



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A__						:						Tuple          =      16
A__						:						str          =      32


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]				):
		return int(x / 2**20				)



class        __snake_case		:




							def __enter__( self  : List[str]):
									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									lowerCAmelCase_    :  List[Any]             =				torch.cuda.memory_allocated()
									lowerCAmelCase_    :  Tuple             =				psutil.Process()

									lowerCAmelCase_    :  Optional[int]             =				self.cpu_mem_used()
									lowerCAmelCase_    :  Optional[Any]             =				True
									lowerCAmelCase_    :  int             =				threading.Thread(target=self.peak_monitor_func)
									lowerCAmelCase_    :  int             =				True
									peak_monitor_thread.start()
									return self




							def      UpperCAmelCase__    ( self  : Optional[int]):
									return self.process.memory_info().rss




							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  List[Any]             =				-1

									while True:
											lowerCAmelCase_    :  int             =				max(self.cpu_mem_used()       ,       self.cpu_peak)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											# time.sleep(0.001) # 1msec

											if not self.peak_monitoring:
													break







							def __exit__( self  : Union[str, Any]       ,       *A_  : int):
									lowerCAmelCase_    :  Optional[int]             =				False

									gc.collect()
									torch.cuda.empty_cache()
									lowerCAmelCase_    :  Optional[int]             =				torch.cuda.memory_allocated()
									lowerCAmelCase_    :  List[Any]             =				torch.cuda.max_memory_allocated()
									lowerCAmelCase_    :  Optional[int]             =				bamb(self.end - self.begin)
									lowerCAmelCase_    :  str             =				bamb(self.peak - self.begin)

									lowerCAmelCase_    :  List[str]             =				self.cpu_mem_used()
									lowerCAmelCase_    :  Optional[int]             =				bamb(self.cpu_end - self.cpu_begin)
									lowerCAmelCase_    :  Dict             =				bamb(self.cpu_peak - self.cpu_begin)
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						A__						:						List[str]          =      mocked_dataloaders  # noqa: F811


def     UpperCamelCase(						__UpperCamelCase							:	Optional[int]  ,__UpperCamelCase							:	str				):
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,__UpperCamelCase				) == "1":
				lowerCAmelCase_    :  Dict             =				2

		# New Code #
		# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
		lowerCAmelCase_    :  Optional[int]             =				FullyShardedDataParallelPlugin(
		    state_dict_config=FullStateDictConfig(offload_to_cpu=__UpperCamelCase  ,ranka_only=__UpperCamelCase				)  ,optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__UpperCamelCase  ,ranka_only=__UpperCamelCase				)  ,)

		# Initialize accelerator
		if args.with_tracking:
				lowerCAmelCase_    :  Dict             =				Accelerator(
				    cpu=args.cpu  ,mixed_precision=args.mixed_precision  ,log_with='''wandb'''  ,project_dir=args.logging_dir  ,fsdp_plugin=__UpperCamelCase  ,)
		else:
				lowerCAmelCase_    :  List[str]             =				Accelerator(fsdp_plugin=__UpperCamelCase				)
		accelerator.print(accelerator.distributed_type				)

		if hasattr(args.checkpointing_steps  ,'''isdigit'''				):
				if args.checkpointing_steps == "epoch":
						lowerCAmelCase_    :  Any             =				args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						lowerCAmelCase_    :  List[str]             =				int(args.checkpointing_steps				)
				else:
						raise ValueError(
						    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""				)
		else:
				lowerCAmelCase_    :  Dict             =				None
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase_    :  List[str]             =				config['''lr''']
		lowerCAmelCase_    :  int             =				int(config['''num_epochs''']				)
		lowerCAmelCase_    :  List[str]             =				int(config['''seed''']				)
		lowerCAmelCase_    :  Tuple             =				int(config['''batch_size''']				)

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				lowerCAmelCase_    :  str             =				vars(__UpperCamelCase				)
				accelerator.init_trackers('''fsdp_glue_no_trainer'''  ,__UpperCamelCase				)

		lowerCAmelCase_    :  Any             =				AutoTokenizer.from_pretrained(args.model_name_or_path				)
		lowerCAmelCase_    :  List[str]             =				load_dataset('''glue'''  ,'''mrpc'''				)
		lowerCAmelCase_    :  Union[str, Any]             =				evaluate.load('''glue'''  ,'''mrpc'''				)

		def tokenize_function(__UpperCamelCase							:	Union[str, Any]				):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase_    :  List[str]             =				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
		# starting with the main process first:
		with accelerator.main_process_first():
				lowerCAmelCase_    :  Optional[Any]             =				datasets.map(
				    __UpperCamelCase  ,batched=__UpperCamelCase  ,remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,)

