




'''simple docstring'''





from datetime import datetime as dt
import os

from github import Github


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




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      Github(os.environ["""GITHUB_TOKEN"""]       )
				_lowerCAmelCase    :    str										=      g.get_repo("""huggingface/transformers"""       )
				_lowerCAmelCase    :    List[str]										=      repo.get_issues(state="""open"""       )

				for issue in open_issues:
								_lowerCAmelCase    :    Union[str, Any]										=      sorted([comment for comment in issue.get_comments()]      ,    key=lambda UpperCamelCase_       : i.created_at      ,    reverse=__lowerCAmelCase       )
								_lowerCAmelCase    :    Union[str, Any]										=      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()       )
								):
												# 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 > 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()       )
								):
												# 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()







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
#
########################################################################


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    int										=      DatasetDict(
				    {
				        """train""": dataset["""train"""].select(UpperCamelCase_       ),
				        """validation""": dataset["""train"""].select(UpperCamelCase_       ),
				        """test""": dataset["""validation"""],
				    }       )

				def tokenize_function(UpperCamelCase_ : str       ):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCAmelCase    :    List[Any]										=      tokenizer(examples["""sentence1"""]      ,    examples["""sentence2"""]      ,    truncation=UpperCamelCase_      ,    max_length=UpperCamelCase_       )
								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():
								_lowerCAmelCase    :    List[str]										=      datasets.map(
								    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    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
				_lowerCAmelCase    :    Optional[Any]										=      tokenized_datasets.rename_column("""label"""      ,    """labels"""       )

				def collate_fn(UpperCamelCase_ : Optional[Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCAmelCase    :    List[str]										=      128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_lowerCAmelCase    :    Optional[Any]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    int										=      8
								else:
												_lowerCAmelCase    :    Dict										=      None

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

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Optional[Any]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				return train_dataloader, eval_dataloader, test_dataloader




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

				_lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

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

				set_seed(UpperCamelCase_       )

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

								# 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).
								_lowerCAmelCase    :    Union[str, Any]										=      model.to(accelerator.device       )

								# Instantiate optimizer
								_lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

								# Instantiate scheduler
								_lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_       ) * 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.
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      accelerator.prepare(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )

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

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

												_lowerCAmelCase    :    List[Any]										=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_       )

								# New Code #
								# We also run predictions on the test set at the very end
								_lowerCAmelCase    :    Dict										=      []
								for step, batch in enumerate(UpperCamelCase_       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																_lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )
												_lowerCAmelCase    :    List[Any]										=      outputs.logits
												_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      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(UpperCamelCase_      ,    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:
				_lowerCAmelCase    :    Tuple										=      torch.cat(UpperCamelCase_      ,    dim=0       )
				_lowerCAmelCase    :    Tuple										=      torch.stack(UpperCamelCase_      ,    dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
				_lowerCAmelCase    :    Union[str, Any]										=      metric.compute(predictions=UpperCamelCase_      ,    references=UpperCamelCase_       )
				accelerator.print("""Average test metrics from all folds:"""      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      argparse.ArgumentParser(description="""Simple example of training script."""       )
				parser.add_argument(
				    """--mixed_precision"""      ,    type=UpperCamelCase_      ,    default=UpperCamelCase_      ,    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=UpperCamelCase_      ,    default=3      ,    help="""The number of splits to perform across the dataset"""       )
				_lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
				_lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
	main()

