"""simple docstring"""





from ....utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)




class  _snake_case		(    lowercase__ ):







    """simple docstring"""






    def __init__(      self							:							Union[str, Any]      ,    _A							:							Any      ,    _A							:							Tuple=None      ,    _A							:							int=2_0_4_8):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    config.__dict__
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    modal_hidden_size
           if num_labels:
                  _SCREAMING_SNAKE_CASE		:						Tuple			  =    num_labels



"""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
#
########################################################################


lowerCAmelCase_     =	16
lowerCAmelCase_     =	32



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    DatasetDict(
							    {
							        """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """test""": dataset["""validation"""],
							    }  )

							def tokenize_function(__SCREAMING_SNAKE_CASE  ):
														# max_length=None => use the model max length (it's actually the default)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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
							# starting with the main process first:
							with accelerator.main_process_first():
														_SCREAMING_SNAKE_CASE		:						str			  =    datasets.map(
														    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	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
							_SCREAMING_SNAKE_CASE		:						Any			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_SNAKE_CASE		:						Any			  =    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":
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE		:						Any			  =    8
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None

														return tokenizer.pad(
														    __SCREAMING_SNAKE_CASE     ,	padding="""longest"""     ,	max_length=__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,	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		:						Optional[int]			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    DataLoader(
							    tokenized_datasets["""test"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							return train_dataloader, eval_dataloader, test_dataloader



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

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(__SCREAMING_SNAKE_CASE  )

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

														# 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).
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.to(accelerator.device  )

														# Instantiate optimizer
														_SCREAMING_SNAKE_CASE		:						int			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														_SCREAMING_SNAKE_CASE		:						int			  =    get_linear_schedule_with_warmup(
														    optimizer=__SCREAMING_SNAKE_CASE     ,	num_warmup_steps=100     ,	num_training_steps=(len(__SCREAMING_SNAKE_CASE  ) * 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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    accelerator.prepare(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														# Now we train the model
														for epoch in range(__SCREAMING_SNAKE_CASE  ):
																					model.train()
																					for step, batch in enumerate(__SCREAMING_SNAKE_CASE  ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device  )
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						Dict			  =    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()

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

																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F"""epoch {epoch}:"""     ,	__SCREAMING_SNAKE_CASE  )

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



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


if __name__ == "__main__":
			main()


