


'''simple docstring'''


from __future__ import annotations






def  __lowerCAmelCase (__lowerCAmelCase   )     ->   List[str]:
		if not nums:
				return 0
		_UpperCAmelCase		:       Optional[Any]								=      nums[0]
		_UpperCAmelCase		:       int								=      0
		for num in nums[1:]:
				_UpperCAmelCase		:       int								=      (
				    max_excluding + num,
				    max(__lowerCAmelCase      , __lowerCAmelCase   ),
				)
		return max(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()







'''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


lowerCamelCase__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return int(x / 2**20   )






class 	lowerCAmelCase__			:




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



					'''simple docstring'''







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






			def __exit__(		self  :     Tuple	,  *lowerCamelCase__  :     str )   ->int:



					'''simple docstring'''







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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"      , __lowerCAmelCase = 320      , __lowerCAmelCase = 160      , ):
		_UpperCAmelCase		:       int								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       Any								=      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)
				_UpperCAmelCase		:       List[Any]								=      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
		_UpperCAmelCase		:       int								=      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
		_UpperCAmelCase		:       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.
				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.
		_UpperCAmelCase		:       Any								=      DataLoader(
		    tokenized_datasets["train"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )
		_UpperCAmelCase		:       List[str]								=      DataLoader(
		    tokenized_datasets["validation"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )

		return train_dataloader, eval_dataloader






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		# Initialize accelerator
		_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

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

		set_seed(__lowerCAmelCase   )
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      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)
		_UpperCAmelCase		:       Optional[int]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

		# Instantiate optimizer
		_UpperCAmelCase		:       Dict								=      (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_UpperCAmelCase		:       str								=      optimizer_cls(params=model.parameters()      , lr=__lowerCAmelCase   )

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCAmelCase		:       Any								=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				_UpperCAmelCase		:       Any								=      1
		_UpperCAmelCase		:       Optional[int]								=      (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
		):
				_UpperCAmelCase		:       Tuple								=      get_linear_schedule_with_warmup(
				    optimizer=__lowerCAmelCase      , num_warmup_steps=0      , num_training_steps=__lowerCAmelCase      , )
		else:
				_UpperCAmelCase		:       Optional[Any]								=      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.
		_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      accelerator.prepare(
		    __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

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

		# Now we train the model
		_UpperCAmelCase		:       Optional[Any]								=      {}
		for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(__lowerCAmelCase   ):
								_UpperCAmelCase		:       Union[str, Any]								=      model(**__lowerCAmelCase   )
								_UpperCAmelCase		:       Union[str, Any]								=      outputs.loss
								_UpperCAmelCase		:       List[Any]								=      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   )   )   )
				_UpperCAmelCase		:       Optional[int]								=      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  __lowerCAmelCase ():
		_UpperCAmelCase		:       Any								=      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."      , )
		_UpperCAmelCase		:       Tuple								=      parser.parse_args()
		_UpperCAmelCase		:       Optional[Any]								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
		training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						main()



