





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


lowerCAmelCase_						   =    16
lowerCAmelCase_						   =    32


def     lowerCamelCase_	(      lowerCAmelCase:	Accelerator    ,							lowerCAmelCase:	int = 16    ,							lowerCAmelCase:	str = "bert-base-cased"				)->		Tuple:
  _snake_case   :				str														=	AutoTokenizer.from_pretrained(UpperCAmelCase__				)
  _snake_case   :				Optional[int]														=	load_dataset('glue'    ,							'mrpc'				)

  def tokenize_function(lowerCAmelCase:	str				):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case   :				Optional[int]														=	tokenizer(examples['sentence1']    ,							examples['sentence2']    ,							truncation=UpperCAmelCase__    ,							max_length=UpperCAmelCase__				)
    return outputs

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

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

  def collate_fn(lowerCAmelCase:	List[str]				):
    # 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(UpperCAmelCase__    ,							padding='max_length'    ,							max_length=1_28    ,							return_tensors='pt'				)
    return tokenizer.pad(UpperCAmelCase__    ,							padding='longest'    ,							return_tensors='pt'				)

  # Instantiate dataloaders.
  _snake_case   :				Optional[int]														=	DataLoader(
      tokenized_datasets['train']    ,							shuffle=UpperCAmelCase__    ,							collate_fn=UpperCAmelCase__    ,							batch_size=UpperCAmelCase__				)
  _snake_case   :				str														=	DataLoader(
      tokenized_datasets['validation']    ,							shuffle=UpperCAmelCase__    ,							collate_fn=UpperCAmelCase__    ,							batch_size=UpperCAmelCase__				)

  return train_dataloader, eval_dataloader


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	Dict				)->		Tuple:
  model.eval()
  _snake_case   :				List[str]														=	0
  for step, batch in enumerate(UpperCAmelCase__				):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device				)
    with torch.no_grad():
      _snake_case   :				Union[str, Any]														=	model(**UpperCAmelCase__				)
    _snake_case   :				int														=	outputs.logits.argmax(dim=-1				)
    # It is slightly faster to call this once, than multiple times
    _snake_case   :				List[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(UpperCAmelCase__				) - 1:
        _snake_case   :				Optional[Any]														=	predictions[: len(eval_dataloader.dataset				) - samples_seen]
        _snake_case   :				List[str]														=	references[: len(eval_dataloader.dataset				) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=UpperCAmelCase__    ,							references=UpperCAmelCase__    ,							)

  _snake_case   :				List[str]														=	metric.compute()
  return eval_metric["accuracy"]


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict				)->		List[str]:
  # Initialize accelerator
  _snake_case   :				Any														=	Accelerator()

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

  set_seed(UpperCAmelCase__				)
  _snake_case   :				Tuple														=	get_dataloaders(UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _snake_case   :				int														=	AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__    ,							return_dict=UpperCAmelCase__				)

  # Instantiate optimizer
  _snake_case   :				Optional[int]														=	(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _snake_case   :				str														=	optimizer_cls(params=model.parameters()    ,							lr=UpperCAmelCase__				)

  if accelerator.state.deepspeed_plugin is not None:
    _snake_case   :				Tuple														=	accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    _snake_case   :				List[Any]														=	1
  _snake_case   :				List[str]														=	(len(UpperCAmelCase__				) * num_epochs) // gradient_accumulation_steps

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

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

  if args.partial_train_epoch is not None:
    _snake_case   :				List[str]														=	args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint				)
    _snake_case   :				Optional[int]														=	args.resume_from_checkpoint.split('epoch_'				)[1]
    _snake_case   :				str														=	""""""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    _snake_case   :				Union[str, Any]														=	int(UpperCAmelCase__				) + 1
    _snake_case   :				Optional[Any]														=	evaluation_loop(UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__				)
    accelerator.print('resumed checkpoint performance:'    ,							UpperCAmelCase__				)
    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:
      _snake_case   :				Any														=	json.load(UpperCAmelCase__				)
      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
  _snake_case   :				Optional[int]														=	{}
  for epoch in range(UpperCAmelCase__    ,							UpperCAmelCase__				):
    model.train()
    for step, batch in enumerate(UpperCAmelCase__				):
      _snake_case   :				Union[str, Any]														=	model(**UpperCAmelCase__				)
      _snake_case   :				Dict														=	outputs.loss
      _snake_case   :				List[str]														=	loss / gradient_accumulation_steps
      accelerator.backward(UpperCAmelCase__				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    _snake_case   :				List[str]														=	F"""epoch_{epoch}"""
    _snake_case   :				Dict														=	os.path.join(args.output_dir    ,							UpperCAmelCase__				)
    accelerator.save_state(UpperCAmelCase__				)
    _snake_case   :				Dict														=	evaluation_loop(UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__    ,							UpperCAmelCase__				)
    _snake_case   :				Optional[int]														=	accuracy
    _snake_case   :				List[str]														=	lr_scheduler.get_lr()[0]
    _snake_case   :				List[str]														=	optimizer.param_groups[0]["""lr"""]
    _snake_case   :				List[str]														=	epoch
    _snake_case   :				List[str]														=	overall_step
    accelerator.print(F"""epoch {epoch}:"""    ,							UpperCAmelCase__				)

    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(UpperCAmelCase__    ,							UpperCAmelCase__				)


def     lowerCamelCase_	(      )->		str:
  _snake_case   :				Optional[int]														=	argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'				)
  parser.add_argument(
      '--model_name_or_path'    ,							type=UpperCAmelCase__    ,							default='bert-base-cased'    ,							help='Path to pretrained model or model identifier from huggingface.co/models.'    ,							required=UpperCAmelCase__    ,							)
  parser.add_argument(
      '--output_dir'    ,							type=UpperCAmelCase__    ,							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=UpperCAmelCase__    ,							default=UpperCAmelCase__    ,							help='If the training should continue from a checkpoint folder.'    ,							)
  parser.add_argument(
      '--partial_train_epoch'    ,							type=UpperCAmelCase__    ,							default=UpperCAmelCase__    ,							help='If passed, the training will stop after this number of epochs.'    ,							)
  parser.add_argument(
      '--num_epochs'    ,							type=UpperCAmelCase__    ,							default=2    ,							help='Number of train epochs.'    ,							)
  _snake_case   :				Dict														=	parser.parse_args()
  _snake_case   :				Tuple														=	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(UpperCAmelCase__    ,							UpperCAmelCase__				)


if __name__ == "__main__":
       main()











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[Union[str, Path]]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=True
  a_							:       Optional[int]      					=None
  a_							:       int      					=1
  a_							:       Optional[Union[str, bool]]      					=None
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return self.__class__(**{k: copy.deepcopy(UpperCamelCase							) for k, v in self.__dict__.items()}							)




