


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           =		1_6
lowerCAmelCase           =		3_2







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__   , lowercase__ = 1_6	)						->	Tuple:

		'''simple docstring'''







		__lowercase=			AutoTokenizer.from_pretrained('bert-base-cased'	)
		__lowercase=			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)
				__lowercase=			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():
				__lowercase=			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
		__lowercase=			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.
				__lowercase=			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":
						__lowercase=			1_6
				elif accelerator.mixed_precision != "no":
						__lowercase=			8
				else:
						__lowercase=			None

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

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

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

		return train_dataloader, eval_dataloader, test_dataloader







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	List[str]:

		'''simple docstring'''







		__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(lowercase__	)

		# 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(lowercase__	):
				__lowercase,							__lowercase,							__lowercase=			get_fold_dataloaders(
				    lowercase__   , lowercase__   , lowercase__   , lowercase__   , )
				# 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=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).
				__lowercase=			model.to(accelerator.device	)

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

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

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







def        _lowerCamelCase(	)						->	Tuple:

		'''simple docstring'''







		__lowercase=			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'	)
		__lowercase=			parser.parse_args()
		__lowercase=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
		training_function(lowercase__   , lowercase__	)


if __name__ == "__main__":
	main()










def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		if collection == []:
				return []

		# get some information about the collection
		__lowercase=			len(lowercase__	)
		__lowercase=			max(lowercase__	)
		__lowercase=			min(lowercase__	)

		# create the counting array
		__lowercase=			coll_max + 1 - coll_min
		__lowercase=			[0] * counting_arr_length

		# count how much a number appears in the collection
		for number in collection:
				counting_arr[number - coll_min] += 1

		# sum each position with it's predecessors. now, counting_arr[i] tells
		# us how many elements <= i has in the collection
		for i in range(1   , lowercase__	):
				__lowercase=			counting_arr[i] + counting_arr[i - 1]

		# create the output collection
		__lowercase=			[0] * coll_len

		# place the elements in the output, respecting the original order (stable
		# sort) from end to begin, updating counting_arr
		for i in reversed(range(0   , lowercase__	)	):
				__lowercase=			collection[i]
				counting_arr[collection[i] - coll_min] -= 1

		return ordered







def        _lowerCamelCase(	lowercase__	)						->	List[str]:

		'''simple docstring'''







		return "".join([chr(lowercase__	) for i in counting_sort([ord(lowercase__	) for c in string]	)]	)


if __name__ == "__main__":
	# Test string sort
	assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

	lowerCAmelCase           =		input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase           =		[int(item) for item in user_input.split(''',''')]
	print(counting_sort(unsorted))






