




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


lowercase_      :							str    							=					16
lowercase_      :							Union[str, Any]    							=					32


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						return int(x / 2**20		)






class        __lowerCAmelCase					:







			def __enter__(			self       :     Dict ):




									"""simple docstring"""


									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									_UpperCAmelCase						=	torch.cuda.memory_allocated()
									_UpperCAmelCase						=	psutil.Process()

									_UpperCAmelCase						=	self.cpu_mem_used()
									_UpperCAmelCase						=	True
									_UpperCAmelCase						=	threading.Thread(target=self.peak_monitor_func )
									_UpperCAmelCase						=	True
									peak_monitor_thread.start()
									return self







			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									return self.process.memory_info().rss







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	-1

									while True:
															_UpperCAmelCase						=	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       :     str       ,       *snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	False

									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 )

									_UpperCAmelCase						=	self.cpu_mem_used()
									_UpperCAmelCase						=	bamb(self.cpu_end - self.cpu_begin )
									_UpperCAmelCase						=	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

				lowercase_      :							List[Any]    							=					mocked_dataloaders  # noqa: F811


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,       snake_case_		) == "1":
												_UpperCAmelCase						=	2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						_UpperCAmelCase						=	FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=snake_case_      ,       ranka_only=snake_case_		)      ,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=snake_case_      ,       ranka_only=snake_case_		)      ,       )

						# Initialize accelerator
						if args.with_tracking:
												_UpperCAmelCase						=	Accelerator(
												    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="wandb"      ,       project_dir=args.logging_dir      ,       fsdp_plugin=snake_case_      ,       )
						else:
												_UpperCAmelCase						=	Accelerator(fsdp_plugin=snake_case_		)
						accelerator.print(accelerator.distributed_type		)

						if hasattr(args.checkpointing_steps      ,       "isdigit"		):
												if args.checkpointing_steps == "epoch":
																		_UpperCAmelCase						=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_UpperCAmelCase						=	int(args.checkpointing_steps		)
												else:
																		raise ValueError(
																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
						else:
												_UpperCAmelCase						=	None
						# 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"]		)

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												_UpperCAmelCase						=	vars(snake_case_		)
												accelerator.init_trackers("fsdp_glue_no_trainer"      ,       snake_case_		)

						_UpperCAmelCase						=	AutoTokenizer.from_pretrained(args.model_name_or_path		)
						_UpperCAmelCase						=	load_dataset("glue"      ,       "mrpc"		)
						_UpperCAmelCase						=	evaluate.load("glue"      ,       "mrpc"		)

						def tokenize_function(snake_case_		):
												# max_length=None => use the model max length (it's actually the default)
												_UpperCAmelCase						=	tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=snake_case_      ,       max_length=snake_case_		)
												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():
												_UpperCAmelCase						=	datasets.map(
												    snake_case_      ,       batched=snake_case_      ,       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
						_UpperCAmelCase						=	tokenized_datasets.rename_column("label"      ,       "labels"		)

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

						def collate_fn(snake_case_		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_UpperCAmelCase						=	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":
																		_UpperCAmelCase						=	16
												elif accelerator.mixed_precision != "no":
																		_UpperCAmelCase						=	8
												else:
																		_UpperCAmelCase						=	None

												return tokenizer.pad(
												    snake_case_      ,       padding="longest"      ,       max_length=snake_case_      ,       pad_to_multiple_of=snake_case_      ,       return_tensors="pt"      ,       )

						# Instantiate dataloaders.
						_UpperCAmelCase						=	DataLoader(
						    tokenized_datasets["train"]      ,       shuffle=snake_case_      ,       collate_fn=snake_case_      ,       batch_size=snake_case_		)
						_UpperCAmelCase						=	DataLoader(
						    tokenized_datasets["validation"]      ,       shuffle=snake_case_      ,       collate_fn=snake_case_      ,       batch_size=snake_case_		)

						set_seed(snake_case_		)

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

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						_UpperCAmelCase						=	accelerator.prepare(snake_case_		)
						accelerator.print(snake_case_		)

						# 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
						_UpperCAmelCase						=	torch.optim.AdamW(params=model.parameters()      ,       lr=snake_case_      ,       weight_decay=2e-4		)

						# Instantiate scheduler
						_UpperCAmelCase						=	get_linear_schedule_with_warmup(
						    optimizer=snake_case_      ,       num_warmup_steps=10      ,       num_training_steps=(len(snake_case_		) * 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.
						_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	accelerator.prepare(
						    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)

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

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace("epoch_"      ,       ""		)		)
																		_UpperCAmelCase						=	None
												else:
																		_UpperCAmelCase						=	int(training_difference.replace("step_"      ,       ""		)		)
																		num_epochs -= resume_step // len(snake_case_		)
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		_UpperCAmelCase						=	(num_epochs * len(snake_case_		)) - resume_step

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

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

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

																		if checkpointing_steps == "epoch":
																								_UpperCAmelCase						=	f"""epoch_{epoch}"""
																								if args.output_dir is not None:
																														_UpperCAmelCase						=	os.path.join(args.output_dir      ,       snake_case_		)
																								accelerator.save_state(snake_case_		)
        # 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=snake_case_      ,       )

						if args.with_tracking:
												accelerator.end_training()


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


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


if __name__ == "__main__":
				main()










import math
from itertools import permutations


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5      ,       int(math.sqrt(snake_case_		) + 1		)      ,       6		):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase				,					_UpperCAmelCase						=	0, len(snake_case_		) - 1
						while left <= right:
												_UpperCAmelCase						=	(left + right) // 2
												if prime_list[middle] == target:
																		return True
												elif prime_list[middle] < target:
																		_UpperCAmelCase						=	middle + 1
												else:
																		_UpperCAmelCase						=	middle - 1

						return False


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	[n for n in range(1001      ,       1_0000      ,       2		) if is_prime(snake_case_		)]
						_UpperCAmelCase						=	[]

						for number in prime_list:
												_UpperCAmelCase						=	[]

												for prime_member in permutations(list(str(snake_case_		)		)		):
																		_UpperCAmelCase						=	int("".join(snake_case_		)		)

																		if prime % 2 == 0:
																								continue

																		if search(snake_case_      ,       snake_case_		):
																								tmp_numbers.append(snake_case_		)

												tmp_numbers.sort()
												if len(snake_case_		) >= 3:
																		candidates.append(snake_case_		)

						_UpperCAmelCase						=	[]
						for candidate in candidates:
												_UpperCAmelCase						=	len(snake_case_		)
												_UpperCAmelCase						=	False

												for i in range(snake_case_		):
																		for j in range(i + 1      ,       snake_case_		):
																								for k in range(j + 1      ,       snake_case_		):
																														if (
																														    abs(candidate[i] - candidate[j]		)
																														    == abs(candidate[j] - candidate[k]		)
																														    and len({candidate[i], candidate[j], candidate[k]}		) == 3
																														):
																																				passed.append(
																																				    sorted([candidate[i], candidate[j], candidate[k]]		)		)
																																				_UpperCAmelCase						=	True

																														if found:
																																				break
																								if found:
																														break
																		if found:
																								break

						_UpperCAmelCase						=	set()
						for seq in passed:
												answer.add("".join([str(snake_case_		) for i in seq]		)		)

						return max(int(snake_case_		) for x in answer		)


if __name__ == "__main__":
				print(solution())




