code: 
  
  
  """simple docstring"""
  
  
  
  
  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
  #
  ########################################################################
  
  
  __magic_name__	:   List[Any]          =							1_6
  __magic_name__	:   Union[str, Any]          =							3_2
  
  
  
  
  
  def 			a_   (  lowercase__   :int    ):
  			return int(x / 2**20    )
  
  
  
  
  class  __snake_case      :
  
  
  
  
  							def __enter__(    self:						Dict      ):
  										gc.collect()
  										torch.cuda.empty_cache()
  										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  										__lowerCamelCase        =			torch.cuda.memory_allocated()
  										__lowerCamelCase        =			psutil.Process()
  
  										__lowerCamelCase        =			self.cpu_mem_used()
  										__lowerCamelCase        =			True
  										__lowerCamelCase        =			threading.Thread(target=self.peak_monitor_func      )
  										__lowerCamelCase        =			True
  										peak_monitor_thread.start()
  										return self
  
  
  
  
  							def      __a      (    self:						str      ):
  										return self.process.memory_info().rss
  
  
  
  
  							def      __a      (    self:						List[str]      ):
  										__lowerCamelCase        =			-1
  
  										while True:
  													__lowerCamelCase        =			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:						Any    ,       *A_:						Any      ):
  										__lowerCamelCase        =			False
  
  										gc.collect()
  										torch.cuda.empty_cache()
  										__lowerCamelCase        =			torch.cuda.memory_allocated()
  										__lowerCamelCase        =			torch.cuda.max_memory_allocated()
  										__lowerCamelCase        =			bamb(self.end - self.begin      )
  										__lowerCamelCase        =			bamb(self.peak - self.begin      )
  
  										__lowerCamelCase        =			self.cpu_mem_used()
  										__lowerCamelCase        =			bamb(self.cpu_end - self.cpu_begin      )
  										__lowerCamelCase        =			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
  
  					__magic_name__	:   Dict          =							mocked_dataloaders  # noqa: F811
  
  
  
  
  
  def 			a_   (  lowercase__   :List[Any], lowercase__   :List[str]    ):
  			# For testing only
  			if os.environ.get("""TESTING_MOCKED_DATALOADERS""", lowercase__    ) == "1":
  						__lowerCamelCase        =			2
  
  			# New Code #
  			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  			__lowerCamelCase        =			FullyShardedDataParallelPlugin(
  			    state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase__, ranka_only=lowercase__    ), optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase__, ranka_only=lowercase__    ), )
  
  			# Initialize accelerator
  			if args.with_tracking:
  						__lowerCamelCase        =			Accelerator(
  						    cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="""wandb""", project_dir=args.logging_dir, fsdp_plugin=lowercase__, )
  			else:
  						__lowerCamelCase        =			Accelerator(fsdp_plugin=lowercase__    )
  			accelerator.print(accelerator.distributed_type    )
  
  			if hasattr(args.checkpointing_steps, """isdigit"""    ):
  						if args.checkpointing_steps == "epoch":
  									__lowerCamelCase        =			args.checkpointing_steps
  						elif args.checkpointing_steps.isdigit():
  									__lowerCamelCase        =			int(args.checkpointing_steps    )
  						else:
  									raise ValueError(
  									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'    )
  			else:
  						__lowerCamelCase        =			None
  			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  			__lowerCamelCase        =			config["""lr"""]
  			__lowerCamelCase        =			int(config["""num_epochs"""]    )
  			__lowerCamelCase        =			int(config["""seed"""]    )
  			__lowerCamelCase        =			int(config["""batch_size"""]    )
  
  			# We need to initialize the trackers we use, and also store our configuration
  			if args.with_tracking:
  						__lowerCamelCase        =			vars(lowercase__    )
  						accelerator.init_trackers("""fsdp_glue_no_trainer""", lowercase__    )
  
  			__lowerCamelCase        =			AutoTokenizer.from_pretrained(args.model_name_or_path    )
  			__lowerCamelCase        =			load_dataset("""glue""", """mrpc"""    )
  			__lowerCamelCase        =			evaluate.load("""glue""", """mrpc"""    )
  
  			def tokenize_function(lowercase__   :Any    ):
  						# max_length=None => use the model max length (it's actually the default)
  						__lowerCamelCase        =			tokenizer(examples["""sentence1"""], examples["""sentence2"""], truncation=lowercase__, max_length=lowercase__    )
  						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        =			datasets.map(
  						    lowercase__, batched=lowercase__, 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        =			tokenized_datasets.rename_column("""label""", """labels"""    )
  
  			# If the batch size is too big we use gradient accumulation
  			__lowerCamelCase        =			1
  			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  						__lowerCamelCase        =			batch_size // MAX_GPU_BATCH_SIZE
  						__lowerCamelCase        =			MAX_GPU_BATCH_SIZE
  
  			def collate_fn(lowercase__   :Optional[int]    ):
  						# On TPU it's best to pad everything to the same length or training will be very slow.
  						__lowerCamelCase        =			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        =			16
  						elif accelerator.mixed_precision != "no":
  									__lowerCamelCase        =			8
  						else:
  									__lowerCamelCase        =			None
  
  						return tokenizer.pad(
  						    lowercase__, padding="""longest""", max_length=lowercase__, pad_to_multiple_of=lowercase__, return_tensors="""pt""", )
  
  			# Instantiate dataloaders.
  			__lowerCamelCase        =			DataLoader(
  			    tokenized_datasets["""train"""], shuffle=lowercase__, collate_fn=lowercase__, batch_size=lowercase__    )
  			__lowerCamelCase        =			DataLoader(
  			    tokenized_datasets["""validation"""], shuffle=lowercase__, collate_fn=lowercase__, batch_size=lowercase__    )
  
  			set_seed(lowercase__    )
  