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

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase_    :  Any             =				1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase_    :  int             =				batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase_    :  int             =				MAX_GPU_BATCH_SIZE

		def collate_fn(__UpperCamelCase							:	str				):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase_    :  Dict             =				128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCAmelCase_    :  Optional[Any]             =				16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase_    :  str             =				8
				else:
						lowerCAmelCase_    :  int             =				None

				return tokenizer.pad(
				    __UpperCamelCase  ,padding='''longest'''  ,max_length=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_tensors='''pt'''  ,)

		# Instantiate dataloaders.
		lowerCAmelCase_    :  Dict             =				DataLoader(
		    tokenized_datasets['''train''']  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase  ,batch_size=__UpperCamelCase				)
		lowerCAmelCase_    :  Union[str, Any]             =				DataLoader(
		    tokenized_datasets['''validation''']  ,shuffle=__UpperCamelCase  ,collate_fn=__UpperCamelCase  ,batch_size=__UpperCamelCase				)

		set_seed(__UpperCamelCase				)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase_    :  List[str]             =				AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path  ,return_dict=__UpperCamelCase  ,low_cpu_mem_usage=__UpperCamelCase				)

		# New Code #
		# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
		lowerCAmelCase_    :  Optional[Any]             =				accelerator.prepare(__UpperCamelCase				)
		accelerator.print(__UpperCamelCase				)

		# Instantiate optimizer
		# New Code #
		# For FSDP feature, at present it doesn't support multiple parameter groups,
		# so we need to create a single parameter group for the whole model
		lowerCAmelCase_    :  Any             =				torch.optim.AdamW(params=model.parameters()  ,lr=__UpperCamelCase  ,weight_decay=2e-4				)

		# Instantiate scheduler
		lowerCAmelCase_    :  List[str]             =				get_linear_schedule_with_warmup(
		    optimizer=__UpperCamelCase  ,num_warmup_steps=10  ,num_training_steps=(len(__UpperCamelCase				) * num_epochs) // gradient_accumulation_steps  ,)

		# New Code #
		# For FSDP feature, prepare everything except the model as we have already prepared the model
		# before creating the optimizer
		# 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.
		lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				accelerator.prepare(
		    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)

		lowerCAmelCase_    :  List[Any]             =				0

		# Potentially load in the weights and states from a previous save
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""				)
						accelerator.load_state(args.resume_from_checkpoint				)
						lowerCAmelCase_    :  str             =				os.path.basename(args.resume_from_checkpoint				)
				else:
						# Get the most recent checkpoint
						lowerCAmelCase_    :  Union[str, Any]             =				[f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
						dirs.sort(key=os.path.getctime				)
						lowerCAmelCase_    :  Dict             =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				lowerCAmelCase_    :  Union[str, Any]             =				os.path.splitext(__UpperCamelCase				)[0]

				if "epoch" in training_difference:
						num_epochs -= int(training_difference.replace('''epoch_'''  ,''''''				)				)
						lowerCAmelCase_    :  Any             =				None
				else:
						lowerCAmelCase_    :  Optional[int]             =				int(training_difference.replace('''step_'''  ,''''''				)				)
						num_epochs -= resume_step // len(__UpperCamelCase				)
						# If resuming by step, we also need to know exactly how far into the DataLoader we went
						lowerCAmelCase_    :  List[str]             =				(num_epochs * len(__UpperCamelCase				)) - resume_step

    # Now we train the model
		for epoch in range(__UpperCamelCase				):
				# New Code #
				# context manager to track the peak memory usage during the training epoch
				with TorchTracemalloc() as tracemalloc:
						model.train()
						if args.with_tracking:
								lowerCAmelCase_    :  Any             =				0
						for step, batch in enumerate(__UpperCamelCase				):
								# We need to skip steps until we reach the resumed step
								if args.resume_from_checkpoint and epoch == 0:
										if resume_step is not None and step < resume_step:
												pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device				)
								lowerCAmelCase_    :  Optional[Any]             =				model(**__UpperCamelCase				)
								lowerCAmelCase_    :  Dict             =				outputs.loss
								lowerCAmelCase_    :  str             =				loss / gradient_accumulation_steps
								# We keep track of the loss at each epoch
								if args.with_tracking:
										total_loss += loss.detach().float()
								accelerator.backward(__UpperCamelCase				)
								if step % gradient_accumulation_steps == 0:
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# accelerator.print(lr_scheduler.get_lr())

								overall_step += 1

								if isinstance(__UpperCamelCase  ,__UpperCamelCase				):
										lowerCAmelCase_    :  str             =				f"""step_{overall_step}"""
										if overall_step % checkpointing_steps == 0:
												if args.output_dir is not None:
														lowerCAmelCase_    :  Optional[int]             =				os.path.join(args.output_dir  ,__UpperCamelCase				)
												accelerator.save_state(__UpperCamelCase				)
        # New Code #
        # 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				)				)				)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin				),
						    }  ,step=__UpperCamelCase  ,)

