import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     BigBirdConfig
  lowercase		:     jnp.dtype            =				jnp.floataa
  lowercase		:     bool            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Union[str, Any]:
         super().setup()
         A						:     List[Any]         =nn.Dense(5      ,	dtype=self.dtype     )







  def __call__(				self    :					Any      ,	*SCREAMING_SNAKE_CASE__    :					List[Any]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Dict:
         A						:     Dict         =super().__call__(*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =self.cls(outputs[2]     )
         return outputs[:2] + (cls_out,)







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				FlaxBigBirdForNaturalQuestionsModule







def A__  (			lowercase:     str,		lowercase:     Optional[int],		lowercase:     Tuple,		lowercase:     Dict,		lowercase:     Optional[Any],		lowercase:     Dict					)				->		int:
       def cross_entropy(lowercase:     Optional[int],		lowercase:     Dict,		lowercase:     List[str]=None					):
              A						:     Any         =logits.shape[-1]
              A						:     List[Any]         =(labels[..., None] == jnp.arange(lowercase					)[None]).astype('f4'					)
              A						:     Union[str, Any]         =jax.nn.log_softmax(lowercase,		axis=-1					)
              A						:     Union[str, Any]         =-jnp.sum(labels * logits,		axis=-1					)
              if reduction is not None:
                     A						:     List[Any]         =reduction(lowercase					)
              return loss

       A						:     Union[str, Any]         =partial(lowercase,		reduction=jnp.mean					)
       A						:     Any         =cross_entropy(lowercase,		lowercase					)
       A						:     List[str]         =cross_entropy(lowercase,		lowercase					)
       A						:     Union[str, Any]         =cross_entropy(lowercase,		lowercase					)
       return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     str            =				"google/bigbird-roberta-base"
  lowercase		:     int            =				3000
  lowercase		:     int            =				10500

  lowercase		:     int            =				128
  lowercase		:     int            =				3

  lowercase		:     int            =				1
  lowercase		:     int            =				5

  # tx_args
  lowercase		:     float            =				3e-5
  lowercase		:     float            =				0.0
  lowercase		:     int            =				20000
  lowercase		:     float            =				0.0_0_9_5

  lowercase		:     str            =				"bigbird-roberta-natural-questions"
  lowercase		:     str            =				"training-expt"
  lowercase		:     str            =				"data/nq-training.jsonl"
  lowercase		:     str            =				"data/nq-validation.jsonl"







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Dict:
         os.makedirs(self.base_dir      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =os.path.join(self.base_dir      ,	self.save_dir     )
         A						:     Optional[Any]         =self.batch_size_per_device * jax.device_count()







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     int
  lowercase		:     int            =				4096  # no dynamic padding on TPUs







  def __call__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Tuple:
         A						:     Dict         =self.collate_fn(SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =jax.tree_util.tree_map(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         return batch







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Dict:
         A						:     Optional[int]         =self.fetch_inputs(features['input_ids']     )
         A						:     Union[str, Any]         ={
             'input_ids': jnp.array(SCREAMING_SNAKE_CASE__      ,	dtype=jnp.intaa     ),
             'attention_mask': jnp.array(SCREAMING_SNAKE_CASE__      ,	dtype=jnp.intaa     ),
             'start_labels': jnp.array(features['start_token']      ,	dtype=jnp.intaa     ),
             'end_labels': jnp.array(features['end_token']      ,	dtype=jnp.intaa     ),
             'pooled_labels': jnp.array(features['category']      ,	dtype=jnp.intaa     ),
         }
         return batch







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					list     )    ->				Union[str, Any]:
         A						:     Tuple         =[self._fetch_inputs(SCREAMING_SNAKE_CASE__     ) for ids in input_ids]
         return zip(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					list     )    ->				Any:
         A						:     Any         =[1 for _ in range(len(SCREAMING_SNAKE_CASE__     )     )]
         while len(SCREAMING_SNAKE_CASE__     ) < self.max_length:
                input_ids.append(self.pad_id     )
                attention_mask.append(0     )
         return input_ids, attention_mask







def A__  (			lowercase:     Optional[Any],		lowercase:     List[Any],		lowercase:     Optional[Any]=None					)				->		Union[str, Any]:
       if seed is not None:
              A						:     Dict         =dataset.shuffle(seed=lowercase					)
       for i in range(len(lowercase					) // batch_size					):
              A						:     Optional[int]         =dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(lowercase					)



@partial(jax.pmap,		axis_name='batch'					)
def A__  (			lowercase:     str,		lowercase:     str,		**lowercase:     Any					)				->		Optional[int]:
       def loss_fn(lowercase:     Dict					):
              A						:     Union[str, Any]         =model_inputs.pop('start_labels'					)
              A						:     Optional[Any]         =model_inputs.pop('end_labels'					)
              A						:     Any         =model_inputs.pop('pooled_labels'					)

