											        " val_check_interval will effect it."
											    )     ,    )
											return parser






class     UpperCamelCase(  __UpperCAmelCase   ):
					snake_case_		:				Dict										=	"translation"
					snake_case_		:				Optional[Any]										=	["loss"]
					snake_case_		:				Optional[int]										=	["bleu"]
					snake_case_		:				int										=	"bleu"


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Optional[Any]:




											'''simple docstring'''

											super().__init__(_lowerCamelCase     ,    **_lowerCamelCase						)
											__snake_case				=		hparams.src_lang
											__snake_case				=		hparams.tgt_lang






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Optional[Any]:




											'''simple docstring'''

											return calculate_bleu(_lowerCamelCase     ,    _lowerCamelCase						)





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=None )						->	List[str]:


						'''simple docstring'''






						Path(args.output_dir ).mkdir(exist_ok=_lowerCAmelCase )
						check_output_dir(_lowerCAmelCase      ,	expected_items=3 )

						if model is None:
												if "summarization" in args.task:
																		__snake_case				=		SummarizationModule(_lowerCAmelCase )
												else:
																		__snake_case				=		TranslationModule(_lowerCAmelCase )
						__snake_case				=		Path(args.data_dir ).name
						if (
						    args.logger_name == "default"
						    or args.fast_dev_run
						    or str(args.output_dir ).startswith("/tmp" )
						    or str(args.output_dir ).startswith("/var" )
						):
												__snake_case				=		True  # don't pollute wandb logs unnecessarily
						elif args.logger_name == "wandb":
												from pytorch_lightning.loggers import WandbLogger

												__snake_case				=		os.environ.get("WANDB_PROJECT"      ,	_lowerCAmelCase )
												__snake_case				=		WandbLogger(name=model.output_dir.name      ,	project=_lowerCAmelCase )

						elif args.logger_name == "wandb_shared":
												from pytorch_lightning.loggers import WandbLogger

												__snake_case				=		WandbLogger(name=model.output_dir.name      ,	project=F'''hf_{dataset}''' )

						if args.early_stopping_patience >= 0:
												__snake_case				=		get_early_stopping_callback(model.val_metric      ,	args.early_stopping_patience )
						else:
												__snake_case				=		False

						__snake_case				=		args.val_metric == "loss"
						__snake_case				=		generic_train(
						    _lowerCAmelCase      ,	_lowerCAmelCase      ,	logging_callback=SeqaSeqLoggingCallback()      ,	checkpoint_callback=get_checkpoint_callback(
						        args.output_dir      ,	model.val_metric      ,	args.save_top_k      ,	_lowerCAmelCase )      ,	early_stopping_callback=_lowerCAmelCase      ,	logger=_lowerCAmelCase      ,	)
						pickle_save(model.hparams      ,	model.output_dir / "hparams.pkl" )
						if not args.do_predict:
												return model

						__snake_case				=		""
						__snake_case				=		sorted(glob.glob(os.path.join(args.output_dir      ,	"*.ckpt" )      ,	recursive=_lowerCAmelCase ) )
						if checkpoints:
												__snake_case				=		checkpoints[-1]
												__snake_case				=		checkpoints[-1]
						trainer.logger.log_hyperparams(model.hparams )

						# test() without a model tests using the best checkpoint automatically
						trainer.test()
						return model


if __name__ == "__main__":
	A							:					List[str]     						=     argparse.ArgumentParser()
	A							:					Optional[Any]     						=     pl.Trainer.add_argparse_args(parser)
	A							:					Tuple     						=     SummarizationModule.add_model_specific_args(parser, os.getcwd())

	A							:					int     						=     parser.parse_args()

	main(args)









import os
from datetime import datetime as dt

from github import Github


A							:					Union[str, Any]     						=     [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






						__snake_case				=		Github(os.environ["GITHUB_TOKEN"] )
						__snake_case				=		g.get_repo("huggingface/diffusers" )
						__snake_case				=		repo.get_issues(state="open" )

						for issue in open_issues:
												__snake_case				=		sorted(issue.get_comments()      ,	key=lambda _lowerCAmelCase : i.created_at      ,	reverse=_lowerCAmelCase )
												__snake_case				=		comments[0] if len(_lowerCAmelCase ) > 0 else None
												if (
												    last_comment is not None
												    and last_comment.user.login == "github-actions[bot]"
												    and (dt.utcnow() - issue.updated_at).days > 7
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
												):
																		# Closes the issue after 7 days of inactivity since the Stalebot notification.
																		issue.edit(state="closed" )
												elif (
												    "stale" in issue.get_labels()
												    and last_comment is not None
												    and last_comment.user.login != "github-actions[bot]"
												):
																		# Opens the issue if someone other than Stalebot commented.
																		issue.edit(state="open" )
																		issue.remove_from_labels("stale" )
												elif (
												    (dt.utcnow() - issue.updated_at).days > 23
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
												):
																		# Post a Stalebot notification after 23 days of inactivity.
																		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/diffusers/blob/main/CONTRIBUTING.md) "
																		    "are likely to be ignored." )
																		issue.add_to_labels("stale" )


if __name__ == "__main__":
	main()