				# New Code #
				# context manager to track the peak memory usage during the evaluation
				with TorchTracemalloc() as tracemalloc:
						model.eval()
						for step, batch in enumerate(__UpperCamelCase				):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device				)
								with torch.no_grad():
										lowerCAmelCase_    :  Optional[int]             =				model(**__UpperCamelCase				)
								lowerCAmelCase_    :  Dict             =				outputs.logits.argmax(dim=-1				)
								lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
								metric.add_batch(
								    predictions=__UpperCamelCase  ,references=__UpperCamelCase  ,)

						lowerCAmelCase_    :  List[str]             =				metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""  ,__UpperCamelCase				)
						if args.with_tracking:
								accelerator.log(
								    {
								        '''accuracy''': eval_metric['''accuracy'''],
								        '''f1''': eval_metric['''f1'''],
								        '''train_loss''': total_loss.item() / len(__UpperCamelCase				),
								    }  ,step=__UpperCamelCase  ,)

						if checkpointing_steps == "epoch":
								lowerCAmelCase_    :  Union[str, Any]             =				f"""epoch_{epoch}"""
								if args.output_dir is not None:
										lowerCAmelCase_    :  Union[str, Any]             =				os.path.join(args.output_dir  ,__UpperCamelCase				)
								accelerator.save_state(__UpperCamelCase				)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
				accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin				)				)				)
				accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used				)				)
				accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked				)				)
				accelerator.print(
				    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
				# Logging the peak memory usage of the GPU to the tracker
				if args.with_tracking:
						accelerator.log(
						    {
						        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin				),
						    }  ,step=__UpperCamelCase  ,)

		if args.with_tracking:
				accelerator.end_training()


def     UpperCamelCase(						):
		lowerCAmelCase_    :  Tuple             =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
		parser.add_argument(
		    '''--mixed_precision'''  ,type=__UpperCamelCase  ,default=__UpperCamelCase  ,choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.'''  ,)
		parser.add_argument('''--cpu'''  ,action='''store_true'''  ,help='''If passed, will train on the CPU.'''				)
		parser.add_argument(
		    '''--checkpointing_steps'''  ,type=__UpperCamelCase  ,default=__UpperCamelCase  ,help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,)
		parser.add_argument(
		    '''--resume_from_checkpoint'''  ,type=__UpperCamelCase  ,default=__UpperCamelCase  ,help='''If the training should continue from a checkpoint folder.'''  ,)
		parser.add_argument(
		    '''--with_tracking'''  ,action='''store_true'''  ,help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,)
		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(
		    '''--logging_dir'''  ,type=__UpperCamelCase  ,default='''logs'''  ,help='''Location on where to store experiment tracking logs`'''  ,)
		parser.add_argument(
		    '''--model_name_or_path'''  ,type=__UpperCamelCase  ,help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,required=__UpperCamelCase  ,)
		lowerCAmelCase_    :  str             =				parser.parse_args()
		lowerCAmelCase_    :  List[str]             =				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__UpperCamelCase  ,__UpperCamelCase				)


if __name__ == "__main__":
						main()










from __future__ import annotations

A__						:						Tuple          =      '''Alexander Joslin'''


def     UpperCamelCase(						__UpperCamelCase							:	int				):

		if number <= 0:
				lowerCAmelCase_    :  str             =				f"""n must be greater than 0. Got n = {number}"""
				raise ValueError(__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				[number + 1] * (number + 1)

		# starting position
		lowerCAmelCase_    :  List[Any]             =				0
		for i in range(1  ,__UpperCamelCase				):
				lowerCAmelCase_    :  int             =				min(table[i + 1]  ,table[i] + 1				)
				# check if out of bounds
				if i * 2 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 2]  ,table[i] + 1				)
				# check if out of bounds
				if i * 3 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 3]  ,table[i] + 1				)
		return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()





