






import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__snake_case    :    Optional[int]         =     16
__snake_case    :    Any         =     32







def 	_UpperCamelCase       (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Any = 16		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						AutoTokenizer.from_pretrained('bert-base-cased'		)
						lowerCAmelCase__    						=						load_dataset('glue'    ,       'mrpc'		)

						def tokenize_function(UpperCamelCase_       :					List[str]		):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__    						=						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():
												lowerCAmelCase__    						=						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
						lowerCAmelCase__    						=						tokenized_datasets.rename_column('label'    ,       'labels'		)

						def collate_fn(UpperCamelCase_       :					Dict		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowerCAmelCase__    						=						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":
																		lowerCAmelCase__    						=						16
												elif accelerator.mixed_precision != "no":
																		lowerCAmelCase__    						=						8
												else:
																		lowerCAmelCase__    						=						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.
						lowerCAmelCase__    						=						DataLoader(
						    tokenized_datasets['train']    ,       shuffle=_SCREAMING_SNAKE_CASE    ,       collate_fn=_SCREAMING_SNAKE_CASE    ,       batch_size=_SCREAMING_SNAKE_CASE		)
						lowerCAmelCase__    						=						DataLoader(
						    tokenized_datasets['validation']    ,       shuffle=_SCREAMING_SNAKE_CASE    ,       collate_fn=_SCREAMING_SNAKE_CASE    ,       batch_size=_SCREAMING_SNAKE_CASE		)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					__snake_case    :    List[str]         =     mocked_dataloaders  # noqa: F811







def 	_UpperCamelCase       (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[str]		)  ->   Dict:

						"""simple docstring"""



						if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,       _SCREAMING_SNAKE_CASE		) == "1":
												lowerCAmelCase__    						=						2
						# Initialize accelerator
						lowerCAmelCase__    						=						Accelerator(cpu=args.cpu    ,       mixed_precision=args.mixed_precision		)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowerCAmelCase__    						=						config['lr']
						lowerCAmelCase__    						=						int(config['num_epochs']		)
						lowerCAmelCase__    						=						int(config['seed']		)
						lowerCAmelCase__    						=						int(config['batch_size']		)

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

						# New Code #
						# We now can define an inner training loop function. It should take a batch size as the only parameter,
						# and build the dataloaders in there.
						# It also gets our decorator
						@find_executable_batch_size(starting_batch_size=_SCREAMING_SNAKE_CASE		)
						def inner_training_loop(UpperCamelCase_       :					Optional[Any]		):
												# And now just move everything below under this function
												# We need to bring in the Accelerator object from earlier
												nonlocal accelerator
												# And reset all of its attributes that could hold onto any memory:
												accelerator.free_memory()

												# Then we can declare the model, optimizer, and everything else:
												set_seed(_SCREAMING_SNAKE_CASE		)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowerCAmelCase__    						=						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).
												lowerCAmelCase__    						=						model.to(accelerator.device		)

												# Instantiate optimizer
												lowerCAmelCase__    						=						AdamW(params=model.parameters()    ,       lr=_SCREAMING_SNAKE_CASE		)
												lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataloaders(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE		)

												# Instantiate scheduler
												lowerCAmelCase__    						=						get_linear_schedule_with_warmup(
												    optimizer=_SCREAMING_SNAKE_CASE    ,       num_warmup_steps=100    ,       num_training_steps=(len(_SCREAMING_SNAKE_CASE		) * num_epochs)    ,       )

												# 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.
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						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		)
																								lowerCAmelCase__    						=						model(**_SCREAMING_SNAKE_CASE		)
																								lowerCAmelCase__    						=						outputs.loss
																								accelerator.backward(_SCREAMING_SNAKE_CASE		)
																								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():
																														lowerCAmelCase__    						=						model(**_SCREAMING_SNAKE_CASE		)
																								lowerCAmelCase__    						=						outputs.logits.argmax(dim=-1		)
																								lowerCAmelCase__						,       lowerCAmelCase__    						=						accelerator.gather_for_metrics((predictions, batch['labels'])		)
																								metric.add_batch(
																								    predictions=_SCREAMING_SNAKE_CASE    ,       references=_SCREAMING_SNAKE_CASE    ,       )

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

												# New Code #
												# And call it at the end with no arguments
												# Note: You could also refactor this outside of your training loop function
						inner_training_loop()







def 	_UpperCamelCase       (    )  ->   Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						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.'		)
						lowerCAmelCase__    						=						parser.parse_args()
						lowerCAmelCase__    						=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
					main()













from __future__ import annotations

from functools import lru_cache
from math import ceil

__snake_case    :    Union[str, Any]         =     1_00

__snake_case    :    Tuple         =     set(range(3, NUM_PRIMES, 2))
primes.add(2)
__snake_case    :    int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=100		)
def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   set[int]:

						"""simple docstring"""



						if number_to_partition < 0:
												return set()
						elif number_to_partition == 0:
												return {1}

						lowerCAmelCase__    						=						set()
						lowerCAmelCase__    						=						42
						lowerCAmelCase__    						=						42

						for prime in primes:
												if prime > number_to_partition:
																		continue
												for sub in partition(number_to_partition - prime		):
																		ret.add(sub * prime		)

						return ret







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 5000		)  ->   int | None:

						"""simple docstring"""



						for number_to_partition in range(1    ,       UpperCamelCase_		):
												if len(partition(UpperCamelCase_		)		) > number_unique_partitions:
																		return number_to_partition
						return None


if __name__ == "__main__":
					print(f'{solution() = }')





