code: 
  
  
  
  
  """simple docstring"""
  
  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
  #
  ########################################################################
  
  __A 		=			16
  __A 		=			32
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     Dict = 16   ):
  		snake_case		:						str								=				AutoTokenizer.from_pretrained("bert-base-cased"   )
  		snake_case		:						str								=				load_dataset("glue"       ,     "mrpc"   )
  
  		def tokenize_function(lowercase__  :     List[str]   ):
  				# max_length=None => use the model max length (it's actually the default)
  				snake_case		:						Optional[Any]								=				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():
  				snake_case		:						List[str]								=				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
  		snake_case		:						Dict								=				tokenized_datasets.rename_column("label"       ,     "labels"   )
  
  		def collate_fn(lowercase__  :     Optional[Any]   ):
  				# On TPU it's best to pad everything to the same length or training will be very slow.
  				snake_case		:						Union[str, Any]								=				128 if accelerator.distributed_type == DistributedType.TPU else None
  				# When using mixed precision we want round multiples of 8/16
  				if accelerator.mixed_precision == "fp8":
  						snake_case		:						int								=				16
  				elif accelerator.mixed_precision != "no":
  						snake_case		:						Dict								=				8
  				else:
  						snake_case		:						str								=				None
  
  				return tokenizer.pad(
  				    lowercase__       ,     padding="longest"       ,     max_length=lowercase__       ,     pad_to_multiple_of=lowercase__       ,     return_tensors="pt"       ,     )
  
  		# Instantiate dataloaders.
  		snake_case		:						Any								=				DataLoader(
  		    tokenized_datasets["train"]       ,     shuffle=lowercase__       ,     collate_fn=lowercase__       ,     batch_size=lowercase__   )
  		snake_case		:						Any								=				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
  
  							__A 		=			mocked_dataloaders  # noqa: F811
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Any       ,     lowercase__  :     Dict   ):
  		# For testing only
  		if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,     lowercase__   ) == "1":
  				snake_case		:						Tuple								=				2
  		# Initialize accelerator
  		snake_case		:						int								=				Accelerator(cpu=args.cpu       ,     mixed_precision=args.mixed_precision   )
  		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  		snake_case		:						str								=				config["lr"]
  		snake_case		:						List[Any]								=				int(config["num_epochs"]   )
  		snake_case		:						Tuple								=				int(config["seed"]   )
  		snake_case		:						List[Any]								=				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":
  						snake_case		:						Optional[int]								=				args.checkpointing_steps
  				elif args.checkpointing_steps.isdigit():
  						snake_case		:						Union[str, Any]								=				int(args.checkpointing_steps   )
  				else:
  						raise ValueError(
  						    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''   )
  		else:
  				snake_case		:						List[Any]								=				None
  
  		set_seed(lowercase__   )
  
  		snake_case		:						Optional[int]								=				get_dataloaders(lowercase__       ,     lowercase__   )
  		snake_case		:						str								=				evaluate.load("glue"       ,     "mrpc"   )
  
  		# If the batch size is too big we use gradient accumulation
  		snake_case		:						List[str]								=				1
  		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  				snake_case		:						Optional[int]								=				batch_size // MAX_GPU_BATCH_SIZE
  				snake_case		:						Optional[Any]								=				MAX_GPU_BATCH_SIZE
  
  		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  		snake_case		:						List[str]								=				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).
  		snake_case		:						Optional[int]								=				model.to(accelerator.device   )
  
  		# Instantiate optimizer
  		snake_case		:						Optional[Any]								=				AdamW(params=model.parameters()       ,     lr=lowercase__   )
  
  		# Instantiate scheduler
  		snake_case		:						List[Any]								=				get_linear_schedule_with_warmup(
  		    optimizer=lowercase__       ,     num_warmup_steps=100       ,     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.
  		snake_case		:						int								=				accelerator.prepare(
  		    lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__   )
  
  		# New Code #
  		# We need to keep track of how many total steps we have iterated over
  		snake_case		:						int								=				0
  		# We also need to keep track of the stating epoch so files are named properly
  		snake_case		:						Optional[int]								=				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   )
  						snake_case		:						Any								=				os.path.basename(args.resume_from_checkpoint   )
  				else:
  						# Get the most recent checkpoint
  						snake_case		:						List[Any]								=				[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
  						dirs.sort(key=os.path.getctime   )
  						snake_case		:						int								=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  				# Extract `epoch_{i}` or `step_{i}`
  				snake_case		:						Optional[Any]								=				os.path.splitext(lowercase__   )[0]
  
  				if "epoch" in training_difference:
  						snake_case		:						List[Any]								=				int(training_difference.replace("epoch_"       ,     ""   )   ) + 1
  						snake_case		:						Optional[Any]								=				None
  				else:
  						snake_case		:						Tuple								=				int(training_difference.replace("step_"       ,     ""   )   )
  						snake_case		:						Tuple								=				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
  						snake_case		:						Optional[int]								=				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
  						snake_case		:						List[Any]								=				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   )
  						snake_case		:						Any								=				model(**lowercase__   )
  						snake_case		:						int								=				outputs.loss
  						snake_case		:						Optional[Any]								=				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__   ):
  								snake_case		:						int								=				F'''step_{overall_step}'''
  								if overall_step % checkpointing_steps == 0:
  										if args.output_dir is not None:
  												snake_case		:						str								=				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():
  								snake_case		:						Any								=				model(**lowercase__   )
  						snake_case		:						List[str]								=				outputs.logits.argmax(dim=-1   )
  						snake_case		:						str								=				accelerator.gather_for_metrics((predictions, batch["labels"])   )
  						metric.add_batch(
  						    predictions=lowercase__       ,     references=lowercase__       ,     )
  
  				snake_case		:						Tuple								=				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":
  						snake_case		:						List[str]								=				F'''epoch_{epoch}'''
  						if args.output_dir is not None:
  								snake_case		:						int								=				os.path.join(args.output_dir       ,     lowercase__   )
  						accelerator.save_state(lowercase__   )
  
  
  
  def   UpperCamelCase__     (		):
  		snake_case		:						List[str]								=				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."       ,     )
  		snake_case		:						Dict								=				parser.parse_args()
  		snake_case		:						str								=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  		training_function(lowercase__       ,     lowercase__   )
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				1
  					snake_case		:						Dict								=				3
  					snake_case		:						int								=				8
  					snake_case		:						int								=				8
  
  					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)
  
  					return sample
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if isinstance(image[0]				, PIL.Image.Image							):
  							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
  					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
  							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					return image
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.dummy_sample
  					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Optional[Any]								=				image_processor.postprocess(
  							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
  							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
  									assert (
  									    np.abs(in_np - out_np							).max() < 1E-6
  									), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				self.dummy_sample
  					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
  					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Union[str, Any]								=				self.dummy_sample
  					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
  					list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  