





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


SCREAMING_SNAKE_CASE_   :      Tuple			=   16
SCREAMING_SNAKE_CASE_   :      Tuple			=   32



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case = 16							)		->    str:
		__lowercase											=     AutoTokenizer.from_pretrained('bert-base-cased'							)
		__lowercase											=     DatasetDict(
		    {
		        'train': dataset['train'].select(snake_case							),
		        'validation': dataset['train'].select(snake_case							),
		        'test': dataset['validation'],
		    }							)

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

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

				return tokenizer.pad(
				    snake_case						,   padding='longest'						,   max_length=snake_case						,   pad_to_multiple_of=snake_case						,   return_tensors='pt'						,   )

		# Instantiate dataloaders.
		__lowercase											=     DataLoader(
		    tokenized_datasets['train']						,   shuffle=snake_case						,   collate_fn=snake_case						,   batch_size=snake_case							)
		__lowercase											=     DataLoader(
		    tokenized_datasets['validation']						,   shuffle=snake_case						,   collate_fn=snake_case						,   batch_size=snake_case							)

		__lowercase											=     DataLoader(
		    tokenized_datasets['test']						,   shuffle=snake_case						,   collate_fn=snake_case						,   batch_size=snake_case							)

		return train_dataloader, eval_dataloader, test_dataloader



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

		__lowercase											=     evaluate.load('glue'						,   'mrpc'							)

		# If the batch size is too big we use gradient accumulation
		__lowercase											=     1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__lowercase											=     batch_size // MAX_GPU_BATCH_SIZE
				__lowercase											=     MAX_GPU_BATCH_SIZE

		set_seed(snake_case							)

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

				# Instantiate optimizer
				__lowercase											=     AdamW(params=model.parameters()						,   lr=snake_case							)

				# Instantiate scheduler
				__lowercase											=     get_linear_schedule_with_warmup(
				    optimizer=snake_case						,   num_warmup_steps=100						,   num_training_steps=(len(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.
				__lowercase						,      __lowercase						,      __lowercase						,      __lowercase						,      __lowercase											=     accelerator.prepare(
				    snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)

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

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

						__lowercase											=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"epoch {epoch}:"						,   snake_case							)

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



def  SCREAMING_SNAKE_CASE				(      )		->    int:
		__lowercase											=     argparse.ArgumentParser(description='Simple example of training script.'							)
		parser.add_argument(
		    '--mixed_precision'						,   type=snake_case						,   default=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=snake_case						,   default=3						,   help='The number of splits to perform across the dataset'							)
		__lowercase											=     parser.parse_args()
		__lowercase											=     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(snake_case						,   snake_case							)


if __name__ == "__main__":
		main()











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




