




import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowerCAmelCase    : List[str]		= logging.getLogger(__name__)



def     lowerCAmelCase		(		_lowerCAmelCase				: str						):




 """simple docstring"""







 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 torch.cuda.manual_seed_all(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Any    ,				_lowerCAmelCase				: Optional[Any]						):




 """simple docstring"""







 model.eval()
 UpperCAmelCase__							=    next(model.parameters()						).device
 UpperCAmelCase__							=    1
 UpperCAmelCase__							=    torch.zeros((eval_batch_size, context_len)    ,				dtype=torch.long    ,				device=_lowerCAmelCase						)
 UpperCAmelCase__							=    DataLoader(_lowerCAmelCase    ,				shuffle=_lowerCAmelCase    ,				batch_size=_lowerCAmelCase						)
 UpperCAmelCase__							=    torch.zeros(1    ,				device=_lowerCAmelCase						)
 UpperCAmelCase__							=    0
 for batch in eval_dataloader:
  batch.to(_lowerCAmelCase						)
  # pad
  context.zero_()
  for i in range(_lowerCAmelCase						):
   UpperCAmelCase__							=    batch[i]
  UpperCAmelCase__							=    model(_lowerCAmelCase    ,				labels=_lowerCAmelCase						)
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0						)
 UpperCAmelCase__							=    eval_loss / nb_eval_examples
 UpperCAmelCase__							=    torch.exp(_lowerCAmelCase						)
 model.train()
 return perplexity



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Any]="gpt2"						):




 """simple docstring"""







 UpperCAmelCase__							=    GPTaLMHeadModel.from_pretrained(_lowerCAmelCase    ,				output_hidden_states=_lowerCAmelCase						)
 torch.save(model.state_dict()    ,				model_name + "local.pt"						)
 return model



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Dict    ,				_lowerCAmelCase				: Union[str, Any]						):




 """simple docstring"""







 UpperCAmelCase__							=    copy.deepcopy(_lowerCAmelCase						)
 model.to(_lowerCAmelCase						)

 UpperCAmelCase__							=    ["bias", "LayerNorm.weight"]
 UpperCAmelCase__							=    [
     {
         "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
         "weight_decay": 0.0,
     },
     {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)], "weight_decay": 0.0},
 ]
 UpperCAmelCase__							=    AdamW(_lowerCAmelCase    ,				lr=5E-5    ,				eps=1E-8						)
 UpperCAmelCase__							=    get_linear_schedule_with_warmup(_lowerCAmelCase    ,				0    ,				_lowerCAmelCase						)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Optional[int]    ,				_lowerCAmelCase				: int    ,				_lowerCAmelCase				: List[str]						):




 """simple docstring"""







 UpperCAmelCase__							=    np.array(_lowerCAmelCase						).mean()
 UpperCAmelCase__							=    np.array(_lowerCAmelCase						).std()
 UpperCAmelCase__							=    (real_perps - avg) / std
 UpperCAmelCase__							=    list(zip(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)						)
 joblib.dump(_lowerCAmelCase    ,				_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[Any]    ,				_lowerCAmelCase				: List[str]    ,				_lowerCAmelCase				: Union[str, Any]    ,				_lowerCAmelCase				: Optional[Any]    ,				_lowerCAmelCase				: Optional[Any]    ,				_lowerCAmelCase				: Any    ,				_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Union[str, Any]="dev.jbl"    ,				_lowerCAmelCase				: int=recopy_gpta    ,				):




 """simple docstring"""







 UpperCAmelCase__							=    []
 UpperCAmelCase__							=    []
 UpperCAmelCase__							=    []

 # Initialize the transformer model
 UpperCAmelCase__							=    copy.deepcopy(_lowerCAmelCase						)
 orig_model.to(device="cpu"						)
 torch.cuda.empty_cache()

 # Compute perplexity of initial transformer model for comparison
 model.train()
 UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__							=    recopy_model(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)

 for step in tqdm(range(_lowerCAmelCase						)						):
  UpperCAmelCase__							=    torch.zeros((1, context_len)    ,				dtype=torch.long    ,				device=_lowerCAmelCase						)
  UpperCAmelCase__							=    random.choice(_lowerCAmelCase						)
  UpperCAmelCase__							=    random.randint(0    ,				len(story[0]						) - context_len - 1						)
  UpperCAmelCase__							=    story[0][start : start + context_len]
  lm_optimizer.zero_grad()
  UpperCAmelCase__							=    model(_lowerCAmelCase    ,				labels=_lowerCAmelCase						)
  UpperCAmelCase__							=    outputs[0]
  UpperCAmelCase__							=    compute_perplexity(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)
  model.train()
  lm_loss.backward()
  # Do LM backprop
  torch.nn.utils.clip_grad_norm_(model.parameters()    ,				3.0						)
  lm_optimizer.step()
  lm_scheduler.step()  # Update learning rate schedule

