





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				:    Optional[Any]       =   16
__A				:    Dict       =   32





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 16 )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''bert-base-cased''' )
			UpperCAmelCase   		=				DatasetDict(
			    {
			        '''train''': dataset['''train'''].select(UpperCamelCase__ ),
			        '''validation''': dataset['''train'''].select(UpperCamelCase__ ),
			        '''test''': dataset['''validation'''],
			    } )

			def tokenize_function(UpperCamelCase__ ):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase   		=				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():
						UpperCAmelCase   		=				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
			UpperCAmelCase   		=				tokenized_datasets.rename_column('''label'''			,			'''labels''' )

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

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

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

			UpperCAmelCase   		=				DataLoader(
			    tokenized_datasets['''test''']			,			shuffle=UpperCamelCase__			,			collate_fn=UpperCamelCase__			,			batch_size=UpperCamelCase__ )

			return train_dataloader, eval_dataloader, test_dataloader





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Optional[Any]:







			'''simple docstring'''

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

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

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

			set_seed(UpperCamelCase__ )

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

						# Instantiate optimizer
						UpperCAmelCase   		=				AdamW(params=model.parameters()			,			lr=UpperCamelCase__ )

						# Instantiate scheduler
						UpperCAmelCase   		=				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.
						UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				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 )
												UpperCAmelCase   		=				model(**UpperCamelCase__ )
												UpperCAmelCase   		=				outputs.loss
												UpperCAmelCase   		=				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():
															UpperCAmelCase   		=				model(**UpperCamelCase__ )
												UpperCAmelCase   		=				outputs.logits.argmax(dim=-1 )
												UpperCAmelCase		,			UpperCAmelCase   		=				accelerator.gather_for_metrics((predictions, batch['''labels''']) )
												metric.add_batch(
												    predictions=UpperCamelCase__			,			references=UpperCamelCase__			,			)

									UpperCAmelCase   		=				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
						UpperCAmelCase   		=				[]
						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():
												UpperCAmelCase   		=				model(**UpperCamelCase__ )
									UpperCAmelCase   		=				outputs.logits
									UpperCAmelCase		,			UpperCAmelCase   		=				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:
			UpperCAmelCase   		=				torch.cat(UpperCamelCase__			,			dim=0 )
			UpperCAmelCase   		=				torch.stack(UpperCamelCase__			,			dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
			UpperCAmelCase   		=				metric.compute(predictions=UpperCamelCase__			,			references=UpperCamelCase__ )
			accelerator.print('''Average test metrics from all folds:'''			,			UpperCamelCase__ )





def 						__SCREAMING_SNAKE_CASE  (				)  ->				str:







			'''simple docstring'''

			UpperCAmelCase   		=				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''' )
			UpperCAmelCase   		=				parser.parse_args()
			UpperCAmelCase   		=				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(UpperCamelCase__			,			UpperCamelCase__ )


if __name__ == "__main__":
				main()










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				list[int]:







			'''simple docstring'''

			if length <= 0 or not isinstance(UpperCamelCase__			,			UpperCamelCase__ ):
						raise ValueError('''Length must be a positive integer.''' )
			return [n * (2 * n - 1) for n in range(UpperCamelCase__ )]


if __name__ == "__main__":
				print(hexagonal_numbers(length=5))
				print(hexagonal_numbers(length=10))



