


"""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()








"""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())




