code: 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      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
  							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
  								lowerCAmelCase				=	file_names
  								lowerCAmelCase				=	image_transform
  								lowerCAmelCase				=	label_to_id
  
  
  	def __len__(							self       )		->Optional[int]:
  								return len(self.file_names       )
  
  
  
  
  	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
  								lowerCAmelCase				=	self.file_names[idx]
  								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
  								if self.image_transform is not None:
  															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
  								if self.label_to_id is not None:
  															lowerCAmelCase				=	self.label_to_id[label]
  								return {"image": image, "label": label}
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
  							# Initialize accelerator
  							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)
  
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							lowerCAmelCase				=	config['''lr''']
  							lowerCAmelCase				=	int(config['''num_epochs''']							)
  							lowerCAmelCase				=	int(config['''seed''']							)
  							lowerCAmelCase				=	int(config['''batch_size''']							)
  							lowerCAmelCase				=	config['''image_size''']
  							if not isinstance(snake_case__     ,						(list, tuple)							):
  														lowerCAmelCase				=	(image_size, image_size)
  
  							# Grab all the image filenames
  							lowerCAmelCase				=	[os.path.join(args.data_dir     ,						snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith('''.jpg'''							)]
  
  							# Build the label correspondences
  							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
  							lowerCAmelCase				=	list(set(snake_case__							)							)
  							id_to_label.sort()
  							lowerCAmelCase				=	{lbl: i for i, lbl in enumerate(snake_case__							)}
  
  							# Set the seed before splitting the data.
  							np.random.seed(snake_case__							)
  							torch.manual_seed(snake_case__							)
  							torch.cuda.manual_seed_all(snake_case__							)
  
  							# Split our filenames between train and validation
  							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
  							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
  							lowerCAmelCase				=	random_perm[:cut]
  							lowerCAmelCase				=	random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							lowerCAmelCase				=	Compose([RandomResizedCrop(snake_case__     ,						scale=(0.5, 1.0)							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset(
  							    [file_names[i] for i in train_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# For evaluation, we use a deterministic Resize
  							lowerCAmelCase				=	Compose([Resize(snake_case__							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset([file_names[i] for i in eval_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# Instantiate dataloaders.
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  
  							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  							lowerCAmelCase				=	create_model('''resnet50d'''     ,						pretrained=snake_case__     ,						num_classes=len(snake_case__							)							)
  
  							# 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).
  							lowerCAmelCase				=	model.to(accelerator.device							)
  
  							# Freezing the base model
  							for param in model.parameters():
  														lowerCAmelCase				=	False
  							for param in model.get_classifier().parameters():
  														lowerCAmelCase				=	True
  
  							# We normalize the batches of images to be a bit faster.
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''mean''']							)[None, :, None, None].to(accelerator.device							)
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''std''']							)[None, :, None, None].to(accelerator.device							)
  
  							# Instantiate optimizer
  							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)
  
  							# Instantiate learning rate scheduler
  							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)
  
  							# 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.
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
  							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)
  
  							# Now we train the model
  							for epoch in range(snake_case__							):
  														model.train()
  														for step, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
  																					accelerator.backward(snake_case__							)
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														lowerCAmelCase				=	0
  														lowerCAmelCase				=	0
  														for _, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					with torch.no_grad():
  																												lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	outputs.argmax(dim=-1							)
  																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''label'''])							)
  																					lowerCAmelCase				=	predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														lowerCAmelCase				=	accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"							)
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
  							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
  							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
  							parser.add_argument(
  							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						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=snake_case__     ,						default=snake_case__     ,						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.'''							)
  							lowerCAmelCase				=	parser.parse_args()
  							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
  							training_function(snake_case__     ,						snake_case__							)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
style_context: from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  lowercase__    :       List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  @dataclass
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      torch.FloatTensor
  
  
  
  
  class      lowercase_						(						UpperCamelCase_   ,  UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Tuple     =       1
  
