code: 
  
  
  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,
  # specifically showcasing the checkpointing capability,
  # and builds off the `nlp_example.py` script.
  #
  # 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)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # 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
  #
  ########################################################################
  
  lowerCAmelCase					 =	1_6
  lowerCAmelCase					 =	3_2
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__ = 1_6			)		->			List[Any]:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    AutoTokenizer.from_pretrained('bert-base-cased'			)
  						__lowercase=    load_dataset('glue'     ,				'mrpc'			)
  
  						def tokenize_function(lowercase__			):
  												# max_length=None => use the model max length (it's actually the default)
  												__lowercase=    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
  						# starting with the main process first:
  						with accelerator.main_process_first():
  												__lowercase=    datasets.map(
  												    lowercase__     ,				batched=lowercase__     ,				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
  						__lowercase=    tokenized_datasets.rename_column('label'     ,				'labels'			)
  
  						def collate_fn(lowercase__			):
  												# On TPU it's best to pad everything to the same length or training will be very slow.
  												__lowercase=    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":
  																		__lowercase=    1_6
  												elif accelerator.mixed_precision != "no":
  																		__lowercase=    8
  												else:
  																		__lowercase=    None
  
  												return tokenizer.pad(
  												    lowercase__     ,				padding='longest'     ,				max_length=lowercase__     ,				pad_to_multiple_of=lowercase__     ,				return_tensors='pt'     ,				)
  
  						# Instantiate dataloaders.
  						__lowercase=    DataLoader(
  						    tokenized_datasets['train']     ,				shuffle=lowercase__     ,				collate_fn=lowercase__     ,				batch_size=lowercase__			)
  						__lowercase=    DataLoader(
  						    tokenized_datasets['validation']     ,				shuffle=lowercase__     ,				collate_fn=lowercase__     ,				batch_size=lowercase__			)
  
  						return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  	from accelerate.test_utils.training import mocked_dataloaders
  
  	lowerCAmelCase					 =	mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Optional[int]:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,				lowercase__			) == "1":
  												__lowercase=    2
  						# Initialize accelerator
  						__lowercase=    Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision			)
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						__lowercase=    config["lr"]
  						__lowercase=    int(config['num_epochs']			)
  						__lowercase=    int(config['seed']			)
  						__lowercase=    int(config['batch_size']			)
  
  						# New Code #
  						# Parse out whether we are saving every epoch or after a certain number of batches
  						if hasattr(args.checkpointing_steps     ,				'isdigit'			):
  												if args.checkpointing_steps == "epoch":
  																		__lowercase=    args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		__lowercase=    int(args.checkpointing_steps			)
  												else:
  																		raise ValueError(
  																		    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
  						else:
  												__lowercase=    None
  
  						set_seed(lowercase__			)
  
  						__lowercase=    get_dataloaders(lowercase__     ,				lowercase__			)
  						__lowercase=    evaluate.load('glue'     ,				'mrpc'			)
  
  						# If the batch size is too big we use gradient accumulation
  						__lowercase=    1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												__lowercase=    batch_size // MAX_GPU_BATCH_SIZE
  												__lowercase=    MAX_GPU_BATCH_SIZE
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						__lowercase=    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,				return_dict=lowercase__			)
  
  						# 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).
  						__lowercase=    model.to(accelerator.device			)
  
  						# Instantiate optimizer
  						__lowercase=    AdamW(params=model.parameters()     ,				lr=lowercase__			)
  
  						# Instantiate scheduler
  						__lowercase=    get_linear_schedule_with_warmup(
  						    optimizer=lowercase__     ,				num_warmup_steps=1_0_0     ,				num_training_steps=(len(lowercase__			) * 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.
  						__lowercase=    accelerator.prepare(
  						    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)
  
  						# New Code #
  						# We need to keep track of how many total steps we have iterated over
  						__lowercase=    0
  						# We also need to keep track of the stating epoch so files are named properly
  						__lowercase=    0
  
  						# We need to load the checkpoint back in before training here with `load_state`
  						# The total number of epochs is adjusted based on where the state is being loaded from,
  						# as we assume continuation of the same training script
  						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			)
  																		__lowercase=    os.path.basename(args.resume_from_checkpoint			)
  												else:
  																		# Get the most recent checkpoint
  																		__lowercase=    [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime			)
  																		__lowercase=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												__lowercase=    os.path.splitext(lowercase__			)[0]
  
  												if "epoch" in training_difference:
  																		__lowercase=    int(training_difference.replace('epoch_'     ,				''			)			) + 1
  																		__lowercase=    None
  												else:
  																		__lowercase=    int(training_difference.replace('step_'     ,				''			)			)
  																		__lowercase=    resume_step // len(lowercase__			)
  																		resume_step -= starting_epoch * len(lowercase__			)
  
      # Now we train the model
  						for epoch in range(lowercase__     ,				lowercase__			):
  												model.train()
  												# New Code #
  												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
  																		__lowercase=    accelerator.skip_first_batches(lowercase__     ,				lowercase__			)
  																		overall_step += resume_step
  												else:
  																		# After the first iteration though, we need to go back to the original dataloader
  																		__lowercase=    train_dataloader
  												for step, batch in enumerate(lowercase__			):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device			)
  																		__lowercase=    model(**lowercase__			)
  																		__lowercase=    outputs.loss
  																		__lowercase=    loss / gradient_accumulation_steps
  																		accelerator.backward(lowercase__			)
  																		if step % gradient_accumulation_steps == 0:
  																								optimizer.step()
  																								lr_scheduler.step()
  																								optimizer.zero_grad()
  																		# New Code #
  																		overall_step += 1
  
  																		# New Code #
  																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  																		# These are saved to folders named `step_{overall_step}`
  																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  																		# If mixed precision was used, will also save a "scalar.bin" file
  																		if isinstance(lowercase__     ,				lowercase__			):
  																								__lowercase=    F'step_{overall_step}'
  																								if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																				__lowercase=    os.path.join(args.output_dir     ,				lowercase__			)
  																														accelerator.save_state(lowercase__			)
  
  												model.eval()
  												for step, batch in enumerate(lowercase__			):
  																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
  																		batch.to(accelerator.device			)
  																		with torch.no_grad():
  																								__lowercase=    model(**lowercase__			)
  																		__lowercase=    outputs.logits.argmax(dim=-1			)
  																		__lowercase=    accelerator.gather_for_metrics((predictions, batch['labels'])			)
  																		metric.add_batch(
  																		    predictions=lowercase__     ,				references=lowercase__     ,				)
  
  												__lowercase=    metric.compute()
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(F'epoch {epoch}:'     ,				lowercase__			)
  
  												# New Code #
  												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  												# These are saved to folders named `epoch_{epoch}`
  												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  												# If mixed precision was used, will also save a "scalar.bin" file
  												if checkpointing_steps == "epoch":
  																		__lowercase=    F'epoch_{epoch}'
  																		if args.output_dir is not None:
  																								__lowercase=    os.path.join(args.output_dir     ,				lowercase__			)
  																		accelerator.save_state(lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Tuple:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    argparse.ArgumentParser(description='Simple example of training script.'			)
  						parser.add_argument(
  						    '--mixed_precision'     ,				type=lowercase__     ,				default=lowercase__     ,				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=lowercase__     ,				default=lowercase__     ,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'     ,				)
  						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.'     ,				)
  						__lowercase=    parser.parse_args()
  						__lowercase=    {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
  						training_function(lowercase__     ,				lowercase__			)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  