


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


_lowercase       :      Tuple	=	16
_lowercase       :      Any	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     DatasetDict							,					UpperCamelCase__:     List[int]							,					UpperCamelCase__:     List[int]							,					UpperCamelCase__:     int = 16						) ->   Optional[int]:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				DatasetDict(
	    {
	        """train""": dataset["""train"""].select(snake_case__						),
	        """validation""": dataset["""train"""].select(snake_case__						),
	        """test""": dataset["""validation"""],
	    }						)

	def tokenize_function(UpperCamelCase__:     Optional[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				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():
		A								=				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
	A								=				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.
		A								=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A								=				16
		elif accelerator.mixed_precision != "no":
			A								=				8
		else:
			A								=				None

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

	# Instantiate dataloaders.
	A								=				DataLoader(
	    tokenized_datasets["""train"""]							,					shuffle=snake_case__							,					collate_fn=snake_case__							,					batch_size=snake_case__						)
	A								=				DataLoader(
	    tokenized_datasets["""validation"""]							,					shuffle=snake_case__							,					collate_fn=snake_case__							,					batch_size=snake_case__						)

	A								=				DataLoader(
	    tokenized_datasets["""test"""]							,					shuffle=snake_case__							,					collate_fn=snake_case__							,					batch_size=snake_case__						)

	return train_dataloader, eval_dataloader, test_dataloader





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     List[str]						) ->   List[str]:





	"""simple docstring"""







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

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

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

	set_seed(snake_case__						)

	# New Code #
	# Create our folds:
	A								=				kfold.split(np.zeros(datasets["""train"""].num_rows						)							,					datasets["""train"""]["""label"""]						)
	A								=				[]
	# Iterate over them
	for i, (train_idxs, valid_idxs) in enumerate(snake_case__						):
		A					,			A					,			A								=				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)
		A								=				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).
		A								=				model.to(accelerator.device						)

		# Instantiate optimizer
		A								=				AdamW(params=model.parameters()							,					lr=snake_case__						)

		# Instantiate scheduler
		A								=				get_linear_schedule_with_warmup(
		    optimizer=snake_case__							,					num_warmup_steps=1_00							,					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.
		A					,			A					,			A					,			A					,			A								=				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						)
				A								=				model(**snake_case__						)
				A								=				outputs.loss
				A								=				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():
					A								=				model(**snake_case__						)
				A								=				outputs.logits.argmax(dim=-1						)
				A					,			A								=				accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
				metric.add_batch(
				    predictions=snake_case__							,					references=snake_case__							,					)

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





def   _lowerCAmelCase				(   ) ->   Tuple:





	"""simple docstring"""







	A								=				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"""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__							,					snake_case__						)


if __name__ == "__main__":
				main()







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



