



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


lowerCAmelCase__					  =  1_6
lowerCAmelCase__					  =  3_2


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__ = 16						):

				"""simple docstring"""


				lowercase__					:   Any													=					AutoTokenizer.from_pretrained("bert-base-cased"						)
				lowercase__					:   List[Any]													=					load_dataset("glue"					,			"mrpc"						)

				def tokenize_function(lowerCamelCase__						):
								# max_length=None => use the model max length (it's actually the default)
								lowercase__					:   List[str]													=					tokenizer(examples["sentence1"]					,			examples["sentence2"]					,			truncation=lowerCamelCase__					,			max_length=lowerCamelCase__						)
								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__					:   Union[str, Any]													=					datasets.map(
								    lowerCamelCase__					,			batched=lowerCamelCase__					,			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__					:   List[Any]													=					tokenized_datasets.rename_column("label"					,			"labels"						)

				def collate_fn(lowerCamelCase__						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase__					:   Optional[int]													=					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__					:   Any													=					16
								elif accelerator.mixed_precision != "no":
												lowercase__					:   int													=					8
								else:
												lowercase__					:   List[str]													=					None

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

				# Instantiate dataloaders.
				lowercase__					:   List[Any]													=					DataLoader(
				    tokenized_datasets["train"]					,			shuffle=lowerCamelCase__					,			collate_fn=lowerCamelCase__					,			batch_size=lowerCamelCase__						)
				lowercase__					:   List[Any]													=					DataLoader(
				    tokenized_datasets["validation"]					,			shuffle=lowerCamelCase__					,			collate_fn=lowerCamelCase__					,			batch_size=lowerCamelCase__						)

				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

				lowerCAmelCase__					  =  mocked_dataloaders  # noqa: F811


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

				lowercase__					:   Union[str, Any]													=					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=lowerCamelCase__						)
				def inner_training_loop(lowerCamelCase__						):
								# 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(lowerCamelCase__						)

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								lowercase__					:   Union[str, Any]													=					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,			return_dict=lowerCamelCase__						)

								# 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__					:   Any													=					model.to(accelerator.device						)

								# Instantiate optimizer
								lowercase__					:   Union[str, Any]													=					AdamW(params=model.parameters()					,			lr=lowerCamelCase__						)
								lowercase__							,							lowercase__					:   Union[str, Any]													=					get_dataloaders(lowerCamelCase__					,			lowerCamelCase__						)

								# Instantiate scheduler
								lowercase__					:   Any													=					get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase__					,			num_warmup_steps=100					,			num_training_steps=(len(lowerCamelCase__						) * 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.
								lowercase__							,							lowercase__							,							lowercase__							,							lowercase__							,							lowercase__					:   Optional[int]													=					accelerator.prepare(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						)

								# Now we train the model
								for epoch in range(lowerCamelCase__						):
												model.train()
												for step, batch in enumerate(lowerCamelCase__						):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device						)
																lowercase__					:   List[Any]													=					model(**lowerCamelCase__						)
																lowercase__					:   Union[str, Any]													=					outputs.loss
																accelerator.backward(lowerCamelCase__						)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

												lowercase__					:   List[Any]													=					metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""					,			lowerCamelCase__						)

    # 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  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					argparse.ArgumentParser(description="Simple example of training script."						)
				parser.add_argument(
				    "--mixed_precision"					,			type=lowerCamelCase__					,			default=lowerCamelCase__					,			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."						)
				lowercase__					:   Tuple													=					parser.parse_args()
				lowercase__					:   str													=					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowerCamelCase__					,			lowerCamelCase__						)


if __name__ == "__main__":
				main()









from math import ceil, sqrt


def  __lowerCamelCase (       lowerCamelCase__ = 1_000_000						):

				"""simple docstring"""


				lowercase__					:   int													=					0

				for outer_width in range(3					,			(limit // 4) + 2						):
								if outer_width**2 > limit:
												lowercase__					:   List[str]													=					max(ceil(sqrt(outer_width**2 - limit						)						)					,			1						)
								else:
												lowercase__					:   List[str]													=					1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


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




