



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__ =						16
UpperCamelCase__ =						32






def  UpperCAmelCase__    (    lowercase__   ):
						return int(x / 2**20   )
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __enter__(				self	:       Any   )						->					Dict:
									"""simple docstring"""







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







			def __exit__(				self	:       Optional[int]			,     *lowercase	:       str   )						->					str:
									"""simple docstring"""







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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__ = 16						,       lowercase__ = "bert-base-cased"						,       lowercase__ = 320						,       lowercase__ = 160						,       ):
						__lowercase					=				AutoTokenizer.from_pretrained(lowercase__   )
						__lowercase					=				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)
												__lowercase					=				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
						__lowercase					=				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
						__lowercase					=				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.
						__lowercase					=				DataLoader(
						    tokenized_datasets["""train"""]						,       shuffle=lowercase__						,       collate_fn=lowercase__						,       batch_size=lowercase__   )
						__lowercase					=				DataLoader(
						    tokenized_datasets["""validation"""]						,       shuffle=lowercase__						,       collate_fn=lowercase__						,       batch_size=lowercase__   )

						return train_dataloader, eval_dataloader






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ):
						# Initialize accelerator
						__lowercase					=				Accelerator()

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

						set_seed(lowercase__   )
						__lowercase	,			__lowercase					=				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)
						__lowercase					=				AutoModelForSequenceClassification.from_pretrained(lowercase__						,       return_dict=lowercase__   )

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

						if accelerator.state.deepspeed_plugin is not None:
												__lowercase					=				accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												__lowercase					=				1
						__lowercase					=				(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
						):
												__lowercase					=				get_linear_schedule_with_warmup(
												    optimizer=lowercase__						,       num_warmup_steps=0						,       num_training_steps=lowercase__						,       )
						else:
												__lowercase					=				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.
						__lowercase	,			__lowercase	,			__lowercase	,			__lowercase	,			__lowercase					=				accelerator.prepare(
						    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   )

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

						# Now we train the model
						__lowercase					=				{}
						for epoch in range(lowercase__						,       lowercase__   ):
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		for step, batch in enumerate(lowercase__   ):
																								__lowercase					=				model(**lowercase__   )
																								__lowercase					=				outputs.loss
																								__lowercase					=				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   )   )   )
												__lowercase					=				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  UpperCAmelCase__    (    ):
						__lowercase					=				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."""						,       )
						__lowercase					=				parser.parse_args()
						__lowercase					=				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(lowercase__						,       lowercase__   )


if __name__ == "__main__":
			main()










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





