


'''simple docstring'''


import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase   ):
  return int(x / 2**20   )






class 	lowerCAmelCase__			:




   def __enter__(		self  :     int )   ->Optional[Any]:



     '''simple docstring'''







     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
     _UpperCAmelCase		:       Tuple								=      torch.cuda.memory_allocated()
     return self






   def __exit__(		self  :     Tuple	,  *lowerCamelCase__  :     str )   ->int:



     '''simple docstring'''







     gc.collect()
     torch.cuda.empty_cache()
     _UpperCAmelCase		:       List[str]								=      torch.cuda.memory_allocated()
     _UpperCAmelCase		:       Tuple								=      torch.cuda.max_memory_allocated()
     _UpperCAmelCase		:       List[Any]								=      bamb(self.end - self.begin )
     _UpperCAmelCase		:       int								=      bamb(self.peak - self.begin )
     # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"      , __lowerCAmelCase = 320      , __lowerCAmelCase = 160      , ):
  _UpperCAmelCase		:       int								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
  _UpperCAmelCase		:       Any								=      load_dataset(
      "glue"      , "mrpc"      , split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

  def tokenize_function(__lowerCAmelCase   ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCAmelCase		:       List[Any]								=      tokenizer(examples["sentence1"]      , examples["sentence2"]      , truncation=__lowerCAmelCase      , max_length=__lowerCAmelCase   )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _UpperCAmelCase		:       int								=      datasets.map(
      __lowerCAmelCase      , batched=__lowerCAmelCase      , remove_columns=["idx", "sentence1", "sentence2"]      , load_from_cache_file=__lowerCAmelCase   )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _UpperCAmelCase		:       List[Any]								=      tokenized_datasets.rename_column("label"      , "labels"   )

  def collate_fn(__lowerCAmelCase   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__lowerCAmelCase      , padding="max_length"      , max_length=128      , return_tensors="pt"   )
    return tokenizer.pad(__lowerCAmelCase      , padding="longest"      , return_tensors="pt"   )

  # Instantiate dataloaders.
  _UpperCAmelCase		:       Any								=      DataLoader(
      tokenized_datasets["train"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )
  _UpperCAmelCase		:       List[str]								=      DataLoader(
      tokenized_datasets["validation"]      , shuffle=__lowerCAmelCase      , collate_fn=__lowerCAmelCase      , batch_size=__lowerCAmelCase   )

  return train_dataloader, eval_dataloader






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
  # Initialize accelerator
  _UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCAmelCase		:       List[Any]								=      config["lr"]
  _UpperCAmelCase		:       List[Any]								=      int(config["num_epochs"]   )
  _UpperCAmelCase		:       int								=      int(config["seed"]   )
  _UpperCAmelCase		:       Union[str, Any]								=      int(config["batch_size"]   )
  _UpperCAmelCase		:       Tuple								=      args.model_name_or_path

  set_seed(__lowerCAmelCase   )
  _UpperCAmelCase		:       List[str]								=      get_dataloaders(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , args.n_train      , args.n_val   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCAmelCase		:       Optional[int]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

  # Instantiate optimizer
  _UpperCAmelCase		:       Dict								=      (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _UpperCAmelCase		:       str								=      optimizer_cls(params=model.parameters()      , lr=__lowerCAmelCase   )

  if accelerator.state.deepspeed_plugin is not None:
    _UpperCAmelCase		:       Any								=      accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    _UpperCAmelCase		:       Any								=      1
  _UpperCAmelCase		:       Optional[int]								=      (len(__lowerCAmelCase   ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    _UpperCAmelCase		:       Tuple								=      get_linear_schedule_with_warmup(
        optimizer=__lowerCAmelCase      , num_warmup_steps=0      , num_training_steps=__lowerCAmelCase      , )
  else:
    _UpperCAmelCase		:       Optional[Any]								=      DummyScheduler(__lowerCAmelCase      , total_num_steps=__lowerCAmelCase      , warmup_num_steps=0   )

  # Prepare everything
  # 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		:       int								=      accelerator.prepare(
      __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

  # We need to keep track of how many total steps we have iterated over
  _UpperCAmelCase		:       Union[str, Any]								=      0
  # We also need to keep track of the stating epoch so files are named properly
  _UpperCAmelCase		:       str								=      0

  # Now we train the model
  _UpperCAmelCase		:       Optional[Any]								=      {}
  for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(__lowerCAmelCase   ):
        _UpperCAmelCase		:       Union[str, Any]								=      model(**__lowerCAmelCase   )
        _UpperCAmelCase		:       Union[str, Any]								=      outputs.loss
        _UpperCAmelCase		:       List[Any]								=      loss / gradient_accumulation_steps
        accelerator.backward(__lowerCAmelCase   )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

        overall_step += 1

        # 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   )   )   )
    _UpperCAmelCase		:       Optional[int]								=      tracemalloc.peaked + bamb(tracemalloc.begin   )
    if args.peak_memory_upper_bound is not None:
      assert (
          train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
      ), "Peak memory usage exceeded the upper bound"

