


"""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


UpperCamelCase__				:Optional[Any]			   =	16
UpperCamelCase__				:Any			   =	32





def 				A_       ( snake_case__		)					->  Optional[Any]:
			return int(x / 2**20		)






class      A:

		"""simple docstring"""





		def __enter__( self   )      ->	Optional[int]:

					"""simple docstring"""



					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_UpperCamelCase					:List[Any]					     =	torch.cuda.memory_allocated()
					return self





		def __exit__( self					,				*SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					gc.collect()
					torch.cuda.empty_cache()
					_UpperCamelCase					:Any					     =	torch.cuda.memory_allocated()
					_UpperCamelCase					:str					     =	torch.cuda.max_memory_allocated()
					_UpperCamelCase					:Union[str, Any]					     =	bamb(self.end - self.begin   )
					_UpperCamelCase					:Any					     =	bamb(self.peak - self.begin   )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 				A_       ( snake_case__       ,					snake_case__ = 16       ,					snake_case__ = "bert-base-cased"       ,					snake_case__ = 3_20       ,					snake_case__ = 1_60       ,					)					->  Any:
			_UpperCamelCase					:Optional[int]					     =	AutoTokenizer.from_pretrained(snake_case__		)
			_UpperCamelCase					:Dict					     =	load_dataset(
			    '''glue'''       ,					'''mrpc'''       ,					split={'''train''': f"train[:{n_train}]", '''validation''': f"validation[:{n_val}]"}		)

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

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			_UpperCamelCase					:Optional[Any]					     =	tokenized_datasets.rename_column('''label'''       ,					'''labels'''		)

			def collate_fn(snake_case__		):
						# 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(snake_case__       ,					padding='''max_length'''       ,					max_length=1_28       ,					return_tensors='''pt'''		)
						return tokenizer.pad(snake_case__       ,					padding='''longest'''       ,					return_tensors='''pt'''		)

			# Instantiate dataloaders.
			_UpperCamelCase					:Dict					     =	DataLoader(
			    tokenized_datasets['''train''']       ,					shuffle=snake_case__       ,					collate_fn=snake_case__       ,					batch_size=snake_case__		)
			_UpperCamelCase					:List[str]					     =	DataLoader(
			    tokenized_datasets['''validation''']       ,					shuffle=snake_case__       ,					collate_fn=snake_case__       ,					batch_size=snake_case__		)

			return train_dataloader, eval_dataloader





def 				A_       ( snake_case__       ,					snake_case__		)					->  int:
			# Initialize accelerator
			_UpperCamelCase					:List[str]					     =	Accelerator()

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

			set_seed(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Dict					     =	get_dataloaders(snake_case__       ,					snake_case__       ,					snake_case__       ,					args.n_train       ,					args.n_val		)

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

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

			if accelerator.state.deepspeed_plugin is not None:
						_UpperCamelCase					:Any					     =	accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						_UpperCamelCase					:Dict					     =	1
			_UpperCamelCase					:Optional[Any]					     =	(len(snake_case__		) * num_epochs) // gradient_accumulation_steps

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

			# We need to keep track of how many total steps we have iterated over
			_UpperCamelCase					:Any					     =	0
			# We also need to keep track of the stating epoch so files are named properly
			_UpperCamelCase					:List[Any]					     =	0

			# Now we train the model
			_UpperCamelCase					:Dict					     =	{}
			for epoch in range(snake_case__       ,					snake_case__		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(snake_case__		):
												_UpperCamelCase					:List[Any]					     =	model(**snake_case__		)
												_UpperCamelCase					:List[str]					     =	outputs.loss
												_UpperCamelCase					:str					     =	loss / gradient_accumulation_steps
												accelerator.backward(snake_case__		)
												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		)		)		)
						_UpperCamelCase					:Dict					     =	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(snake_case__       ,					snake_case__		)





def 				A_       ( )					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''       ,					type=snake_case__       ,					default='''bert-base-cased'''       ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,					required=snake_case__       ,					)
			parser.add_argument(
			    '''--output_dir'''       ,					type=snake_case__       ,					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=snake_case__       ,					default=snake_case__       ,					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=snake_case__       ,					default=3_20       ,					help='''Number of training examples to use.'''       ,					)
			parser.add_argument(
			    '''--n_val'''       ,					type=snake_case__       ,					default=1_60       ,					help='''Number of validation examples to use.'''       ,					)
			parser.add_argument(
			    '''--num_epochs'''       ,					type=snake_case__       ,					default=1       ,					help='''Number of train epochs.'''       ,					)
			_UpperCamelCase					:Tuple					     =	parser.parse_args()
			_UpperCamelCase					:Optional[Any]					     =	{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
			training_function(snake_case__       ,					snake_case__		)


if __name__ == "__main__":
					main()





"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

			_UpperCamelCase					:str					     =	0
			_UpperCamelCase					:Union[str, Any]					     =	0

			for i, name in enumerate(snake_case__		):
						for letter in name:
									name_score += ord(snake_case__		) - 64

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


if __name__ == "__main__":
					print(solution())

