code: 
  '''simple docstring'''
  
  
  
  import argparse
  import os
  import re
  
  import numpy as np
  import PIL
  import torch
  from timm import create_model
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
  
  from accelerate import Accelerator
  
  
  
  
  
  def  _A		(				lowercase__	):
  						lowercase__							 =       fname.split(os.path.sep	)[-1]
  						return re.search(r"""^(.*)_\d+\.jpg$"""		,		lowercase__	).groups()[0]
  
  
  
  
  
  class 			A							(       A__				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       file_names
  									lowercase__							 =       image_transform
  									lowercase__							 =       label_to_id
  
  
  
  			def __len__(					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return len(self.file_names       )
  
  
  
  			def __getitem__(					self       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.file_names[idx]
  									lowercase__							 =       PIL.Image.open(_lowerCAmelCase       )
  									lowercase__							 =       raw_image.convert("""RGB"""       )
  									if self.image_transform is not None:
  															lowercase__							 =       self.image_transform(_lowerCAmelCase       )
  									lowercase__							 =       extract_label(_lowerCAmelCase       )
  									if self.label_to_id is not None:
  															lowercase__							 =       self.label_to_id[label]
  									return {"image": image, "label": label}
  
  
  
  
  
  def  _A		(				lowercase__		,		lowercase__	):
  						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"""]	)
  						lowercase__							 =       config["""image_size"""]
  						if not isinstance(lowercase__		,		(list, tuple)	):
  												lowercase__							 =       (image_size, image_size)
  
  						# Grab all the image filenames
  						lowercase__							 =       [os.path.join(args.data_dir		,		lowercase__	) for fname in os.listdir(args.data_dir	) if fname.endswith(""".jpg"""	)]
  
  						# Build the label correspondences
  						lowercase__							 =       [extract_label(lowercase__	) for fname in file_names]
  						lowercase__							 =       list(set(lowercase__	)	)
  						id_to_label.sort()
  						lowercase__							 =       {lbl: i for i, lbl in enumerate(lowercase__	)}
  
  						# Set the seed before splitting the data.
  						np.random.seed(lowercase__	)
  						torch.manual_seed(lowercase__	)
  						torch.cuda.manual_seed_all(lowercase__	)
  
  						# Split our filenames between train and validation
  						lowercase__							 =       np.random.permutation(len(lowercase__	)	)
  						lowercase__							 =       int(0.8 * len(lowercase__	)	)
  						lowercase__							 =       random_perm[:cut]
  						lowercase__							 =       random_perm[cut:]
  
  						# For training we use a simple RandomResizedCrop
  						lowercase__							 =       Compose([RandomResizedCrop(lowercase__		,		scale=(0.5, 1.0)	), ToTensor()]	)
  						lowercase__							 =       PetsDataset(
  						    [file_names[i] for i in train_split]		,		image_transform=lowercase__		,		label_to_id=lowercase__	)
  
  						# For evaluation, we use a deterministic Resize
  						lowercase__							 =       Compose([Resize(lowercase__	), ToTensor()]	)
  						lowercase__							 =       PetsDataset([file_names[i] for i in eval_split]		,		image_transform=lowercase__		,		label_to_id=lowercase__	)
  
  						# Instantiate dataloaders.
  						lowercase__							 =       DataLoader(lowercase__		,		shuffle=lowercase__		,		batch_size=lowercase__		,		num_workers=4	)
  						lowercase__							 =       DataLoader(lowercase__		,		shuffle=lowercase__		,		batch_size=lowercase__		,		num_workers=4	)
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						lowercase__							 =       create_model("""resnet50d"""		,		pretrained=lowercase__		,		num_classes=len(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	)
  
  						# Freezing the base model
  						for param in model.parameters():
  												lowercase__							 =       False
  						for param in model.get_classifier().parameters():
  												lowercase__							 =       True
  
  						# We normalize the batches of images to be a bit faster.
  						lowercase__							 =       torch.tensor(model.default_cfg["""mean"""]	)[None, :, None, None].to(accelerator.device	)
  						lowercase__							 =       torch.tensor(model.default_cfg["""std"""]	)[None, :, None, None].to(accelerator.device	)
  
  						# Instantiate optimizer
  						lowercase__							 =       torch.optim.Adam(params=model.parameters()		,		lr=lr / 25	)
  
  						# Instantiate learning rate scheduler
  						lowercase__							 =       OneCycleLR(optimizer=lowercase__		,		max_lr=lowercase__		,		epochs=lowercase__		,		steps_per_epoch=len(lowercase__	)	)
  
