
"""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_       :    Tuple						      =			16
UpperCamelCase_       :    Optional[Any]						      =			32
def 				A_  (__a					):

					'''simple docstring'''





					return int(x / 2**20					)



class __lowerCAmelCase					:




			"""simple docstring"""







			def __enter__(					self    :       List[str]						)   ->			Tuple:







								"""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    :       int							, *_snake_case    :       int						)   ->			int:







								"""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_  (__a					, __a = 16					, __a = "bert-base-cased"					, __a = 320					, __a = 160					, ):

					'''simple docstring'''





					A_								=    AutoTokenizer.from_pretrained(__a					)
					A_								=    load_dataset(
					    "glue"					, "mrpc"					, split={"train": f'train[:{n_train}]', "validation": f'validation[:{n_val}]'}					)

					def tokenize_function(__a					):
										# max_length=None => use the model max length (it's actually the default)
										A_								=    tokenizer(examples["sentence1"]					, examples["sentence2"]					, truncation=__a					, max_length=__a					)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					A_								=    datasets.map(
					    __a					, batched=__a					, remove_columns=["idx", "sentence1", "sentence2"]					, load_from_cache_file=__a					)

					# 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(__a					):
										# 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(__a					, padding="max_length"					, max_length=128					, return_tensors="pt"					)
										return tokenizer.pad(__a					, padding="longest"					, return_tensors="pt"					)

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

					return train_dataloader, eval_dataloader
def 				A_  (__a					, __a					):

					'''simple docstring'''





					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(__a					)
					A_			,     A_								=    get_dataloaders(__a					, __a					, __a					, 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(__a					, return_dict=__a					)

					# 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=__a					)

					if accelerator.state.deepspeed_plugin is not None:
										A_								=    accelerator.state.deepspeed_plugin.deepspeed_config[
										    "gradient_accumulation_steps"
										]
					else:
										A_								=    1
					A_								=    (len(__a					) * 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=__a					, num_warmup_steps=0					, num_training_steps=__a					, )
					else:
										A_								=    DummyScheduler(__a					, total_num_steps=__a					, 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(
					    __a					, __a					, __a					, __a					, __a					)

					# 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(__a					, __a					):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(__a					):
																				A_								=    model(**__a					)
																				A_								=    outputs.loss
																				A_								=    loss / gradient_accumulation_steps
																				accelerator.backward(__a					)
																				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(__a					, __a					)
def 				A_  ():

					'''simple docstring'''





					A_								=    argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."					)
					parser.add_argument(
					    "--model_name_or_path"					, type=__a					, default="bert-base-cased"					, help="Path to pretrained model or model identifier from huggingface.co/models."					, required=__a					, )
					parser.add_argument(
					    "--output_dir"					, type=__a					, 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=__a					, default=__a					, 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=__a					, default=320					, help="Number of training examples to use."					, )
					parser.add_argument(
					    "--n_val"					, type=__a					, default=160					, help="Number of validation examples to use."					, )
					parser.add_argument(
					    "--num_epochs"					, type=__a					, 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(__a					, __a					)


if __name__ == "__main__":
		main()






"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_       :    Any						      =			{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    str						      =			[
		    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VanForImageClassification''',
		    '''VanModel''',
		    '''VanPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_       :    Optional[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




