



'''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


a							   =				16
a							   =				32







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			return int(x / 2**20					)






class 						a_		:


							def __enter__(				self   :						Optional[Any]			)			->					List[Any]:
										gc.collect()
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
										snake_case:    Any      							=torch.cuda.memory_allocated()
										return self




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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase = 16				,							__UpperCAmelCase = "bert-base-cased"				,							__UpperCAmelCase = 3_20				,							__UpperCAmelCase = 1_60				,							)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Optional[int]      							=AutoTokenizer.from_pretrained(__UpperCAmelCase					)
			snake_case:    List[Any]      							=load_dataset(
			    'glue'				,							'mrpc'				,							split={'train': f'''train[:{n_train}]''', 'validation': f'''validation[:{n_val}]'''}					)

			def tokenize_function(__UpperCAmelCase					):
						# max_length=None => use the model max length (it's actually the default)
						snake_case:    Union[str, Any]      							=tokenizer(examples['sentence1']				,							examples['sentence2']				,							truncation=__UpperCAmelCase				,							max_length=__UpperCAmelCase					)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			snake_case:    List[Any]      							=datasets.map(
			    __UpperCAmelCase				,							batched=__UpperCAmelCase				,							remove_columns=['idx', 'sentence1', 'sentence2']				,							load_from_cache_file=__UpperCAmelCase					)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			snake_case:    str      							=tokenized_datasets.rename_column('label'				,							'labels'					)

			def collate_fn(__UpperCAmelCase					):
						# 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(__UpperCAmelCase				,							padding='max_length'				,							max_length=1_28				,							return_tensors='pt'					)
						return tokenizer.pad(__UpperCAmelCase				,							padding='longest'				,							return_tensors='pt'					)

			# Instantiate dataloaders.
			snake_case:    List[Any]      							=DataLoader(
			    tokenized_datasets['train']				,							shuffle=__UpperCAmelCase				,							collate_fn=__UpperCAmelCase				,							batch_size=__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=DataLoader(
			    tokenized_datasets['validation']				,							shuffle=__UpperCAmelCase				,							collate_fn=__UpperCAmelCase				,							batch_size=__UpperCAmelCase					)

			return train_dataloader, eval_dataloader







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[Any]      							=Accelerator()

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

			set_seed(__UpperCAmelCase					)
			snake_case							,  snake_case:    List[Any]      							=get_dataloaders(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							args.n_train				,							args.n_val					)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			snake_case:    Any      							=AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase				,							return_dict=__UpperCAmelCase					)

			# Instantiate optimizer
			snake_case:    List[str]      							=(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			snake_case:    Tuple      							=optimizer_cls(params=model.parameters()				,							lr=__UpperCAmelCase					)

			if accelerator.state.deepspeed_plugin is not None:
						snake_case:    List[Any]      							=accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						snake_case:    List[Any]      							=1
			snake_case:    Optional[Any]      							=(len(__UpperCAmelCase					) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						snake_case:    Union[str, Any]      							=get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,							num_warmup_steps=0				,							num_training_steps=__UpperCAmelCase				,							)
			else:
						snake_case:    Dict      							=DummyScheduler(__UpperCAmelCase				,							total_num_steps=__UpperCAmelCase				,							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.
			snake_case							,  snake_case							,  snake_case							,  snake_case							,  snake_case:    str      							=accelerator.prepare(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

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

			# Now we train the model
			snake_case:    Optional[Any]      							={}
			for epoch in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(__UpperCAmelCase					):
												snake_case:    str      							=model(**__UpperCAmelCase					)
												snake_case:    Tuple      							=outputs.loss
												snake_case:    Any      							=loss / gradient_accumulation_steps
												accelerator.backward(__UpperCAmelCase					)
												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					)					)					)
						snake_case:    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(__UpperCAmelCase				,							__UpperCAmelCase					)







def a_     (    )				->					Any:





			"""simple docstring"""







			snake_case:    Dict      							=argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
			parser.add_argument(
			    '--model_name_or_path'				,							type=__UpperCAmelCase				,							default='bert-base-cased'				,							help='Path to pretrained model or model identifier from huggingface.co/models.'				,							required=__UpperCAmelCase				,							)
			parser.add_argument(
			    '--output_dir'				,							type=__UpperCAmelCase				,							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=__UpperCAmelCase				,							default=__UpperCAmelCase				,							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=__UpperCAmelCase				,							default=3_20				,							help='Number of training examples to use.'				,							)
			parser.add_argument(
			    '--n_val'				,							type=__UpperCAmelCase				,							default=1_60				,							help='Number of validation examples to use.'				,							)
			parser.add_argument(
			    '--num_epochs'				,							type=__UpperCAmelCase				,							default=1				,							help='Number of train epochs.'				,							)
			snake_case:    Union[str, Any]      							=parser.parse_args()
			snake_case:    Optional[Any]      							={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
			training_function(__UpperCAmelCase				,							__UpperCAmelCase					)


if __name__ == "__main__":
	main()









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