              A						:     List[Any]         =state.apply_fn(**lowercase,		params=lowercase,		dropout_rng=lowercase,		train=lowercase					)
              A						:     List[str]         =outputs

              return state.loss_fn(
                  lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		)

       A						:     Union[str, Any]         =jax.random.split(lowercase					)
       A						:     Dict         =jax.value_and_grad(lowercase					)
       A						:     str         =grad_fn(state.params					)
       A						:     Any         =jax.lax.pmean({'loss': loss},		axis_name='batch'					)
       A						:     Dict         =jax.lax.pmean(lowercase,		'batch'					)

       A						:     List[str]         =state.apply_gradients(grads=lowercase					)
       return state, metrics, new_drp_rng



@partial(jax.pmap,		axis_name='batch'					)
def A__  (			lowercase:     str,		**lowercase:     Tuple					)				->		List[str]:
       A						:     List[str]         =model_inputs.pop('start_labels'					)
       A						:     Union[str, Any]         =model_inputs.pop('end_labels'					)
       A						:     Union[str, Any]         =model_inputs.pop('pooled_labels'					)

       A						:     List[Any]         =state.apply_fn(**lowercase,		params=state.params,		train=lowercase					)
       A						:     List[Any]         =outputs

       A						:     Tuple         =state.loss_fn(lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)
       A						:     int         =jax.lax.pmean({'loss': loss},		axis_name='batch'					)
       return metrics



class  SCREAMING_SNAKE_CASE_ (					train_state.TrainState			):
  '''simple docstring'''


  lowercase		:     Callable            =				struct.field(pytree_node=lowerCAmelCase_			)







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     Args
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     wandb
  lowercase		:     Callable            =				None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int=None     )    ->				List[Any]:
         A						:     List[Any]         =model.params
         A						:     int         =TrainState.create(
             apply_fn=model.__call__      ,	params=SCREAMING_SNAKE_CASE__      ,	tx=SCREAMING_SNAKE_CASE__      ,	loss_fn=SCREAMING_SNAKE_CASE__      ,	)
         if ckpt_dir is not None:
                A						:     Dict         =restore_checkpoint(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                A						:     str         ={
                    'lr': args.lr,
                    'init_lr': args.init_lr,
                    'warmup_steps': args.warmup_steps,
                    'num_train_steps': num_train_steps,
                    'weight_decay': args.weight_decay,
                }
                A						:     Optional[int]         =build_tx(**SCREAMING_SNAKE_CASE__     )
                A						:     List[Any]         =train_state.TrainState(
                    step=SCREAMING_SNAKE_CASE__      ,	apply_fn=model.__call__      ,	params=SCREAMING_SNAKE_CASE__      ,	tx=SCREAMING_SNAKE_CASE__      ,	opt_state=SCREAMING_SNAKE_CASE__      ,	)
                A						:     int         =args
                A						:     List[str]         =data_collator
                A						:     List[str]         =lr
                A						:     Any         =params
         A						:     Any         =jax_utils.replicate(SCREAMING_SNAKE_CASE__     )
         return state







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[int]:
         A						:     Union[str, Any]         =self.args
         A						:     Optional[int]         =len(SCREAMING_SNAKE_CASE__     ) // args.batch_size

         A						:     Optional[int]         =jax.random.PRNGKey(0     )
         A						:     str         =jax.random.split(SCREAMING_SNAKE_CASE__      ,	jax.device_count()     )
         for epoch in range(args.max_epochs     ):
                A						:     Optional[Any]         =jnp.array(0      ,	dtype=jnp.floataa     )
                A						:     Union[str, Any]         =get_batched_dataset(SCREAMING_SNAKE_CASE__      ,	args.batch_size      ,	seed=SCREAMING_SNAKE_CASE__     )
                A						:     Any         =0
                for batch in tqdm(SCREAMING_SNAKE_CASE__      ,	total=SCREAMING_SNAKE_CASE__      ,	desc=f'Running EPOCH-{epoch}'     ):
                       A						:     Dict         =self.data_collator(SCREAMING_SNAKE_CASE__     )
                       A						:     Union[str, Any]         =self.train_step_fn(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       running_loss += jax_utils.unreplicate(metrics['loss']     )
                       i += 1
                       if i % args.logging_steps == 0:
                              A						:     Union[str, Any]         =jax_utils.unreplicate(state.step     )
                              A						:     int         =running_loss.item() / i
                              A						:     List[Any]         =self.scheduler_fn(state_step - 1     )

