




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





def 		__A		(	__lowerCamelCase    )      ->       str:
	return int(x / 2**20    )

class 		__lowerCAmelCase	:
							def __enter__(						self						:str				):







								'''simple docstring'''







								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								a										=							torch.cuda.memory_allocated()
								return self





							def __exit__(						self						:Optional[int]		,      *__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								gc.collect()
								torch.cuda.empty_cache()
								a										=							torch.cuda.memory_allocated()
								a										=							torch.cuda.max_memory_allocated()
								a										=							bamb(self.end - self.begin				)
								a										=							bamb(self.peak - self.begin				)
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16							,			__lowerCamelCase = "bert-base-cased"							,			__lowerCamelCase = 320							,			__lowerCamelCase = 160							,			)      ->       Dict:
	a										=							AutoTokenizer.from_pretrained(__lowerCamelCase    )
	a										=							load_dataset(
	    """glue"""							,			"""mrpc"""							,			split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'}    )

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

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

	# Instantiate dataloaders.
	a										=							DataLoader(
	    tokenized_datasets["""train"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )
	a										=							DataLoader(
	    tokenized_datasets["""validation"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )

	return train_dataloader, eval_dataloader





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# Initialize accelerator
	a										=							Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	a										=							config["""lr"""]
	a										=							int(config["""num_epochs"""]    )
	a										=							int(config["""seed"""]    )
	a										=							int(config["""batch_size"""]    )
	a										=							args.model_name_or_path

	set_seed(__lowerCamelCase    )
	a				, a										=							get_dataloaders(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			args.n_train							,			args.n_val    )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	a										=							AutoModelForSequenceClassification.from_pretrained(__lowerCamelCase							,			return_dict=__lowerCamelCase    )

	# Instantiate optimizer
	a										=							(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	a										=							optimizer_cls(params=model.parameters()							,			lr=__lowerCamelCase    )

	if accelerator.state.deepspeed_plugin is not None:
		a										=							accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		a										=							1
	a										=							(len(__lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		a										=							get_linear_schedule_with_warmup(
		    optimizer=__lowerCamelCase							,			num_warmup_steps=0							,			num_training_steps=__lowerCamelCase							,			)
	else:
		a										=							DummyScheduler(__lowerCamelCase							,			total_num_steps=__lowerCamelCase							,			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.
	a				, a				, a				, a				, a										=							accelerator.prepare(
	    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )

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

	# Now we train the model
	a										=							{}
	for epoch in range(__lowerCamelCase							,			__lowerCamelCase    ):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							outputs.loss
				a										=							loss / gradient_accumulation_steps
				accelerator.backward(__lowerCamelCase    )
				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    )    )    )
		a										=							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(__lowerCamelCase							,			__lowerCamelCase    )





def 		__A		(	)      ->       Union[str, Any]:
	a										=							argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
	parser.add_argument(
	    """--model_name_or_path"""							,			type=__lowerCamelCase							,			default="""bert-base-cased"""							,			help="""Path to pretrained model or model identifier from huggingface.co/models."""							,			required=__lowerCamelCase							,			)
	parser.add_argument(
	    """--output_dir"""							,			type=__lowerCamelCase							,			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=__lowerCamelCase							,			default=__lowerCamelCase							,			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=__lowerCamelCase							,			default=320							,			help="""Number of training examples to use."""							,			)
	parser.add_argument(
	    """--n_val"""							,			type=__lowerCamelCase							,			default=160							,			help="""Number of validation examples to use."""							,			)
	parser.add_argument(
	    """--num_epochs"""							,			type=__lowerCamelCase							,			default=1							,			help="""Number of train epochs."""							,			)
	a										=							parser.parse_args()
	a										=							{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()







from __future__ import annotations

from collections.abc import Iterator

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							value
								a										=							None
								a										=							None







class 		__lowerCAmelCase	:
							def __init__(						self						:str		,      __magic_name__						:Node				):







								'''simple docstring'''







								a										=							tree
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Node | None				):







								'''simple docstring'''







								if node is None:
									return 0
								return node.value + (
								    self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
								)





							def __iter__(						self						:Tuple				):







								'''simple docstring'''







								yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