  
  	@register_to_config
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 40	,    __SCREAMING_SNAKE_CASE = 0.0_0_2	,    __SCREAMING_SNAKE_CASE = 8_0.0	,    __SCREAMING_SNAKE_CASE = 0.5	,    __SCREAMING_SNAKE_CASE = 1.0	,    __SCREAMING_SNAKE_CASE = 7.0	,    __SCREAMING_SNAKE_CASE = True	,    )		->Tuple:
  								# standard deviation of the initial noise distribution
  								lowerCAmelCase				=	sigma_max
  
  								lowerCAmelCase				=	np.linspace(0	,    1	,    __SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )
  
  								# setable values
  								lowerCAmelCase				=	None
  								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	False
  								lowerCAmelCase				=	False
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
  								if schedule_timesteps is None:
  															lowerCAmelCase				=	self.timesteps
  
  								lowerCAmelCase				=	(schedule_timesteps == timestep).nonzero()
  								return indices.item()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->torch.FloatTensor:
  								# Get sigma corresponding to timestep
  								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
  															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
  								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	self.sigmas[step_idx]
  
  								lowerCAmelCase				=	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
  								lowerCAmelCase				=	True
  								return sample
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								if not isinstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       ):
  															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.floataa       )
  
  								lowerCAmelCase				=	1000 * 0.2_5 * np.log(sigmas + 1e-44       )
  
  								return timesteps
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    )		->str:
  								if num_inference_steps is None and timesteps is None:
  															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )
  
  								if num_inference_steps is not None and timesteps is not None:
  															raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )
  
  								# Follow DDPMScheduler custom timesteps logic
  								if timesteps is not None:
  															for i in range(1	,    len(__SCREAMING_SNAKE_CASE       )       ):
  																						if timesteps[i] >= timesteps[i - 1]:
  																													raise ValueError('''`timesteps` must be in descending order.'''       )
  
  															if timesteps[0] >= self.config.num_train_timesteps:
  																						raise ValueError(
  																						    F"`timesteps` must start before `self.config.train_timesteps`:"
  																						    F" {self.config.num_train_timesteps}."       )
  
  															lowerCAmelCase				=	np.array(__SCREAMING_SNAKE_CASE	,    dtype=np.intaa       )
  															lowerCAmelCase				=	True
  								else:
  															if num_inference_steps > self.config.num_train_timesteps:
  																						raise ValueError(
  																						    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
  																						    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
  																						    F" maximal {self.config.num_train_timesteps} timesteps."       )
  
  															lowerCAmelCase				=	num_inference_steps
  
  															lowerCAmelCase				=	self.config.num_train_timesteps // self.num_inference_steps
  															lowerCAmelCase				=	(np.arange(0	,    __SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
  															lowerCAmelCase				=	False
  
  								# Map timesteps to Karras sigmas directly for multistep sampling
  								# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  								lowerCAmelCase				=	self.config.num_train_timesteps
  								lowerCAmelCase				=	timesteps[::-1].copy()
  								lowerCAmelCase				=	ramp / (num_train_timesteps - 1)
  								lowerCAmelCase				=	self._convert_to_karras(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	self.sigma_to_t(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
  								lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )
  
  								if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
  															# mps does not support float64
  															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE	,    dtype=torch.floataa       )
  								else:
  															lowerCAmelCase				=	torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.config.sigma_min
  								lowerCAmelCase				=	self.config.sigma_max
  
  								lowerCAmelCase				=	self.config.rho
  								lowerCAmelCase				=	sigma_min ** (1 / rho)
  								lowerCAmelCase				=	sigma_max ** (1 / rho)
  								lowerCAmelCase				=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  								return sigmas
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.config.sigma_data
  
  								lowerCAmelCase				=	sigma_data**2 / (sigma**2 + sigma_data**2)
  								lowerCAmelCase				=	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  								return c_skip, c_out
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
  								lowerCAmelCase				=	self.config.sigma_min
  								lowerCAmelCase				=	self.config.sigma_data
  