                              A						:     str         =self.evaluate(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                              A						:     Tuple         ={
                                  'step': state_step.item(),
                                  'eval_loss': eval_loss.item(),
                                  'tr_loss': tr_loss,
                                  'lr': lr.item(),
                              }
                              tqdm.write(str(SCREAMING_SNAKE_CASE__     )     )
                              self.logger.log(SCREAMING_SNAKE_CASE__      ,	commit=SCREAMING_SNAKE_CASE__     )

                       if i % args.save_steps == 0:
                              self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}'      ,	state=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Optional[Any]:
         A						:     List[Any]         =get_batched_dataset(SCREAMING_SNAKE_CASE__      ,	self.args.batch_size     )
         A						:     str         =len(SCREAMING_SNAKE_CASE__     ) // self.args.batch_size
         A						:     List[str]         =jnp.array(0      ,	dtype=jnp.floataa     )
         A						:     Optional[int]         =0
         for batch in tqdm(SCREAMING_SNAKE_CASE__      ,	total=SCREAMING_SNAKE_CASE__      ,	desc='Evaluating ... '     ):
                A						:     Dict         =self.data_collator(SCREAMING_SNAKE_CASE__     )
                A						:     Optional[Any]         =self.val_step_fn(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                running_loss += jax_utils.unreplicate(metrics['loss']     )
                i += 1
         return running_loss / i







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				str:
         A						:     List[Any]         =jax_utils.unreplicate(SCREAMING_SNAKE_CASE__     )
         print(f'SAVING CHECKPOINT IN {save_dir}'      ,	end=' ... '     )
         self.model_save_fn(SCREAMING_SNAKE_CASE__      ,	params=state.params     )
         with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	'opt_state.msgpack'     )      ,	'wb'     ) as f:
                f.write(to_bytes(state.opt_state     )     )
         joblib.dump(self.args      ,	os.path.join(SCREAMING_SNAKE_CASE__      ,	'args.joblib'     )     )
         joblib.dump(self.data_collator      ,	os.path.join(SCREAMING_SNAKE_CASE__      ,	'data_collator.joblib'     )     )
         with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	'training_state.json'     )      ,	'w'     ) as f:
                json.dump({'step': state.step.item()}      ,	SCREAMING_SNAKE_CASE__     )
         print('DONE'     )







def A__  (			lowercase:     Tuple,		lowercase:     Dict					)				->		Optional[Any]:
       print(F'RESTORING CHECKPOINT FROM {save_dir}',		end=' ... '					)
       with open(os.path.join(lowercase,		'flax_model.msgpack'					),		'rb'					) as f:
              A						:     Tuple         =from_bytes(state.params,		f.read()					)

       with open(os.path.join(lowercase,		'opt_state.msgpack'					),		'rb'					) as f:
              A						:     List[str]         =from_bytes(state.opt_state,		f.read()					)

       A						:     Any         =joblib.load(os.path.join(lowercase,		'args.joblib'					)					)
       A						:     Any         =joblib.load(os.path.join(lowercase,		'data_collator.joblib'					)					)

       with open(os.path.join(lowercase,		'training_state.json'					),		'r'					) as f:
              A						:     List[str]         =json.load(lowercase					)
       A						:     int         =training_state['step']

       print('DONE'					)
       return params, opt_state, step, args, data_collator



def A__  (			lowercase:     Dict,		lowercase:     List[Any],		lowercase:     List[str],		lowercase:     List[str]					)				->		int:
       A						:     str         =num_train_steps - warmup_steps
       A						:     Any         =optax.linear_schedule(init_value=lowercase,		end_value=lowercase,		transition_steps=lowercase					)
       A						:     str         =optax.linear_schedule(init_value=lowercase,		end_value=1e-7,		transition_steps=lowercase					)
       A						:     int         =optax.join_schedules(schedules=[warmup_fn, decay_fn],		boundaries=[warmup_steps]					)
       return lr



def A__  (			lowercase:     Union[str, Any],		lowercase:     Union[str, Any],		lowercase:     Tuple,		lowercase:     Union[str, Any],		lowercase:     List[str]					)				->		Union[str, Any]:
       def weight_decay_mask(lowercase:     List[Any]					):
              A						:     Union[str, Any]         =traverse_util.flatten_dict(lowercase					)
              A						:     List[str]         ={k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
              return traverse_util.unflatten_dict(lowercase					)

       A						:     Dict         =scheduler_fn(lowercase,		lowercase,		lowercase,		lowercase					)

       A						:     List[str]         =optax.adamw(learning_rate=lowercase,		weight_decay=lowercase,		mask=lowercase					)
       return tx, lr







import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

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

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # 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(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       for step, batch in enumerate(lowercase					):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device					)
              with torch.no_grad():
                     A						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

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

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		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.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     Tuple         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     List[str]         =0
       A						:     Tuple         =evaluate.load('glue',		'mrpc'					)
       A						:     Optional[int]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     Union[str, Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