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir      , "peak_memory_utilization.json"   )      , "w"   ) as f:
      json.dump(__lowerCAmelCase      , __lowerCAmelCase   )






def  __lowerCAmelCase ():
  _UpperCAmelCase		:       Any								=      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
  parser.add_argument(
      "--model_name_or_path"      , type=__lowerCAmelCase      , default="bert-base-cased"      , help="Path to pretrained model or model identifier from huggingface.co/models."      , required=__lowerCAmelCase      , )
  parser.add_argument(
      "--output_dir"      , type=__lowerCAmelCase      , default="."      , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      , )
  parser.add_argument(
      "--peak_memory_upper_bound"      , type=__lowerCAmelCase      , default=__lowerCAmelCase      , help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."      , )
  parser.add_argument(
      "--n_train"      , type=__lowerCAmelCase      , default=320      , help="Number of training examples to use."      , )
  parser.add_argument(
      "--n_val"      , type=__lowerCAmelCase      , default=160      , help="Number of validation examples to use."      , )
  parser.add_argument(
      "--num_epochs"      , type=__lowerCAmelCase      , default=1      , help="Number of train epochs."      , )
  _UpperCAmelCase		:       Tuple								=      parser.parse_args()
  _UpperCAmelCase		:       Optional[Any]								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
      main()







'''simple docstring'''


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
						import transformers






def  __lowerCAmelCase (__lowerCAmelCase   ):
		random.seed(__lowerCAmelCase   )
		np.random.seed(__lowerCAmelCase   )
		torch.manual_seed(__lowerCAmelCase   )
		torch.cuda.manual_seed_all(__lowerCAmelCase   )
		# ^^ safe to call this function even if cuda is not available






class 	lowerCAmelCase__			:




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter]	,  lowerCamelCase__  :     float = 0.9_9_9_9	,  lowerCamelCase__  :     float = 0.0	,  lowerCamelCase__  :     int = 0	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     Union[float, int] = 1.0	,  lowerCamelCase__  :     Union[float, int] = 2 / 3	,  lowerCamelCase__  :     Optional[Any] = None	,  lowerCamelCase__  :     Dict[str, Any] = None	,  **lowerCamelCase__  :     Optional[int]	,  )   ->Optional[Any]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       List[Any]								=      (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       List[str]								=      parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							_UpperCAmelCase		:       Optional[int]								=      True

					if kwargs.get("max_value"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Tuple								=      "The `max_value` argument is deprecated. Please use `decay` instead."
							deprecate("max_value"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							_UpperCAmelCase		:       str								=      kwargs["max_value"]

					if kwargs.get("min_value"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Optional[int]								=      "The `min_value` argument is deprecated. Please use `min_decay` instead."
							deprecate("min_value"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      kwargs["min_value"]

					_UpperCAmelCase		:       Optional[Any]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Dict								=      [p.clone().detach() for p in parameters]

					if kwargs.get("device"	,  lowerCamelCase__ ) is not None:
							_UpperCAmelCase		:       Any								=      "The `device` argument is deprecated. Please use `to` instead."
							deprecate("device"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__ )
							self.to(device=kwargs["device"] )

					_UpperCAmelCase		:       Union[str, Any]								=      None

					_UpperCAmelCase		:       int								=      decay
					_UpperCAmelCase		:       Any								=      min_decay
					_UpperCAmelCase		:       Optional[int]								=      update_after_step
					_UpperCAmelCase		:       str								=      use_ema_warmup
					_UpperCAmelCase		:       Union[str, Any]								=      inv_gamma
					_UpperCAmelCase		:       Union[str, Any]								=      power
					_UpperCAmelCase		:       List[str]								=      0
					_UpperCAmelCase		:       List[str]								=      None  # set in `step()`

					_UpperCAmelCase		:       Optional[int]								=      model_cls
					_UpperCAmelCase		:       Union[str, Any]								=      model_config




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->"EMAModel":



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model_cls.load_config(lowerCamelCase__	,  return_unused_kwargs=lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      model_cls.from_pretrained(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      cls(model.parameters()	,  model_cls=lowerCamelCase__	,  model_config=model.config )

					ema_model.load_state_dict(lowerCamelCase__ )
					return ema_model




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int )   ->Dict:



					'''simple docstring'''







					if self.model_cls is None:
							raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." )

					if self.model_config is None:
							raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." )

					_UpperCAmelCase		:       int								=      self.model_cls.from_config(self.model_config )
					_UpperCAmelCase		:       Union[str, Any]								=      self.state_dict()
					state_dict.pop("shadow_params"	,  lowerCamelCase__ )

					model.register_to_config(**lowerCamelCase__ )
					self.copy_to(model.parameters() )
					model.save_pretrained(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     int )   ->float:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      max(0	,  optimization_step - self.update_after_step - 1 )

					if step <= 0:
							return 0.0

					if self.use_ema_warmup:
							_UpperCAmelCase		:       int								=      1 - (1 + step / self.inv_gamma) ** -self.power
					else:
							_UpperCAmelCase		:       Any								=      (1 + step) / (10 + step)

