
"""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
#
########################################################################


_A      :	Optional[Any]       		=	16
_A      :	Any       		=	32


def   __magic_name__     (				__snake_case    :	Union[str, Any]  )			->    Tuple:
       return int(x / 2**20  )

class 		a__						:
      def __enter__(      self    ):
             gc.collect()
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
             lowercase   :    List[Any]			=							torch.cuda.memory_allocated()
             lowercase   :    List[Any]			=							psutil.Process()

             lowercase   :    int			=							self.cpu_mem_used()
             lowercase   :    Dict			=							True
             lowercase   :    Optional[int]			=							threading.Thread(target=self.peak_monitor_func    )
             lowercase   :    Optional[int]			=							True
             peak_monitor_thread.start()
             return self
      def       __magic_name__      (      self    ):
             return self.process.memory_info().rss
      def       __magic_name__      (      self    ):
             lowercase   :    Dict			=							-1

             while True:
                    lowercase   :    Optional[int]			=							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     ,   *_a    ):
             lowercase   :    Optional[int]			=							False

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

             lowercase   :    Any			=							self.cpu_mem_used()
             lowercase   :    List[str]			=							bamb(self.cpu_end - self.cpu_begin    )
             lowercase   :    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

   _A      :	Dict       		=	mocked_dataloaders  # noqa: F811


def   __magic_name__     (				__snake_case    :	List[str]					,			__snake_case    :	Optional[int]  )			->    Tuple:


       if os.environ.get("TESTING_MOCKED_DATALOADERS"					,			_A  ) == "1":
              lowercase   :    Union[str, Any]			=							2

       # New Code #
       # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
       lowercase   :    Optional[int]			=							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:
              lowercase   :    List[str]			=							Accelerator(
                  cpu=args.cpu					,			mixed_precision=args.mixed_precision					,			log_with="wandb"					,			project_dir=args.logging_dir					,			fsdp_plugin=_A					,			)
       else:
              lowercase   :    str			=							Accelerator(fsdp_plugin=_A  )
       accelerator.print(accelerator.distributed_type  )

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

       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
              lowercase   :    List[str]			=							vars(_A  )
              accelerator.init_trackers("fsdp_glue_no_trainer"					,			_A  )

       lowercase   :    Optional[Any]			=							AutoTokenizer.from_pretrained(args.model_name_or_path  )
       lowercase   :    int			=							load_dataset("glue"					,			"mrpc"  )
       lowercase   :    str			=							evaluate.load("glue"					,			"mrpc"  )

       def tokenize_function(__snake_case    :	Tuple  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase   :    Dict			=							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():
              lowercase   :    List[str]			=							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
       lowercase   :    int			=							tokenized_datasets.rename_column("label"					,			"labels"  )

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

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

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

       # Instantiate dataloaders.
       lowercase   :    Dict			=							DataLoader(
           tokenized_datasets["train"]					,			shuffle=_A					,			collate_fn=_A					,			batch_size=_A  )
       lowercase   :    int			=							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)
       lowercase   :    Dict			=							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
       lowercase   :    List[Any]			=							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
       lowercase   :    str			=							torch.optim.AdamW(params=model.parameters()					,			lr=_A					,			weight_decay=2E-4  )

       # Instantiate scheduler
       lowercase   :    Optional[int]			=							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.
       lowercase		,   lowercase		,   lowercase		,   lowercase   :    Any			=							accelerator.prepare(
           _A					,			_A					,			_A					,			_A  )

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

              if "epoch" in training_difference:
                     num_epochs -= int(training_difference.replace("epoch_"					,			""  )  )
                     lowercase   :    Dict			=							None
              else:
                     lowercase   :    str			=							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
                     lowercase   :    Optional[Any]			=							(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:
                            lowercase   :    int			=							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  )
                            lowercase   :    Union[str, Any]			=							model(**_A  )
                            lowercase   :    Optional[Any]			=							outputs.loss
                            lowercase   :    Dict			=							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  ):
                                   lowercase   :    Optional[Any]			=							f"""step_{overall_step}"""
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 lowercase   :    List[Any]			=							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():
                                   lowercase   :    Tuple			=							model(**_A  )
                            lowercase   :    Union[str, Any]			=							outputs.logits.argmax(dim=-1  )
                            lowercase		,   lowercase   :    str			=							accelerator.gather_for_metrics((predictions, batch["labels"])  )
                            metric.add_batch(
                                predictions=_A					,			references=_A					,			)

                     lowercase   :    Tuple			=							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":
                            lowercase   :    int			=							f"""epoch_{epoch}"""
                            if args.output_dir is not None:
                                   lowercase   :    int			=							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__     (				)			->    Optional[Any]:
       lowercase   :    Union[str, Any]			=							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					,			)
       lowercase   :    Optional[Any]			=							parser.parse_args()
       lowercase   :    str			=							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(_A					,			_A  )


if __name__ == "__main__":
   main()




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