  # Compute perplexity after back-propagating on the selected context
  UpperCAmelCase__							=    compute_perplexity(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1000 == 0 and step > 1:
   intermittent_save(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__							=    recopy_model(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)

  past_perps.append(past_perp.item()						)
  real_perps.append(orig_perp - real_perp.item()						)
  contexts.append(np.array(context.cpu()						)						)

 intermittent_save(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[str]    ,				_lowerCAmelCase				: Dict="data/tokenized_stories_train_wikitext103.jbl"    ,				_lowerCAmelCase				: str=100    ,				_lowerCAmelCase				: Tuple=1026    ,				_lowerCAmelCase				: Tuple=True						):




 """simple docstring"""







 UpperCAmelCase__							=    joblib.load(_lowerCAmelCase						)
 print("data loaded"						)
 UpperCAmelCase__							=    []
 if trim:
  for i, example in enumerate(_lowerCAmelCase						):
   if len(example[0]						) > min_len:
    UpperCAmelCase__							=    random.randint(0    ,				len(example[0]						) - context_len - 1						)
    objective_set.append(example[0, start : start + context_len]						)
   if len(_lowerCAmelCase						) >= number:
    break
  UpperCAmelCase__							=    []
  for j in range(i + 1    ,				len(_lowerCAmelCase						)						):
   if len(data[j][0]						) > min_len:
    train_data.append(data[j]						)
 else:
  UpperCAmelCase__							=    data[0:number]
  UpperCAmelCase__							=    data[number:]

 joblib.dump(_lowerCAmelCase    ,				"objective_set.jbl"						)
 print("objective set saved"						)
 return train_data, objective_set



def     lowerCAmelCase		(		_lowerCAmelCase				: List[str]    ,				_lowerCAmelCase				: Union[str, Any]    ,				_lowerCAmelCase				: Optional[Any]    ,				_lowerCAmelCase				: List[Any]    ,				_lowerCAmelCase				: List[str]=50    ,				_lowerCAmelCase				: Tuple="secondary_learner.pt"						):




 """simple docstring"""







 UpperCAmelCase__							=    torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)
 # We will use the first 512 pairs from our dataset as a test set for
 # our secondary learner and the rest to train
 UpperCAmelCase__							=    train_dataset[:512]
 UpperCAmelCase__							=    train_dataset[512:]
 UpperCAmelCase__							=    DataLoader(_lowerCAmelCase    ,				shuffle=_lowerCAmelCase    ,				batch_size=_lowerCAmelCase						)
 UpperCAmelCase__							=    DataLoader(_lowerCAmelCase    ,				shuffle=_lowerCAmelCase    ,				batch_size=_lowerCAmelCase						)

 # secondary learner model set up
 UpperCAmelCase__							=    nn.MSELoss()
 UpperCAmelCase__							=    nn.MSELoss(reduction="sum"						)
 secondary_learner.to(_lowerCAmelCase						)
 UpperCAmelCase__							=    torch.optim.Adam(secondary_learner.parameters()    ,				lr=0.00_001						)
 secondary_learner.train()

 # TODO in original code this is written as number of actual batches seen
 # not number of items seen but other places it is number of items instead.
 # improve consistency! changed this to epochs for clarity
 UpperCAmelCase__							=    float("inf"						)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(_lowerCAmelCase						)						):
  UpperCAmelCase__							=    0.0
  secondary_learner.train()
  for step, batch in enumerate(_lowerCAmelCase						):
   UpperCAmelCase__							=    batch[0].to(_lowerCAmelCase						)
   UpperCAmelCase__							=    batch[1].to(_lowerCAmelCase						)
   UpperCAmelCase__							=    secondary_learner(_lowerCAmelCase						)
   q_optimizer.zero_grad()
   UpperCAmelCase__							=    loss(_lowerCAmelCase    ,				real_q.float()						)
   q_loss.backward()
   q_optimizer.step()
   tr_q_loss += q_loss.item()

