



'''simple docstring'''




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a     :    List[str]      						=					1_6
a     :    List[Any]      						=					3_2



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = 1_6	)   ->    Any:
							UpperCAmelCase  :	Dict							  =	AutoTokenizer.from_pretrained("""bert-base-cased"""	)
							UpperCAmelCase  :	Optional[Any]							  =	DatasetDict(
							    {
							        """train""": dataset["""train"""].select(_lowercase	),
							        """validation""": dataset["""train"""].select(_lowercase	),
							        """test""": dataset["""validation"""],
							    }	)

							def tokenize_function(_lowercase	):
														# max_length=None => use the model max length (it's actually the default)
														UpperCAmelCase  :	str							  =	tokenizer(examples["""sentence1"""]			,       examples["""sentence2"""]			,       truncation=_lowercase			,       max_length=_lowercase	)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														UpperCAmelCase  :	Any							  =	datasets.map(
														    _lowercase			,       batched=_lowercase			,       remove_columns=["""idx""", """sentence1""", """sentence2"""]			,       )

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

							def collate_fn(_lowercase	):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														UpperCAmelCase  :	Any							  =	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					UpperCAmelCase  :	List[str]							  =	1_6
														elif accelerator.mixed_precision != "no":
																					UpperCAmelCase  :	Any							  =	8
														else:
																					UpperCAmelCase  :	Tuple							  =	None

														return tokenizer.pad(
														    _lowercase			,       padding="""longest"""			,       max_length=_lowercase			,       pad_to_multiple_of=_lowercase			,       return_tensors="""pt"""			,       )

							# Instantiate dataloaders.
							UpperCAmelCase  :	int							  =	DataLoader(
							    tokenized_datasets["""train"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)
							UpperCAmelCase  :	Tuple							  =	DataLoader(
							    tokenized_datasets["""validation"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)

							UpperCAmelCase  :	Tuple							  =	DataLoader(
							    tokenized_datasets["""test"""]			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase	)

							return train_dataloader, eval_dataloader, test_dataloader



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							# New Code #
							UpperCAmelCase  :	List[Any]							  =	[]
							# Download the dataset
							UpperCAmelCase  :	List[Any]							  =	load_dataset("""glue"""			,       """mrpc"""	)
							# Create our splits
							UpperCAmelCase  :	Dict							  =	StratifiedKFold(n_splits=int(args.num_folds	)	)
							# Initialize accelerator
							UpperCAmelCase  :	int							  =	Accelerator(cpu=args.cpu			,       mixed_precision=args.mixed_precision	)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCAmelCase  :	List[str]							  =	config["""lr"""]
							UpperCAmelCase  :	str							  =	int(config["""num_epochs"""]	)
							UpperCAmelCase  :	str							  =	int(config["""seed"""]	)
							UpperCAmelCase  :	Tuple							  =	int(config["""batch_size"""]	)

							UpperCAmelCase  :	Union[str, Any]							  =	evaluate.load("""glue"""			,       """mrpc"""	)

							# If the batch size is too big we use gradient accumulation
							UpperCAmelCase  :	Union[str, Any]							  =	1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														UpperCAmelCase  :	str							  =	batch_size // MAX_GPU_BATCH_SIZE
														UpperCAmelCase  :	Optional[int]							  =	MAX_GPU_BATCH_SIZE

							set_seed(_lowercase	)

							# New Code #
							# Create our folds:
							UpperCAmelCase  :	Optional[int]							  =	kfold.split(np.zeros(datasets["""train"""].num_rows	)			,       datasets["""train"""]["""label"""]	)
							UpperCAmelCase  :	Tuple							  =	[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(_lowercase	):
														UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	Tuple							  =	get_fold_dataloaders(
														    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														UpperCAmelCase  :	Optional[Any]							  =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,       return_dict=_lowercase	)

														# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
														# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
														# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
														UpperCAmelCase  :	Any							  =	model.to(accelerator.device	)

														# Instantiate optimizer
														UpperCAmelCase  :	Union[str, Any]							  =	AdamW(params=model.parameters()			,       lr=_lowercase	)