  			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  			__lowerCamelCase        =			AutoModelForSequenceClassification.from_pretrained(
  			    args.model_name_or_path, return_dict=lowercase__, low_cpu_mem_usage=lowercase__    )
  
  			# New Code #
  			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  			__lowerCamelCase        =			accelerator.prepare(lowercase__    )
  			accelerator.print(lowercase__    )
  
  			# 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        =			torch.optim.AdamW(params=model.parameters(), lr=lowercase__, weight_decay=2e-4    )
  
  			# Instantiate scheduler
  			__lowerCamelCase        =			get_linear_schedule_with_warmup(
  			    optimizer=lowercase__, num_warmup_steps=10, num_training_steps=(len(lowercase__    ) * 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        =			accelerator.prepare(
  			    lowercase__, lowercase__, lowercase__, lowercase__    )
  
  			__lowerCamelCase        =			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        =			os.path.basename(args.resume_from_checkpoint    )
  						else:
  									# Get the most recent checkpoint
  									__lowerCamelCase        =			[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
  									dirs.sort(key=os.path.getctime    )
  									__lowerCamelCase        =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  						# Extract `epoch_{i}` or `step_{i}`
  						__lowerCamelCase        =			os.path.splitext(lowercase__    )[0]
  
  						if "epoch" in training_difference:
  									num_epochs -= int(training_difference.replace("""epoch_""", """"""    )    )
  									__lowerCamelCase        =			None
  						else:
  									__lowerCamelCase        =			int(training_difference.replace("""step_""", """"""    )    )
  									num_epochs -= resume_step // len(lowercase__    )
  									# If resuming by step, we also need to know exactly how far into the DataLoader we went
  									__lowerCamelCase        =			(num_epochs * len(lowercase__    )) - resume_step
  
      # Now we train the model
  			for epoch in range(lowercase__    ):
  						# 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        =			0
  									for step, batch in enumerate(lowercase__    ):
  												# 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        =			model(**lowercase__    )
  												__lowerCamelCase        =			outputs.loss
  												__lowerCamelCase        =			loss / gradient_accumulation_steps
  												# We keep track of the loss at each epoch
  												if args.with_tracking:
  															total_loss += loss.detach().float()
  												accelerator.backward(lowercase__    )
  												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(lowercase__, lowercase__    ):
  															__lowerCamelCase        =			f'step_{overall_step}'
  															if overall_step % checkpointing_steps == 0:
  																		if args.output_dir is not None:
  																					__lowerCamelCase        =			os.path.join(args.output_dir, lowercase__    )
  																		accelerator.save_state(lowercase__    )
          # 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=lowercase__, )
  
  						# New Code #
  						# context manager to track the peak memory usage during the evaluation
  						with TorchTracemalloc() as tracemalloc:
  									model.eval()
  									for step, batch in enumerate(lowercase__    ):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												batch.to(accelerator.device    )
  												with torch.no_grad():
  															__lowerCamelCase        =			model(**lowercase__    )
  												__lowerCamelCase        =			outputs.logits.argmax(dim=-1    )
  												__lowerCamelCase    ,__lowerCamelCase        =			accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
  												metric.add_batch(
  												    predictions=lowercase__, references=lowercase__, )
  
  									__lowerCamelCase        =			metric.compute()
  									# Use accelerator.print to print only on the main process.
  									accelerator.print(f'epoch {epoch}:', lowercase__    )
  									if args.with_tracking:
  												accelerator.log(
  												    {
  												        """accuracy""": eval_metric["""accuracy"""],
  												        """f1""": eval_metric["""f1"""],
  												        """train_loss""": total_loss.item() / len(lowercase__    ),
  												    }, step=lowercase__, )
  
  									if checkpointing_steps == "epoch":
  												__lowerCamelCase        =			f'epoch_{epoch}'
  												if args.output_dir is not None:
  															__lowerCamelCase        =			os.path.join(args.output_dir, lowercase__    )
  												accelerator.save_state(lowercase__    )
          # 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=lowercase__, )
  
  			if args.with_tracking:
  						accelerator.end_training()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser(description="""Simple example of training script."""    )
  			parser.add_argument(
  			    """--mixed_precision""", type=lowercase__, default=lowercase__, 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=lowercase__, default=lowercase__, 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=lowercase__, default=lowercase__, 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=lowercase__, default=""".""", help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""", )
  			parser.add_argument(
  			    """--logging_dir""", type=lowercase__, default="""logs""", help="""Location on where to store experiment tracking logs`""", )
  			parser.add_argument(
  			    """--model_name_or_path""", type=lowercase__, help="""Path to pretrained model or model identifier from huggingface.co/models.""", required=lowercase__, )
  			__lowerCamelCase        =			parser.parse_args()
  			__lowerCamelCase        =			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  			training_function(lowercase__, lowercase__    )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import os
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			os.path.dirname(os.path.realpath(lowercase__    )    )
  			__lowerCamelCase        =			os.path.join(lowercase__, """triangle.txt"""    )
  
  			with open(lowercase__    ) as in_file:
  						__lowerCamelCase        =			[[int(lowercase__    ) for i in line.split()] for line in in_file]
  
  			while len(lowercase__    ) != 1:
  						__lowerCamelCase        =			triangle.pop()
  						__lowerCamelCase        =			triangle[-1]
  						for j in range(len(lowercase__    ) - 1    ):
  									curr_row[j] += max(last_row[j], last_row[j + 1]    )
  			return triangle[0][0]
  
  
  if __name__ == "__main__":
  					print(solution())
  
  
  
  
  