



'''simple docstring'''

import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case_			=     16
snake_case_			=     32






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple       ):
				return int(x / 2**20       )


class 	a__     :


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

					"""simple docstring"""

					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.cuda.memory_allocated()
					return self






	def __exit__(self : List[str],					*__UpperCAmelCase : List[str]      )     ->			Any:

					"""simple docstring"""

					gc.collect()
					torch.cuda.empty_cache()
					SCREAMING_SNAKE_CASE  :						int      					= torch.cuda.memory_allocated()
					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.cuda.max_memory_allocated()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= bamb(self.end - self.begin      )
					SCREAMING_SNAKE_CASE  :						int      					= bamb(self.peak - self.begin      )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Accelerator    ,	_SCREAMING_SNAKE_CASE      :int = 16    ,	_SCREAMING_SNAKE_CASE      :str = "bert-base-cased"    ,	_SCREAMING_SNAKE_CASE      :int = 3_20    ,	_SCREAMING_SNAKE_CASE      :int = 1_60    ,	):
				SCREAMING_SNAKE_CASE  :						Tuple      					= AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= load_dataset(
				    '''glue'''    ,	'''mrpc'''    ,	split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}       )

				def tokenize_function(_SCREAMING_SNAKE_CASE      :int       ):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= tokenizer(examples['''sentence1''']    ,	examples['''sentence2''']    ,	truncation=_SCREAMING_SNAKE_CASE    ,	max_length=_SCREAMING_SNAKE_CASE       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				SCREAMING_SNAKE_CASE  :						Dict      					= datasets.map(
				    _SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE    ,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,	load_from_cache_file=_SCREAMING_SNAKE_CASE       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				SCREAMING_SNAKE_CASE  :						Tuple      					= tokenized_datasets.rename_column('''label'''    ,	'''labels'''       )

				def collate_fn(_SCREAMING_SNAKE_CASE      :List[str]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(_SCREAMING_SNAKE_CASE    ,	padding='''max_length'''    ,	max_length=1_28    ,	return_tensors='''pt'''       )
								return tokenizer.pad(_SCREAMING_SNAKE_CASE    ,	padding='''longest'''    ,	return_tensors='''pt'''       )

				# Instantiate dataloaders.
				SCREAMING_SNAKE_CASE  :						int      					= DataLoader(
				    tokenized_datasets['''train''']    ,	shuffle=_SCREAMING_SNAKE_CASE    ,	collate_fn=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= DataLoader(
				    tokenized_datasets['''validation''']    ,	shuffle=_SCREAMING_SNAKE_CASE    ,	collate_fn=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE       )

				return train_dataloader, eval_dataloader






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[Any]       ):
				# Initialize accelerator
				SCREAMING_SNAKE_CASE  :						int      					= Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				SCREAMING_SNAKE_CASE  :						List[Any]      					= config['''lr''']
				SCREAMING_SNAKE_CASE  :						List[Any]      					= int(config['''num_epochs''']       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= int(config['''seed''']       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= int(config['''batch_size''']       )
				SCREAMING_SNAKE_CASE  :						Dict      					= args.model_name_or_path

				set_seed(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= get_dataloaders(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	args.n_train    ,	args.n_val       )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE  :						List[Any]      					= AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE    ,	return_dict=_SCREAMING_SNAKE_CASE       )

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				SCREAMING_SNAKE_CASE  :						int      					= optimizer_cls(params=model.parameters()    ,	lr=_SCREAMING_SNAKE_CASE       )

				if accelerator.state.deepspeed_plugin is not None:
								SCREAMING_SNAKE_CASE  :						Any      					= accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								SCREAMING_SNAKE_CASE  :						Any      					= 1
				SCREAMING_SNAKE_CASE  :						Any      					= (len(_SCREAMING_SNAKE_CASE       ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								SCREAMING_SNAKE_CASE  :						int      					= get_linear_schedule_with_warmup(
								    optimizer=_SCREAMING_SNAKE_CASE    ,	num_warmup_steps=0    ,	num_training_steps=_SCREAMING_SNAKE_CASE    ,	)
				else:
								SCREAMING_SNAKE_CASE  :						Any      					= DummyScheduler(_SCREAMING_SNAKE_CASE    ,	total_num_steps=_SCREAMING_SNAKE_CASE    ,	warmup_num_steps=0       )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= accelerator.prepare(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# We need to keep track of how many total steps we have iterated over
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= 0
				# We also need to keep track of the stating epoch so files are named properly
				SCREAMING_SNAKE_CASE  :						Dict      					= 0

				# Now we train the model
				SCREAMING_SNAKE_CASE  :						Dict      					= {}
				for epoch in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(_SCREAMING_SNAKE_CASE       ):
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(**_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						str      					= outputs.loss
																SCREAMING_SNAKE_CASE  :						List[Any]      					= loss / gradient_accumulation_steps
																accelerator.backward(_SCREAMING_SNAKE_CASE       )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin       )       )       )
								accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used       )       )
								accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked       )       )
								accelerator.print(
								    '''Total Peak Memory consumed during the train (max): {}'''.format(
								        tracemalloc.peaked + bamb(tracemalloc.begin       )       )       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= tracemalloc.peaked + bamb(tracemalloc.begin       )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir    ,	'''peak_memory_utilization.json'''       )    ,	'''w'''       ) as f:
												json.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Dict      					= argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
				parser.add_argument(
				    '''--model_name_or_path'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default='''bert-base-cased'''    ,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,	required=_SCREAMING_SNAKE_CASE    ,	)
				parser.add_argument(
				    '''--output_dir'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default='''.'''    ,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,	)
				parser.add_argument(
				    '''--peak_memory_upper_bound'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=_SCREAMING_SNAKE_CASE    ,	help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''    ,	)
				parser.add_argument(
				    '''--n_train'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=3_20    ,	help='''Number of training examples to use.'''    ,	)
				parser.add_argument(
				    '''--n_val'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=1_60    ,	help='''Number of validation examples to use.'''    ,	)
				parser.add_argument(
				    '''--num_epochs'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=1    ,	help='''Number of train epochs.'''    ,	)
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= parser.parse_args()
				SCREAMING_SNAKE_CASE  :						Tuple      					= {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	main()









'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


snake_case_			=     [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Github(os.environ['''GITHUB_TOKEN''']       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= g.get_repo('''huggingface/diffusers'''       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= repo.get_issues(state='''open'''       )

				for issue in open_issues:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= sorted(issue.get_comments()    ,	key=lambda _SCREAMING_SNAKE_CASE       : i.created_at    ,	reverse=_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= comments[0] if len(_SCREAMING_SNAKE_CASE       ) > 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()




