




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


__lowerCAmelCase						:							List[Any]			     =						16
__lowerCAmelCase						:							str			     =						32




def      __magic_name__							(					A						:   Dict  ):






			'''simple docstring'''







			return int(x / 2**20  )




class        snake_case__				:
				"""simple docstring"""



				def __enter__( self							:	Union[str, Any]				)   ->	Union[str, Any]:
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							a       =     torch.cuda.memory_allocated()
							a       =     psutil.Process()

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





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							return self.process.memory_info().rss





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							a       =     -1

							while True:
										a       =     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]   , *__lowerCamelCase							:	str				)   ->	Dict:
							a       =     False

							gc.collect()
							torch.cuda.empty_cache()
							a       =     torch.cuda.memory_allocated()
							a       =     torch.cuda.max_memory_allocated()
							a       =     bamb(self.end - self.begin				)
							a       =     bamb(self.peak - self.begin				)

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

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




def      __magic_name__							(					A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			if os.environ.get("TESTING_MOCKED_DATALOADERS",    A  ) == "1":
						a       =     2

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

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

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

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

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

			def tokenize_function(A						:   List[Any]  ):
						# max_length=None => use the model max length (it's actually the default)
						a       =     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
			# starting with the main process first:
			with accelerator.main_process_first():
						a       =     datasets.map(
						    A,    batched=A,    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       =     tokenized_datasets.rename_column("label",    "labels"  )

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

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

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

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

			set_seed(A  )

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

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

			# 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       =     torch.optim.AdamW(params=model.parameters(),    lr=A,    weight_decay=2E-4  )

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

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

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

						# Now we train the model
			for epoch in range(A  ):
						# 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       =     0
									for step, batch in enumerate(A  ):
												# 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       =     model(**A  )
												a       =     outputs.loss
												a       =     loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(A  )
												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(A,    A  ):
															a       =     F"""step_{overall_step}"""
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					a       =     os.path.join(args.output_dir,    A  )
																		accelerator.save_state(A  )
												# 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=A,    )

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

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

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

			if args.with_tracking:
						accelerator.end_training()




def      __magic_name__							(					):






			'''simple docstring'''







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


if __name__ == "__main__":
		main()












from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

  for i in range(1       ,  r + 1				):
    for j in range(1       ,  number + 1				):
      if j < values[i - 1]:
        _lowercase:			Optional[int]      =     subset[i - 1][j]
      else:
        _lowercase:			Optional[Any]      =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(f'''{number} is {'' if weird(number) else 'not '}weird.''')