  						# 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__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       accelerator.prepare(
  						    lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	)
  
  						# Now we train the model
  						for epoch in range(lowercase__	):
  												model.train()
  												for step, batch in enumerate(lowercase__	):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		lowercase__							 =       {k: v.to(accelerator.device	) for k, v in batch.items()}
  																		lowercase__							 =       (batch["""image"""] - mean) / std
  																		lowercase__							 =       model(lowercase__	)
  																		lowercase__							 =       torch.nn.functional.cross_entropy(lowercase__		,		batch["""label"""]	)
  																		accelerator.backward(lowercase__	)
  																		optimizer.step()
  																		lr_scheduler.step()
  																		optimizer.zero_grad()
  
  												model.eval()
  												lowercase__							 =       0
  												lowercase__							 =       0
  												for _, batch in enumerate(lowercase__	):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		lowercase__							 =       {k: v.to(accelerator.device	) for k, v in batch.items()}
  																		lowercase__							 =       (batch["""image"""] - mean) / std
  																		with torch.no_grad():
  																								lowercase__							 =       model(lowercase__	)
  																		lowercase__							 =       outputs.argmax(dim=-1	)
  																		lowercase__ ,						lowercase__							 =       accelerator.gather_for_metrics((predictions, batch["""label"""])	)
  																		lowercase__							 =       predictions == references
  																		num_elems += accurate_preds.shape[0]
  																		accurate += accurate_preds.long().sum()
  
  												lowercase__							 =       accurate.item() / num_elems
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''	)
  
  
  
  
  
  def  _A		(				):
  						lowercase__							 =       argparse.ArgumentParser(description="""Simple example of training script."""	)
  						parser.add_argument("""--data_dir"""		,		required=lowercase__		,		help="""The data folder on disk."""	)
  						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(
  						    """--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("""--cpu"""		,		action="""store_true"""		,		help="""If passed, will train on the CPU."""	)
  						lowercase__							 =       parser.parse_args()
  						lowercase__							 =       {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  						training_function(lowercase__		,		lowercase__	)
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import timeit
  from typing import Callable, Optional
  
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_pyanvml_available, is_torch_available, logging
  from .benchmark_utils import (
      Benchmark,
      Memory,
      MemorySummary,
      measure_peak_memory_cpu,
      start_memory_tracing,
      stop_memory_tracing,
  )
  
  
  if is_torch_available():
  				import torch
  
  				from .benchmark_args import PyTorchBenchmarkArguments
  
  
  if is_pyanvml_available():
  				import pyanvml.pyanvml as nvml
  
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									if self.args.torchscript:
  															lowercase__							 =       True
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )
  
  									model.eval()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									if self.args.torchscript:
  															with torch.no_grad():
  																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       model
  
  									def encoder_decoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
  															return outputs
  
  									def encoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       )
  															return outputs
  
  									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )
  
  									if self.args.torchscript:
  															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
  									else:
  															lowercase__							 =       model
  
  									model.train()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									def compute_loss_and_backprob_encoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									lowercase__							 =       (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.is_tpu or self.args.torchscript:
  																					# run additional 10 times to stabilize compilation for tpu and torchscript
  																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
  																					timeit.repeat(
  																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )
  
  															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
  															lowercase__							 =       timeit.repeat(
  															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )
  
  															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
  																					import torch_xla.debug.metrics as met
  
  																					self.print_fn(met.metrics_report()       )
  
  															return min(lowerCamelCase__       ) / 10.0
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A"
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       start_memory_tracing("""transformers"""       )
  
  															if self.args.is_tpu:
  																					# tpu
  																					raise NotImplementedError(
  																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
  																					    """ `--no-memory` or `args.memory=False`"""       )
  															elif self.args.is_gpu:
  																					if not is_pyanvml_available():
  																											logger.warning(
  																											    """py3nvml not installed, we won't log GPU memory usage. """
  																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
  																											lowercase__							 =       """N/A"""
  																					else:
  																											logger.info(
  																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
  																											    """ on the same GPU."""       )
  																											# init nvml
  																											nvml.nvmlInit()
  																											func()
  																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
  																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
  																											lowercase__							 =       meminfo.used
  																											lowercase__							 =       Memory(lowerCamelCase__       )
  																											# shutdown nvml
  																											nvml.nvmlShutdown()
  															else:
  																					# cpu
  																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
  																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes
  
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
  															else:
  																					lowercase__							 =       None
  
  															return memory, summary
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A", None
  
  
  
  
  
  
  