   # model trains fairly quickly so we won't wait for a full epoch
   # eval is triggered at eval_freq and end of epochs
   if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_lowerCAmelCase						)):
    UpperCAmelCase__							=    tr_q_loss / (step + 1)

    secondary_learner.eval()
    UpperCAmelCase__							=    0.0
    UpperCAmelCase__							=    0.0
    UpperCAmelCase__							=    []
    UpperCAmelCase__							=    []
    # Compute performance of the secondary learner after this batch
    for stepa, batcha in enumerate(_lowerCAmelCase						):
     UpperCAmelCase__							=    batcha[0].to(_lowerCAmelCase						)
     UpperCAmelCase__							=    batcha[1].to(_lowerCAmelCase						)
     UpperCAmelCase__							=    secondary_learner(_lowerCAmelCase						)
     q_lossa += test_loss(_lowerCAmelCase    ,				_lowerCAmelCase						).item()
     sum_qa += torch.sum(_lowerCAmelCase						).item()
     for ei, i in enumerate(predicted_qa.cpu().detach().numpy()						):
      predicted.append(i.item()						)
     for ei, i in enumerate(real_qa.cpu().detach().numpy()						):
      actual.append(i.item()						)

    q_lossa /= len(_lowerCAmelCase						)
    print(
        "Epoch: "    ,				_lowerCAmelCase    ,				"step: "    ,				_lowerCAmelCase    ,				"Avg. q:"    ,				sum_qa / len(_lowerCAmelCase						)    ,				"Train Loss: "    ,				_lowerCAmelCase    ,				"Test Loss: "    ,				_lowerCAmelCase    ,				)
    if q_lossa < best_test_loss:
     joblib.dump((predicted, actual)    ,				"pred_vs_actual.jbl"						)
     torch.save(secondary_learner.state_dict()    ,				_lowerCAmelCase						)
     UpperCAmelCase__							=    q_lossa

   secondary_learner.train()
 return secondary_learner


class       _UpperCamelCase   (					nn.Module		):
      def __init__(      self :Optional[Any]			,   lowerCamelCase :Optional[Any]   )  ->      str:
       super(_UpperCAmelCase			,   self   ).__init__()
       UpperCAmelCase__							=    model.transformer.wte
       UpperCAmelCase__							=    copy.deepcopy(model.transformer.wte.weight   )
       UpperCAmelCase__							=    nn.Convad(self.embeddings.weight.size(1   )			,   256			,   3			,   padding=1   )
       UpperCAmelCase__							=    nn.Sequential(nn.Linear(256			,   32   )			,   nn.Dropout(p=0.1   )			,   nn.Linear(32			,   32   )			,   nn.Linear(32			,   1   )   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :List[Any]   )  ->      Tuple:
       UpperCAmelCase__							=    torch.max(self.conv(self.embeddings(_UpperCAmelCase   ).squeeze(1   ).transpose(1			,   2   )   )			,   2   )[0]
       UpperCAmelCase__							=    self.fc(_UpperCAmelCase   )
       return qs.squeeze(1   )







      @classmethod
      def 						UpperCAmelCase_    (      cls :Optional[int]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Any   )  ->      List[str]:
       UpperCAmelCase__							=    cls(_UpperCAmelCase   )  # this calls __init__
       UpperCAmelCase__							=    torch.load(_UpperCAmelCase   )
       secondary_learner.load_state_dict(_UpperCAmelCase   )
       UpperCAmelCase__							=    model.transformer.wte
       UpperCAmelCase__							=    copy.deepcopy(model.transformer.wte.weight   )
       return secondary_learner













import argparse
import os

import evaluate
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


########################################################################
# 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)
#
# 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
#
########################################################################


_UpperCAmelCase					:						Union[str, Any]         =     16
_UpperCAmelCase					:						Dict         =     32






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			List[Any]    ):
   # Initialize accelerator
   if args.with_tracking:
      lowerCamelCase									=Accelerator(
          cpu=args.cpu			,mixed_precision=args.mixed_precision			,log_with="""all"""			,project_dir=args.project_dir    )
   else:
      lowerCamelCase									=Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision    )

