
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
#
########################################################################


UpperCamelCase_  =	16
UpperCamelCase_  =	32





def 							_lowerCamelCase			(					lowerCamelCase_:  List[Any]		):



				'''simple docstring'''







				return int(x / 2**20		)

class     _SCREAMING_SNAKE_CASE							:





				def __enter__(		self :		str							):


								"""simple docstring"""

								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								A     :  Optional[Any]   =				torch.cuda.memory_allocated()
								A     :  Any   =				psutil.Process()

								A     :  int   =				self.cpu_mem_used()
								A     :  int   =				True
								A     :  Union[str, Any]   =				threading.Thread(target=self.peak_monitor_func							)
								A     :  Tuple   =				True
								peak_monitor_thread.start()
								return self





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								return self.process.memory_info().rss





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  Tuple   =				-1

								while True:
												A     :  str   =				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 :		Optional[int]					,      *snake_case_ :		int							):


								"""simple docstring"""

								A     :  Optional[int]   =				False

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

								A     :  List[str]   =				self.cpu_mem_used()
								A     :  Union[str, Any]   =				bamb(self.cpu_end - self.cpu_begin							)
								A     :  Optional[int]   =				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

					UpperCamelCase_  =	mocked_dataloaders  # noqa: F811





def 							_lowerCamelCase			(					lowerCamelCase_:  Optional[int]				,	lowerCamelCase_:  int		):



				'''simple docstring'''







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,	lowerCamelCase_		) == "1":
								A     :  Tuple   =				2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				A     :  Union[str, Any]   =				FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase_				,	ranka_only=lowerCamelCase_		)				,	optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase_				,	ranka_only=lowerCamelCase_		)				,	)

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

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

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

				A     :  Tuple   =				AutoTokenizer.from_pretrained(args.model_name_or_path		)
				A     :  Tuple   =				load_dataset('''glue'''				,	'''mrpc'''		)
				A     :  Optional[int]   =				evaluate.load('''glue'''				,	'''mrpc'''		)

				def tokenize_function(lowerCamelCase_:  Any		):
								# max_length=None => use the model max length (it's actually the default)
								A     :  Union[str, Any]   =				tokenizer(examples['''sentence1''']				,	examples['''sentence2''']				,	truncation=lowerCamelCase_				,	max_length=lowerCamelCase_		)
								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():
								A     :  Optional[Any]   =				datasets.map(
								    lowerCamelCase_				,	batched=lowerCamelCase_				,	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
				A     :  List[Any]   =				tokenized_datasets.rename_column('''label'''				,	'''labels'''		)

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

				def collate_fn(lowerCamelCase_:  Union[str, Any]		):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A     :  str   =				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":
												A     :  Optional[Any]   =				16
								elif accelerator.mixed_precision != "no":
												A     :  List[str]   =				8
								else:
												A     :  Union[str, Any]   =				None

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

				# Instantiate dataloaders.
				A     :  Dict   =				DataLoader(
				    tokenized_datasets['''train''']				,	shuffle=lowerCamelCase_				,	collate_fn=lowerCamelCase_				,	batch_size=lowerCamelCase_		)
				A     :  str   =				DataLoader(
				    tokenized_datasets['''validation''']				,	shuffle=lowerCamelCase_				,	collate_fn=lowerCamelCase_				,	batch_size=lowerCamelCase_		)

				set_seed(lowerCamelCase_		)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A     :  Dict   =				AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path				,	return_dict=lowerCamelCase_				,	low_cpu_mem_usage=lowerCamelCase_		)

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

				# 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
				A     :  str   =				torch.optim.AdamW(params=model.parameters()				,	lr=lowerCamelCase_				,	weight_decay=2e-4		)

				# Instantiate scheduler
				A     :  Tuple   =				get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_				,	num_warmup_steps=10				,	num_training_steps=(len(lowerCamelCase_		) * 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.
				A ,       A ,       A ,       A     :  List[Any]   =				accelerator.prepare(
				    lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_		)

				A     :  Optional[int]   =				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		)
												A     :  int   =				os.path.basename(args.resume_from_checkpoint		)
								else:
												# Get the most recent checkpoint
												A     :  Optional[int]   =				[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
												dirs.sort(key=os.path.getctime		)
												A     :  str   =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								A     :  Dict   =				os.path.splitext(lowerCamelCase_		)[0]

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

    # Now we train the model
				for epoch in range(lowerCamelCase_		):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																A     :  List[Any]   =				0
												for step, batch in enumerate(lowerCamelCase_		):
																# 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		)
																A     :  int   =				model(**lowerCamelCase_		)
																A     :  str   =				outputs.loss
																A     :  Optional[Any]   =				loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(lowerCamelCase_		)
																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(lowerCamelCase_				,	lowerCamelCase_		):
																				A     :  Union[str, Any]   =				f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												A     :  str   =				os.path.join(args.output_dir				,	lowerCamelCase_		)
																								accelerator.save_state(lowerCamelCase_		)
        # 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=lowerCamelCase_				,	)

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

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

												if checkpointing_steps == "epoch":
																A     :  Tuple   =				f"""epoch_{epoch}"""
																if args.output_dir is not None:
																				A     :  Optional[int]   =				os.path.join(args.output_dir				,	lowerCamelCase_		)
																accelerator.save_state(lowerCamelCase_		)
        # 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=lowerCamelCase_				,	)

				if args.with_tracking:
								accelerator.end_training()





def 							_lowerCamelCase			(					):



				'''simple docstring'''







				A     :  Tuple   =				argparse.ArgumentParser(description='''Simple example of training script.'''		)
				parser.add_argument(
				    '''--mixed_precision'''				,	type=lowerCamelCase_				,	default=lowerCamelCase_				,	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=lowerCamelCase_				,	default=lowerCamelCase_				,	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=lowerCamelCase_				,	default=lowerCamelCase_				,	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=lowerCamelCase_				,	default='''.'''				,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,	)
				parser.add_argument(
				    '''--logging_dir'''				,	type=lowerCamelCase_				,	default='''logs'''				,	help='''Location on where to store experiment tracking logs`'''				,	)
				parser.add_argument(
				    '''--model_name_or_path'''				,	type=lowerCamelCase_				,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,	required=lowerCamelCase_				,	)
				A     :  Dict   =				parser.parse_args()
				A     :  Dict   =				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					main()

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()