					_UpperCAmelCase		:       int								=      min(lowerCamelCase__	,  self.decay )
					# make sure decay is not smaller than min_decay
					_UpperCAmelCase		:       Union[str, Any]								=      max(lowerCamelCase__	,  self.min_decay )
					return cur_decay_value




			@torch.no_grad()
			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->Dict:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  torch.nn.Module ):
							_UpperCAmelCase		:       Union[str, Any]								=      (
							    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
							    "Please pass the parameters of the module instead."
							)
							deprecate(
							    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"	,  "1.0.0"	,  lowerCamelCase__	,  standard_warn=lowerCamelCase__	,  )
							_UpperCAmelCase		:       Any								=      parameters.parameters()

					_UpperCAmelCase		:       Dict								=      list(lowerCamelCase__ )

					self.optimization_step += 1

					# Compute the decay factor for the exponential moving average.
					_UpperCAmelCase		:       Tuple								=      self.get_decay(self.optimization_step )
					_UpperCAmelCase		:       Any								=      decay
					_UpperCAmelCase		:       Optional[Any]								=      1 - decay

					_UpperCAmelCase		:       Union[str, Any]								=      contextlib.nullcontext
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									_UpperCAmelCase		:       str								=      deepspeed.zero.GatheredParameters(lowerCamelCase__	,  modifier_rank=lowerCamelCase__ )

							with context_manager():
									if param.requires_grad:
											s_param.sub_(one_minus_decay * (s_param - param) )
									else:
											s_param.copy_(lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      list(lowerCamelCase__ )
					for s_param, param in zip(self.shadow_params	,  lowerCamelCase__ ):
							param.data.copy_(s_param.to(param.device ).data )




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Dict=None	,  lowerCamelCase__  :     Optional[int]=None )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [
					    p.to(device=lowerCamelCase__	,  dtype=lowerCamelCase__ ) if p.is_floating_point() else p.to(device=lowerCamelCase__ )
					    for p in self.shadow_params
					]




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->dict:



					'''simple docstring'''







					return {
					    "decay": self.decay,
					    "min_decay": self.min_decay,
					    "optimization_step": self.optimization_step,
					    "update_after_step": self.update_after_step,
					    "use_ema_warmup": self.use_ema_warmup,
					    "inv_gamma": self.inv_gamma,
					    "power": self.power,
					    "shadow_params": self.shadow_params,
					}




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      [param.detach().cpu().clone() for param in parameters]




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Iterable[torch.nn.Parameter] )   ->None:



					'''simple docstring'''







					if self.temp_stored_params is None:
							raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" )
					for c_param, param in zip(self.temp_stored_params	,  lowerCamelCase__ ):
							param.data.copy_(c_param.data )

					# Better memory-wise.
					_UpperCAmelCase		:       int								=      None






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     dict )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      copy.deepcopy(lowerCamelCase__ )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("decay"	,  self.decay )
					if self.decay < 0.0 or self.decay > 1.0:
							raise ValueError("Decay must be between 0 and 1" )

					_UpperCAmelCase		:       Union[str, Any]								=      state_dict.get("min_decay"	,  self.min_decay )
					if not isinstance(self.min_decay	,  lowerCamelCase__ ):
							raise ValueError("Invalid min_decay" )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("optimization_step"	,  self.optimization_step )
					if not isinstance(self.optimization_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid optimization_step" )

					_UpperCAmelCase		:       List[Any]								=      state_dict.get("update_after_step"	,  self.update_after_step )
					if not isinstance(self.update_after_step	,  lowerCamelCase__ ):
							raise ValueError("Invalid update_after_step" )

					_UpperCAmelCase		:       str								=      state_dict.get("use_ema_warmup"	,  self.use_ema_warmup )
					if not isinstance(self.use_ema_warmup	,  lowerCamelCase__ ):
							raise ValueError("Invalid use_ema_warmup" )

					_UpperCAmelCase		:       int								=      state_dict.get("inv_gamma"	,  self.inv_gamma )
					if not isinstance(self.inv_gamma	,  (float, int) ):
							raise ValueError("Invalid inv_gamma" )

					_UpperCAmelCase		:       Any								=      state_dict.get("power"	,  self.power )
					if not isinstance(self.power	,  (float, int) ):
							raise ValueError("Invalid power" )

					_UpperCAmelCase		:       List[str]								=      state_dict.get("shadow_params"	,  lowerCamelCase__ )
					if shadow_params is not None:
							_UpperCAmelCase		:       Optional[Any]								=      shadow_params
							if not isinstance(self.shadow_params	,  lowerCamelCase__ ):
									raise ValueError("shadow_params must be a list" )
							if not all(isinstance(lowerCamelCase__	,  torch.Tensor ) for p in self.shadow_params ):
									raise ValueError("shadow_params must all be Tensors" )



