




import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase     							=						16
__UpperCAmelCase     							=						32




def 					lowercase__				(					__snake_case   :							Accelerator					,	__snake_case   :							int = 16  ):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						AutoTokenizer.from_pretrained('bert-base-cased'  )
					UpperCAmelCase_   :				Dict						=						load_dataset('glue'					,	'mrpc'  )

					def tokenize_function(__snake_case   :							Optional[int]  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCAmelCase_   :				List[Any]						=						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():
										UpperCAmelCase_   :				Any						=						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
					UpperCAmelCase_   :				Union[str, Any]						=						tokenized_datasets.rename_column('label'					,	'labels'  )

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

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

					# Instantiate dataloaders.
					UpperCAmelCase_   :				List[Any]						=						DataLoader(
					    tokenized_datasets['train']					,	shuffle=__snake_case					,	collate_fn=__snake_case					,	batch_size=__snake_case					,	drop_last=__snake_case  )
					UpperCAmelCase_   :				str						=						DataLoader(
					    tokenized_datasets['validation']					,	shuffle=__snake_case					,	collate_fn=__snake_case					,	batch_size=__snake_case					,	drop_last=(accelerator.mixed_precision == 'fp8')					,	)

					return train_dataloader, eval_dataloader




def 					lowercase__				(					__snake_case   :							Optional[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase_   :				int						=						config['lr']
					UpperCAmelCase_   :				List[Any]						=						int(config['num_epochs']  )
					UpperCAmelCase_   :				Any						=						int(config['seed']  )
					UpperCAmelCase_   :				Optional[Any]						=						int(config['batch_size']  )

					UpperCAmelCase_   :				List[Any]						=						evaluate.load('glue'					,	'mrpc'  )

					# If the batch size is too big we use gradient accumulation
					UpperCAmelCase_   :				List[Any]						=						1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCAmelCase_   :				Any						=						batch_size // MAX_GPU_BATCH_SIZE
										UpperCAmelCase_   :				List[str]						=						MAX_GPU_BATCH_SIZE

					set_seed(__snake_case  )
					UpperCAmelCase_   :				List[Any]						=						get_dataloaders(__snake_case					,	__snake_case  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase_   :				Optional[int]						=						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).
					UpperCAmelCase_   :				List[Any]						=						model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCAmelCase_   :				Optional[Any]						=						AdamW(params=model.parameters()					,	lr=__snake_case  )

					# Instantiate scheduler
					UpperCAmelCase_   :				Union[str, Any]						=						get_linear_schedule_with_warmup(
					    optimizer=__snake_case					,	num_warmup_steps=100					,	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.

					UpperCAmelCase_   :				str						=						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  )
															UpperCAmelCase_   :				Tuple						=						model(**__snake_case  )
															UpperCAmelCase_   :				Tuple						=						outputs.loss
															UpperCAmelCase_   :				Optional[Any]						=						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():
																				UpperCAmelCase_   :				Union[str, Any]						=						model(**__snake_case  )
															UpperCAmelCase_   :				int						=						outputs.logits.argmax(dim=-1  )
															UpperCAmelCase_   :				Optional[int]						=						accelerator.gather_for_metrics((predictions, batch['labels'])  )
															metric.add_batch(
															    predictions=__snake_case					,	references=__snake_case					,	)

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




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						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.'  )
					UpperCAmelCase_   :				List[Any]						=						parser.parse_args()
					UpperCAmelCase_   :				str						=						{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__snake_case					,	__snake_case  )


if __name__ == "__main__":
		main()














from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__				(					__snake_case   :							int = 1_000_000					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				defaultdict						=						defaultdict(__snake_case  )

					for outer_width in range(3					,	(t_limit // 4) + 2  ):
										if outer_width * outer_width > t_limit:
															UpperCAmelCase_   :				Union[str, Any]						=						max(
															    ceil(sqrt(outer_width * outer_width - t_limit  )  )					,	1  )
										else:
															UpperCAmelCase_   :				int						=						1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(__snake_case					,	outer_width - 1					,	2  ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10  )


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