														# Instantiate scheduler
														UpperCAmelCase  :	Any							  =	get_linear_schedule_with_warmup(
														    optimizer=_lowercase			,       num_warmup_steps=1_0_0			,       num_training_steps=(len(_lowercase	) * num_epochs) // gradient_accumulation_steps			,       )

														# 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.
														UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	accelerator.prepare(
														    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)

														# Now we train the model
														for epoch in range(_lowercase	):
																					model.train()
																					for step, batch in enumerate(_lowercase	):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device	)
																												UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																												UpperCAmelCase  :	Any							  =	outputs.loss
																												UpperCAmelCase  :	Any							  =	loss / gradient_accumulation_steps
																												accelerator.backward(_lowercase	)
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

																					model.eval()
																					for step, batch in enumerate(_lowercase	):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device	)
																												with torch.no_grad():
																																			UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																												UpperCAmelCase  :	Any							  =	outputs.logits.argmax(dim=-1	)
																												UpperCAmelCase  ,  UpperCAmelCase  :	Optional[Any]							  =	accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																												metric.add_batch(
																												    predictions=_lowercase			,       references=_lowercase			,       )

																					UpperCAmelCase  :	Any							  =	metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'''epoch {epoch}:'''			,       _lowercase	)

														# New Code #
														# We also run predictions on the test set at the very end
														UpperCAmelCase  :	Optional[Any]							  =	[]
														for step, batch in enumerate(_lowercase	):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device	)
																					with torch.no_grad():
																												UpperCAmelCase  :	Optional[Any]							  =	model(**_lowercase	)
																					UpperCAmelCase  :	List[str]							  =	outputs.logits
																					UpperCAmelCase  ,  UpperCAmelCase  :	Dict							  =	accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																					fold_predictions.append(predictions.cpu()	)
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()	)
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(_lowercase			,       dim=0	)	)
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							UpperCAmelCase  :	Dict							  =	torch.cat(_lowercase			,       dim=0	)
							UpperCAmelCase  :	int							  =	torch.stack(_lowercase			,       dim=0	).sum(dim=0	).div(int(args.num_folds	)	).argmax(dim=-1	)
							UpperCAmelCase  :	List[Any]							  =	metric.compute(predictions=_lowercase			,       references=_lowercase	)
							accelerator.print("""Average test metrics from all folds:"""			,       _lowercase	)



def    __lowerCamelCase						(      )   ->    Optional[Any]:
							UpperCAmelCase  :	List[Any]							  =	argparse.ArgumentParser(description="""Simple example of training script."""	)
							parser.add_argument(
							    """--mixed_precision"""			,       type=_lowercase			,       default=_lowercase			,       choices=["""no""", """fp16""", """bf16""", """fp8"""]			,       help="""Whether to use mixed precision. Choose"""
							    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
							    """and an Nvidia Ampere GPU."""			,       )
							parser.add_argument("""--cpu"""			,       action="""store_true"""			,       help="""If passed, will train on the CPU."""	)
							# New Code #
							parser.add_argument("""--num_folds"""			,       type=_lowercase			,       default=3			,       help="""The number of splits to perform across the dataset"""	)
							UpperCAmelCase  :	Optional[int]							  =	parser.parse_args()
							UpperCAmelCase  :	Optional[int]							  =	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
							training_function(_lowercase			,       _lowercase	)


if __name__ == "__main__":
							main()






'''simple docstring'''




from datetime import datetime as dt
import os

from github import Github


a     :    int      						=					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	str							  =	Github(os.environ["""GITHUB_TOKEN"""]	)
							UpperCAmelCase  :	Dict							  =	g.get_repo("""huggingface/transformers"""	)
							UpperCAmelCase  :	int							  =	repo.get_issues(state="""open"""	)

							for issue in open_issues:
														UpperCAmelCase  :	Optional[int]							  =	sorted([comment for comment in issue.get_comments()]			,       key=lambda _lowercase	: i.created_at			,       reverse=_lowercase	)
														UpperCAmelCase  :	Any							  =	comments[0] if len(_lowercase	) > 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 >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""	)
														elif (
														    (dt.utcnow() - issue.updated_at).days > 2_3
														    and (dt.utcnow() - issue.created_at).days >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# print(f"Would add stale comment to {issue.number}")
																					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/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""	)


if __name__ == "__main__":
							main()

