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_			(		snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							fname.split(os.path.sep    )[-1]
      return re.search(R"""^(.*)_\d+\.jpg$"""						,snake_case__    ).groups()[0]
  class 							lowerCamelCase		( UpperCamelCase_				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None ):
         UpperCamelCase				:   Optional[int]          =							file_names
         UpperCamelCase				:   int          =							image_transform
         UpperCamelCase				:   Optional[int]          =							label_to_id
  
  
  
  
  
  
  
     def __len__(							self ):
         return len(self.file_names )
  
  
  
  
  
  
  
     def __getitem__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							self.file_names[idx]
         UpperCamelCase				:   List[Any]          =							PIL.Image.open(__SCREAMING_SNAKE_CASE )
         UpperCamelCase				:   Tuple          =							raw_image.convert("""RGB""" )
         if self.image_transform is not None:
             UpperCamelCase				:   Any          =							self.image_transform(__SCREAMING_SNAKE_CASE )
         UpperCamelCase				:   Union[str, Any]          =							extract_label(__SCREAMING_SNAKE_CASE )
         if self.label_to_id is not None:
             UpperCamelCase				:   int          =							self.label_to_id[label]
         return {"image": image, "label": label}
  
  
  def 					A_			(		snake_case_     :       Dict						,snake_case_     :       Union[str, Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      # Initialize accelerator
      UpperCamelCase				:   str          =							Accelerator(cpu=args.cpu						,mixed_precision=args.mixed_precision    )
  
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase				:   Dict          =							config["""lr"""]
      UpperCamelCase				:   int          =							int(config["""num_epochs"""]    )
      UpperCamelCase				:   Dict          =							int(config["""seed"""]    )
      UpperCamelCase				:   int          =							int(config["""batch_size"""]    )
      UpperCamelCase				:   Union[str, Any]          =							config["""image_size"""]
      if not isinstance(snake_case__						,(list, tuple)    ):
          UpperCamelCase				:   Dict          =							(image_size, image_size)
  
      # Grab all the image filenames
      UpperCamelCase				:   Union[str, Any]          =							[os.path.join(args.data_dir						,snake_case__    ) for fname in os.listdir(args.data_dir    ) if fname.endswith(""".jpg"""    )]
  
      # Build the label correspondences
      UpperCamelCase				:   Optional[Any]          =							[extract_label(snake_case__    ) for fname in file_names]
      UpperCamelCase				:   Optional[Any]          =							list(set(snake_case__    )    )
      id_to_label.sort()
      UpperCamelCase				:   int          =							{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
      UpperCamelCase				:   List[str]          =							np.random.permutation(len(snake_case__    )    )
      UpperCamelCase				:   str          =							int(0.8 * len(snake_case__    )    )
      UpperCamelCase				:   int          =							random_perm[:cut]
      UpperCamelCase				:   Optional[int]          =							random_perm[cut:]
  
      # For training we use a simple RandomResizedCrop
      UpperCamelCase				:   Any          =							Compose([RandomResizedCrop(snake_case__						,scale=(0.5, 1.0)    ), ToTensor()]    )
      UpperCamelCase				:   Optional[int]          =							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
      UpperCamelCase				:   Optional[Any]          =							Compose([Resize(snake_case__    ), ToTensor()]    )
      UpperCamelCase				:   int          =							PetsDataset([file_names[i] for i in eval_split]						,image_transform=snake_case__						,label_to_id=snake_case__    )
  
      # Instantiate dataloaders.
      UpperCamelCase				:   Tuple          =							DataLoader(snake_case__						,shuffle=snake_case__						,batch_size=snake_case__						,num_workers=4    )
      UpperCamelCase				:   Optional[int]          =							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)
      UpperCamelCase				:   Union[str, Any]          =							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).
      UpperCamelCase				:   str          =							model.to(accelerator.device    )
  
      # Freezing the base model
      for param in model.parameters():
          UpperCamelCase				:   Optional[int]          =							False
      for param in model.get_classifier().parameters():
          UpperCamelCase				:   Any          =							True
  
      # We normalize the batches of images to be a bit faster.
      UpperCamelCase				:   List[str]          =							torch.tensor(model.default_cfg["""mean"""]    )[None, :, None, None].to(accelerator.device    )
      UpperCamelCase				:   str          =							torch.tensor(model.default_cfg["""std"""]    )[None, :, None, None].to(accelerator.device    )
  
      # Instantiate optimizer
      UpperCamelCase				:   Dict          =							torch.optim.Adam(params=model.parameters()						,lr=lr / 2_5    )
  
      # Instantiate learning rate scheduler
      UpperCamelCase				:   Union[str, Any]          =							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.
      UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							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`.
              UpperCamelCase				:   List[Any]          =							{k: v.to(accelerator.device    ) for k, v in batch.items()}
              UpperCamelCase				:   int          =							(batch["""image"""] - mean) / std
              UpperCamelCase				:   Union[str, Any]          =							model(snake_case__    )
              UpperCamelCase				:   List[Any]          =							torch.nn.functional.cross_entropy(snake_case__						,batch["""label"""]    )
              accelerator.backward(snake_case__    )
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
  
          model.eval()
          UpperCamelCase				:   int          =							0
          UpperCamelCase				:   int          =							0
          for _, batch in enumerate(snake_case__    ):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              UpperCamelCase				:   Dict          =							{k: v.to(accelerator.device    ) for k, v in batch.items()}
              UpperCamelCase				:   Any          =							(batch["""image"""] - mean) / std
              with torch.no_grad():
                  UpperCamelCase				:   Optional[int]          =							model(snake_case__    )
              UpperCamelCase				:   Optional[Any]          =							outputs.argmax(dim=-1    )
              UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							accelerator.gather_for_metrics((predictions, batch["""label"""])    )
              UpperCamelCase				:   Tuple          =							predictions == references
              num_elems += accurate_preds.shape[0]
              accurate += accurate_preds.long().sum()
  
          UpperCamelCase				:   Tuple          =							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 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							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."""    )
      UpperCamelCase				:   Optional[Any]          =							parser.parse_args()
      UpperCamelCase				:   int          =							{"""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: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  
  
  