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$"""		,		__a	).groups()[0]
  
  
  
  
  
  class 			A							(       UpperCamelCase__				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       file_names
  									lowercase__							 =       image_transform
  									lowercase__							 =       label_to_id
  
  
  
  			def __len__(					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return len(self.file_names       )
  
  
  
  			def __getitem__(					self       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.file_names[idx]
  									lowercase__							 =       PIL.Image.open(snake_case_       )
  									lowercase__							 =       raw_image.convert("""RGB"""       )
  									if self.image_transform is not None:
  															lowercase__							 =       self.image_transform(snake_case_       )
  									lowercase__							 =       extract_label(snake_case_       )
  									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(__a		,		(list, tuple)	):
  												lowercase__							 =       (image_size, image_size)
  
  						# Grab all the image filenames
  						lowercase__							 =       [os.path.join(args.data_dir		,		__a	) for fname in os.listdir(args.data_dir	) if fname.endswith(""".jpg"""	)]
  
  						# Build the label correspondences
  						lowercase__							 =       [extract_label(__a	) for fname in file_names]
  						lowercase__							 =       list(set(__a	)	)
  						id_to_label.sort()
  						lowercase__							 =       {lbl: i for i, lbl in enumerate(__a	)}
  
  						# Set the seed before splitting the data.
  						np.random.seed(__a	)
  						torch.manual_seed(__a	)
  						torch.cuda.manual_seed_all(__a	)
  
  						# Split our filenames between train and validation
  						lowercase__							 =       np.random.permutation(len(__a	)	)
  						lowercase__							 =       int(0.8 * len(__a	)	)
  						lowercase__							 =       random_perm[:cut]
  						lowercase__							 =       random_perm[cut:]
  
  						# For training we use a simple RandomResizedCrop
  						lowercase__							 =       Compose([RandomResizedCrop(__a		,		scale=(0.5, 1.0)	), ToTensor()]	)
  						lowercase__							 =       PetsDataset(
  						    [file_names[i] for i in train_split]		,		image_transform=__a		,		label_to_id=__a	)
  
  						# For evaluation, we use a deterministic Resize
  						lowercase__							 =       Compose([Resize(__a	), ToTensor()]	)
  						lowercase__							 =       PetsDataset([file_names[i] for i in eval_split]		,		image_transform=__a		,		label_to_id=__a	)
  
  						# Instantiate dataloaders.
  						lowercase__							 =       DataLoader(__a		,		shuffle=__a		,		batch_size=__a		,		num_workers=4	)
  						lowercase__							 =       DataLoader(__a		,		shuffle=__a		,		batch_size=__a		,		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=__a		,		num_classes=len(__a	)	)
  
  						# 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=__a		,		max_lr=__a		,		epochs=__a		,		steps_per_epoch=len(__a	)	)
  
  						# 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(
  						    __a		,		__a		,		__a		,		__a		,		__a	)
  
  						# Now we train the model
  						for epoch in range(__a	):
  												model.train()
  												for step, batch in enumerate(__a	):
  																		# 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(__a	)
  																		lowercase__							 =       torch.nn.functional.cross_entropy(__a		,		batch["""label"""]	)
  																		accelerator.backward(__a	)
  																		optimizer.step()
  																		lr_scheduler.step()
  																		optimizer.zero_grad()
  
  												model.eval()
  												lowercase__							 =       0
  												lowercase__							 =       0
  												for _, batch in enumerate(__a	):
  																		# 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(__a	)
  																		lowercase__							 =       outputs.argmax(dim=-1	)
  																		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=__a		,		help="""The data folder on disk."""	)
  						parser.add_argument(
  						    """--mixed_precision"""		,		type=__a		,		default=__a		,		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=__a		,		default=__a		,		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(__a		,		__a	)
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       copy.deepcopy(lowercase__	)
  						lowercase__							 =       sorted(lowercase__	)
  						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
  						return quantized
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
  						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
  						# count number of elements per group
  						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
  						lowercase__							 =       [0] + bins + [np.inf]
  						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
  						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
  						return groups
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
  									lowercase__							 =       sampler
  									lowercase__							 =       group_ids
  									lowercase__							 =       batch_size
  
  
  
  			def __iter__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  
  									lowercase__							 =       0
  									for idx in self.sampler:
  															lowercase__							 =       self.group_ids[idx]
  															buffer_per_group[group_id].append(lowerCamelCase__       )
  															samples_per_group[group_id].append(lowerCamelCase__       )
  															if len(buffer_per_group[group_id]       ) == self.batch_size:
  																					yield buffer_per_group[group_id]  # TODO
  																					num_batches += 1
  																					del buffer_per_group[group_id]
  															assert len(buffer_per_group[group_id]       ) < self.batch_size
  
  									# now we have run out of elements that satisfy
  									# the group criteria, let's return the remaining
  									# elements so that the size of the sampler is
  									# deterministic
  									lowercase__							 =       len(self       )
  									lowercase__							 =       expected_num_batches - num_batches
  									if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															lowercase__							 =       []
  															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
  																					batch_idx.extend(lowerCamelCase__       )
  																					if len(lowerCamelCase__       ) >= self.batch_size:
  																											yield batch_idx[: self.batch_size]
  																											lowercase__							 =       batch_idx[self.batch_size :]
  																											num_remaining -= 1
  															if len(lowerCamelCase__       ) > 0:
  																					yield batch_idx
  																					num_remaining -= 1
  									assert num_remaining == 0
  
  
  
  			def __len__(					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  