   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									=os.path.split(lowerCamelCase    )[-1].split("""."""    )[0]
      accelerator.init_trackers(lowerCamelCase			,lowerCamelCase    )

   lowerCamelCase									=AutoTokenizer.from_pretrained("""bert-base-cased"""    )
   lowerCamelCase									=load_dataset("""glue"""			,"""mrpc"""    )
   lowerCamelCase									=evaluate.load("""glue"""			,"""mrpc"""    )

   def tokenize_function(lowerCamelCase	:			int    ):
      # max_length=None => use the model max length (it's actually the default)
      lowerCamelCase									=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
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCamelCase									=datasets.map(
          lowerCamelCase			,batched=lowerCamelCase			,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(lowerCamelCase	:			Any    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCamelCase									=1_28 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(
          lowerCamelCase			,padding="""longest"""			,max_length=lowerCamelCase			,pad_to_multiple_of=lowerCamelCase			,return_tensors="""pt"""			,)

   # Instantiate dataloaders.
   lowerCamelCase									=DataLoader(
       tokenized_datasets["""train"""]			,shuffle=lowerCamelCase			,collate_fn=lowerCamelCase			,batch_size=lowerCamelCase    )
   lowerCamelCase									=DataLoader(
       tokenized_datasets["""validation"""]			,shuffle=lowerCamelCase			,collate_fn=lowerCamelCase			,batch_size=lowerCamelCase    )

   set_seed(lowerCamelCase    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase									=AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,return_dict=lowerCamelCase    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowerCamelCase									=model.to(accelerator.device    )

   # Instantiate optimizer
   lowerCamelCase									=AdamW(params=model.parameters()			,lr=lowerCamelCase    )

   # Instantiate scheduler
   lowerCamelCase									=get_linear_schedule_with_warmup(
       optimizer=lowerCamelCase			,num_warmup_steps=1_00			,num_training_steps=(len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps			,)

   # 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.
   lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=accelerator.prepare(
       lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )

   # We need to keep track of how many total steps we have iterated over
   lowerCamelCase									=0
   # We also need to keep track of the stating epoch so files are named properly
   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(lowerCamelCase    )[0]

      if "epoch" in training_difference:
         lowerCamelCase									=int(training_difference.replace("""epoch_"""			,""""""    )    ) + 1
         lowerCamelCase									=None
      else:
         lowerCamelCase									=int(training_difference.replace("""step_"""			,""""""    )    )
         lowerCamelCase									=resume_step // len(lowerCamelCase    )
         resume_step -= starting_epoch * len(lowerCamelCase    )

    # Now we train the model
   for epoch in range(lowerCamelCase			,lowerCamelCase    ):
      model.train()
      if args.with_tracking:
         lowerCamelCase									=0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         lowerCamelCase									=accelerator.skip_first_batches(lowerCamelCase			,lowerCamelCase    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowerCamelCase									=train_dataloader
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         lowerCamelCase									=model(**lowerCamelCase    )
         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(lowerCamelCase    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(lowerCamelCase			,lowerCamelCase    ):
            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			,lowerCamelCase    )
               accelerator.save_state(lowerCamelCase    )

      model.eval()
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         with torch.no_grad():
            lowerCamelCase									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.logits.argmax(dim=-1    )
         lowerCamelCase				,       lowerCamelCase									=accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
         metric.add_batch(
             predictions=lowerCamelCase			,references=lowerCamelCase			,)

      lowerCamelCase									=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,lowerCamelCase    )
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(lowerCamelCase    ),
                 """epoch""": epoch,
             }			,step=lowerCamelCase			,)

      if checkpointing_steps == "epoch":
         lowerCamelCase									=f'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
         accelerator.save_state(lowerCamelCase    )

   if args.with_tracking:
      accelerator.end_training()






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=argparse.ArgumentParser(description="""Simple example of training script."""    )
   parser.add_argument(
       """--mixed_precision"""			,type=lowerCamelCase			,default=lowerCamelCase			,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=lowerCamelCase			,default=lowerCamelCase			,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=lowerCamelCase			,default=lowerCamelCase			,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=lowerCamelCase			,default="""."""			,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,)
   parser.add_argument(
       """--project_dir"""			,type=lowerCamelCase			,default="""logs"""			,help="""Location on where to store experiment tracking logs` and relevent project information"""			,)
   lowerCamelCase									=parser.parse_args()
   lowerCamelCase									={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(lowerCamelCase			,lowerCamelCase    )


if __name__ == "__main__":
   main()