





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     (a__   :str       ):






							"""simple docstring"""





							return int(x / 2**20       )

class        __SCREAMING_SNAKE_CASE					:






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







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






def     _UpperCamelCase     (a__   :Accelerator   ,			a__   :int = 16   ,			a__   :str = "bert-base-cased"   ,			a__   :int = 320   ,			a__   :int = 160   ,			):






							"""simple docstring"""





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

							def tokenize_function(a__   :int       ):
														# max_length=None => use the model max length (it's actually the default)
														UpperCamelCase__				     =			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
							UpperCamelCase__				     =			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
							UpperCamelCase__				     =			tokenized_datasets.rename_column("""label"""   ,			"""labels"""       )

							def collate_fn(a__   :Dict       ):
														# 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.
							UpperCamelCase__				     =			DataLoader(
							    tokenized_datasets["""train"""]   ,			shuffle=a__   ,			collate_fn=a__   ,			batch_size=a__       )
							UpperCamelCase__				     =			DataLoader(
							    tokenized_datasets["""validation"""]   ,			shuffle=a__   ,			collate_fn=a__   ,			batch_size=a__       )

							return train_dataloader, eval_dataloader





def     _UpperCamelCase     (a__   :str   ,			a__   :Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			Accelerator()

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

							set_seed(a__       )
							UpperCamelCase__     ,     UpperCamelCase__				     =			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)
							UpperCamelCase__				     =			AutoModelForSequenceClassification.from_pretrained(a__   ,			return_dict=a__       )

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

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

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

							# Now we train the model
							UpperCamelCase__				     =			{}
							for epoch in range(a__   ,			a__       ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(a__       ):
																												UpperCamelCase__				     =			model(**a__       )
																												UpperCamelCase__				     =			outputs.loss
																												UpperCamelCase__				     =			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       )       )       )
														UpperCamelCase__				     =			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     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			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."""   ,			)
							UpperCamelCase__				     =			parser.parse_args()
							UpperCamelCase__				     =			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(a__   ,			a__       )


if __name__ == "__main__":
					main()











from typing import Any





def     _UpperCamelCase     (a__   :list       ):






							"""simple docstring"""





							if not input_list:
														return []
							UpperCamelCase__				     =			[input_list.count(a__       ) for value in input_list]
							UpperCamelCase__				     =			max(a__       )  # Gets the maximum count in the input list.
							# Gets values of modes
							return sorted({input_list[i] for i, value in enumerate(a__       ) if value == y}       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