  								lowerCAmelCase				=	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  								lowerCAmelCase				=	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  								return c_skip, c_out
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    )		->Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
  								if (
  								    isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.IntTensor       )
  								    or isinstance(__SCREAMING_SNAKE_CASE	,    torch.LongTensor       )
  								):
  															raise ValueError(
  															    (
  															        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
  															        F" `{self.__class__}.step()` is not supported. Make sure to pass"
  															        ''' one of the `scheduler.timesteps` as a timestep.'''
  															    )	,    )
  
  								if not self.is_scale_input_called:
  															logger.warning(
  															    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
  															    '''See `StableDiffusionPipeline` for a usage example.'''       )
  
  								if isinstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       ):
  															lowerCAmelCase				=	timestep.to(self.timesteps.device       )
  
  								lowerCAmelCase				=	self.config.sigma_min
  								lowerCAmelCase				=	self.config.sigma_max
  
  								lowerCAmelCase				=	self.index_for_timestep(__SCREAMING_SNAKE_CASE       )
  
  								# sigma_next corresponds to next_t in original implementation
  								lowerCAmelCase				=	self.sigmas[step_index]
  								if step_index + 1 < self.config.num_train_timesteps:
  															lowerCAmelCase				=	self.sigmas[step_index + 1]
  								else:
  															# Set sigma_next to sigma_min
  															lowerCAmelCase				=	self.sigmas[-1]
  
  								# Get scalings for boundary conditions
  								lowerCAmelCase	,   lowerCAmelCase				=	self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE       )
  
  								# 1. Denoise model output using boundary conditions
  								lowerCAmelCase				=	c_out * model_output + c_skip * sample
  								if self.config.clip_denoised:
  															lowerCAmelCase				=	denoised.clamp(-1	,    1       )
  
  								# 2. Sample z ~ N(0, s_noise^2 * I)
  								# Noise is not used for onestep sampling.
  								if len(self.timesteps       ) > 1:
  															lowerCAmelCase				=	randn_tensor(
  															    model_output.shape	,    dtype=model_output.dtype	,    device=model_output.device	,    generator=__SCREAMING_SNAKE_CASE       )
  								else:
  															lowerCAmelCase				=	torch.zeros_like(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	noise * self.config.s_noise
  
  								lowerCAmelCase				=	sigma_next.clamp(min=__SCREAMING_SNAKE_CASE	,    max=__SCREAMING_SNAKE_CASE       )
  
  								# 3. Return noisy sample
  								# tau = sigma_hat, eps = sigma_min
  								lowerCAmelCase				=	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
  								if not return_dict:
  															return (prev_sample,)
  
  								return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    )		->torch.FloatTensor:
  								# Make sure sigmas and timesteps have the same device and dtype as original_samples
  								lowerCAmelCase				=	self.sigmas.to(device=original_samples.device	,    dtype=original_samples.dtype       )
  								if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE       ):
  															# mps does not support float64
  															lowerCAmelCase				=	self.timesteps.to(original_samples.device	,    dtype=torch.floataa       )
  															lowerCAmelCase				=	timesteps.to(original_samples.device	,    dtype=torch.floataa       )
  								else:
  															lowerCAmelCase				=	self.timesteps.to(original_samples.device       )
  															lowerCAmelCase				=	timesteps.to(original_samples.device       )
  
  								lowerCAmelCase				=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  								lowerCAmelCase				=	sigmas[step_indices].flatten()
  								while len(sigma.shape       ) < len(original_samples.shape       ):
  															lowerCAmelCase				=	sigma.unsqueeze(-1       )
  
  								lowerCAmelCase				=	original_samples + noise * sigma
  								return noisy_samples
  
  
  
  
  	def __len__(							self       )		->int:
  								return self.config.num_train_timesteps
  
  
  
  