
'''simple docstring'''


import argparse
import gc
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


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




def lowercase_	(   _lowercase				)     ->							Any:
					'''simple docstring'''

					return int(x / 2**20				)

class 	__lowercase				:

			def __enter__(self      ):
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								lowerCamelCase_					:      str     =		torch.cuda.memory_allocated()
								return self






			def __exit__(self			,   *A      ):
								gc.collect()
								torch.cuda.empty_cache()
								lowerCamelCase_					:      Any     =		torch.cuda.memory_allocated()
								lowerCamelCase_					:      Optional[Any]     =		torch.cuda.max_memory_allocated()
								lowerCamelCase_					:      Dict     =		bamb(self.end - self.begin      )
								lowerCamelCase_					:      List[str]     =		bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def lowercase_	(   _lowercase			,       _lowercase = 16			,       _lowercase = "bert-base-cased"			,       _lowercase = 320			,       _lowercase = 160			,       )     ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_					:      Dict     =		AutoTokenizer.from_pretrained(_lowercase				)
					lowerCamelCase_					:      Dict     =		load_dataset(
					    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"""train[:{n_train}]""", '''validation''': F"""validation[:{n_val}]"""}				)

					def tokenize_function(_lowercase				):
										# max_length=None => use the model max length (it's actually the default)
										lowerCamelCase_					:      List[str]     =		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
					lowerCamelCase_					:      Optional[Any]     =		datasets.map(
					    _lowercase			,       batched=_lowercase			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=_lowercase				)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					lowerCamelCase_					:      Union[str, Any]     =		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.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(_lowercase			,       padding='''max_length'''			,       max_length=128			,       return_tensors='''pt'''				)
										return tokenizer.pad(_lowercase			,       padding='''longest'''			,       return_tensors='''pt'''				)

					# Instantiate dataloaders.
					lowerCamelCase_					:      List[Any]     =		DataLoader(
					    tokenized_datasets['''train''']			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase				)
					lowerCamelCase_					:      List[str]     =		DataLoader(
					    tokenized_datasets['''validation''']			,       shuffle=_lowercase			,       collate_fn=_lowercase			,       batch_size=_lowercase				)

					return train_dataloader, eval_dataloader




def lowercase_	(   _lowercase			,       _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		Accelerator()

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

					set_seed(_lowercase				)
					lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		get_dataloaders(_lowercase			,       _lowercase			,       _lowercase			,       args.n_train			,       args.n_val				)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCamelCase_					:      str     =		AutoModelForSequenceClassification.from_pretrained(_lowercase			,       return_dict=_lowercase				)

					# Instantiate optimizer
					lowerCamelCase_					:      Optional[int]     =		(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					lowerCamelCase_					:      int     =		optimizer_cls(params=model.parameters()			,       lr=_lowercase				)

					if accelerator.state.deepspeed_plugin is not None:
										lowerCamelCase_					:      List[str]     =		accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										lowerCamelCase_					:      Any     =		1
					lowerCamelCase_					:      List[Any]     =		(len(_lowercase				) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										lowerCamelCase_					:      List[Any]     =		get_linear_schedule_with_warmup(
										    optimizer=_lowercase			,       num_warmup_steps=0			,       num_training_steps=_lowercase			,       )
					else:
										lowerCamelCase_					:      List[str]     =		DummyScheduler(_lowercase			,       total_num_steps=_lowercase			,       warmup_num_steps=0				)

					# 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_					:      Union[str, Any]     =		accelerator.prepare(
					    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

					# We need to keep track of how many total steps we have iterated over
					lowerCamelCase_					:      Dict     =		0
					# We also need to keep track of the stating epoch so files are named properly
					lowerCamelCase_					:      Dict     =		0

					# Now we train the model
					lowerCamelCase_					:      Dict     =		{}
					for epoch in range(_lowercase			,       _lowercase				):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_lowercase				):
																				lowerCamelCase_					:      Tuple     =		model(**_lowercase				)
																				lowerCamelCase_					:      Optional[Any]     =		outputs.loss
																				lowerCamelCase_					:      List[str]     =		loss / gradient_accumulation_steps
																				accelerator.backward(_lowercase				)
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

																				overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin				)				)				)
										accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used				)				)
										accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked				)				)
										accelerator.print(
										    '''Total Peak Memory consumed during the train (max): {}'''.format(
										        tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
										lowerCamelCase_					:      Union[str, Any]     =		tracemalloc.peaked + bamb(tracemalloc.begin				)
										if args.peak_memory_upper_bound is not None:
															assert (
															    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
															), "Peak memory usage exceeded the upper bound"

					accelerator.wait_for_everyone()
					if accelerator.is_main_process:
										with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''				)			,       '''w'''				) as f:
															json.dump(_lowercase			,       _lowercase				)




def lowercase_	(   )     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      Dict     =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
					parser.add_argument(
					    '''--model_name_or_path'''			,       type=_lowercase			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=_lowercase			,       )
					parser.add_argument(
					    '''--output_dir'''			,       type=_lowercase			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
					parser.add_argument(
					    '''--peak_memory_upper_bound'''			,       type=_lowercase			,       default=_lowercase			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
					parser.add_argument(
					    '''--n_train'''			,       type=_lowercase			,       default=320			,       help='''Number of training examples to use.'''			,       )
					parser.add_argument(
					    '''--n_val'''			,       type=_lowercase			,       default=160			,       help='''Number of validation examples to use.'''			,       )
					parser.add_argument(
					    '''--num_epochs'''			,       type=_lowercase			,       default=1			,       help='''Number of train epochs.'''			,       )
					lowerCamelCase_					:      str     =		parser.parse_args()
					lowerCamelCase_					:      List[Any]     =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
					training_function(_lowercase			,       _lowercase				)


if __name__ == "__main__":
					main()




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


