'''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=	1_6
a=	3_2







def 			_UpperCamelCase			(  _a						:						Optional[int]      ):




    """simple docstring"""



    return int(x / 2**2_0      )






class       __lowercase		:







     """simple docstring"""






     def __enter__(		self     ):
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         __UpperCamelCase						:						Tuple		     =	torch.cuda.memory_allocated()
         __UpperCamelCase						:						Any		     =	psutil.Process()

         __UpperCamelCase						:						Union[str, Any]		     =	self.cpu_mem_used()
         __UpperCamelCase						:						Optional[int]		     =	True
         __UpperCamelCase						:						List[str]		     =	threading.Thread(target=self.peak_monitor_func     )
         __UpperCamelCase						:						List[str]		     =	True
         peak_monitor_thread.start()
         return self







     def lowerCAmelCase  (		self     ):
         return self.process.memory_info().rss







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	-1

         while True:
             __UpperCamelCase						:						Optional[Any]		     =	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  ,      *_lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	False

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

         __UpperCamelCase						:						str		     =	self.cpu_mem_used()
         __UpperCamelCase						:						List[str]		     =	bamb(self.cpu_end - self.cpu_begin     )
         __UpperCamelCase						:						Any		     =	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=	mocked_dataloaders  # noqa: F811







def 			_UpperCamelCase			(  _a						:						Any					,  _a						:						int      ):




    """simple docstring"""



    if os.environ.get('TESTING_MOCKED_DATALOADERS'					,  _a      ) == "1":
        __UpperCamelCase						:						List[str]		     =	2

    # New Code #
    # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
    __UpperCamelCase						:						Any		     =	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:
        __UpperCamelCase						:						Tuple		     =	Accelerator(
            cpu=args.cpu					,  mixed_precision=args.mixed_precision					,  log_with='wandb'					,  project_dir=args.logging_dir					,  fsdp_plugin=_a					,  )
    else:
        __UpperCamelCase						:						Optional[int]		     =	Accelerator(fsdp_plugin=_a      )
    accelerator.print(accelerator.distributed_type      )

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

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

    __UpperCamelCase						:						Optional[int]		     =	AutoTokenizer.from_pretrained(args.model_name_or_path      )
    __UpperCamelCase						:						Tuple		     =	load_dataset('glue'					,  'mrpc'      )
    __UpperCamelCase						:						Optional[int]		     =	evaluate.load('glue'					,  'mrpc'      )

    def tokenize_function(_a						:						Optional[Any]      ):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase						:						List[Any]		     =	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():
        __UpperCamelCase						:						Tuple		     =	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
    __UpperCamelCase						:						Optional[int]		     =	tokenized_datasets.rename_column('label'					,  'labels'      )

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

    def collate_fn(_a						:						Optional[Any]      ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase						:						Any		     =	1_2_8 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						:						Optional[int]		     =	1_6
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase						:						Union[str, Any]		     =	8
        else:
            __UpperCamelCase						:						int		     =	None

        return tokenizer.pad(
            _a					,  padding='longest'					,  max_length=_a					,  pad_to_multiple_of=_a					,  return_tensors='pt'					,  )

    # Instantiate dataloaders.
    __UpperCamelCase						:						Union[str, Any]		     =	DataLoader(
        tokenized_datasets['train']					,  shuffle=_a					,  collate_fn=_a					,  batch_size=_a      )
    __UpperCamelCase						:						Optional[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)
    __UpperCamelCase						:						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
    __UpperCamelCase						:						int		     =	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
    __UpperCamelCase						:						Optional[int]		     =	torch.optim.AdamW(params=model.parameters()					,  lr=_a					,  weight_decay=2E-4      )

    # Instantiate scheduler
    __UpperCamelCase						:						int		     =	get_linear_schedule_with_warmup(
        optimizer=_a					,  num_warmup_steps=1_0					,  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.
    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	accelerator.prepare(
        _a					,  _a					,  _a					,  _a      )

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

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

            __UpperCamelCase						:						Any		     =	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":
                __UpperCamelCase						:						Union[str, Any]		     =	f"""epoch_{epoch}"""
                if args.output_dir is not None:
                    __UpperCamelCase						:						str		     =	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 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						List[str]		     =	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					,  )
    __UpperCamelCase						:						int		     =	parser.parse_args()
    __UpperCamelCase						:						Optional[Any]		     =	{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
    training_function(_a					,  _a      )


if __name__ == "__main__":
  main()




'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						bool = False      ):




    """simple docstring"""



    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(_a      )
    np.random.seed(_a      )
    torch.manual_seed(_a      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(_a      )
    elif is_npu_available():
        torch.npu.manual_seed_all(_a      )
    else:
        torch.cuda.manual_seed_all(_a      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(_a      )







def 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    if rng_type == RNGType.TORCH:
        __UpperCamelCase						:						Union[str, Any]		     =	torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __UpperCamelCase						:						int		     =	torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __UpperCamelCase						:						Dict		     =	torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __UpperCamelCase						:						Optional[Any]		     =	torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __UpperCamelCase						:						str		     =	torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __UpperCamelCase						:						List[Any]		     =	generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __UpperCamelCase						:						List[Any]		     =	AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __UpperCamelCase						:						Union[str, Any]		     =	rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __UpperCamelCase						:						str		     =	rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __UpperCamelCase						:						List[Any]		     =	rng_state.to(state.device      )
        torch.distributed.broadcast(_a					,  0      )
        __UpperCamelCase						:						Optional[Any]		     =	rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(_a					,  0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(_a      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(_a      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(_a      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(_a      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(_a      )







def 			_UpperCamelCase			(  _a						:						List[Union[str, RNGType]]					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    for rng_type in rng_types:
        synchronize_rng_state(RNGType(_a      )					,  generator=_a      )



