




'''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 			_UpperCamelCase (_lowerCamelCase							:     Optional[Any]	)->   int:




     '''simple docstring'''





     __snake_case    					=  fname.split(os.path.sep	)[-1]
     return re.search(R'''^(.*)_\d+\.jpg$'''   ,	_lowerCamelCase	).groups()[0]





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=None  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  file_names
            __snake_case    					=  image_transform
            __snake_case    					=  label_to_id


       def __len__(   self  )      ->  str:



            '''simple docstring'''




            return len(self.file_names  )




       def __getitem__(   self    ,  __SCREAMING_SNAKE_CASE  )      ->  Dict:



            '''simple docstring'''




            __snake_case    					=  self.file_names[idx]
            __snake_case    					=  PIL.Image.open(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  raw_image.convert('''RGB'''  )
            if self.image_transform is not None:
                 __snake_case    					=  self.image_transform(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  extract_label(__SCREAMING_SNAKE_CASE  )
            if self.label_to_id is not None:
                 __snake_case    					=  self.label_to_id[label]
            return {"image": image, "label": label}







def 			_UpperCamelCase (_lowerCamelCase							:     Optional[int]   ,	_lowerCamelCase							:     str	)->   Optional[Any]:




     '''simple docstring'''





     __snake_case    					=  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    					=  config['''lr''']
     __snake_case    					=  int(config['''num_epochs''']	)
     __snake_case    					=  int(config['''seed''']	)
     __snake_case    					=  int(config['''batch_size''']	)
     __snake_case    					=  config['''image_size''']
     if not isinstance(_lowerCamelCase   ,	(list, tuple)	):
          __snake_case    					=  (image_size, image_size)

     # Grab all the image filenames
     __snake_case    					=  [os.path.join(args.data_dir   ,	_lowerCamelCase	) for fname in os.listdir(args.data_dir	) if fname.endswith('''.jpg'''	)]

     # Build the label correspondences
     __snake_case    					=  [extract_label(_lowerCamelCase	) for fname in file_names]
     __snake_case    					=  list(set(_lowerCamelCase	)	)
     id_to_label.sort()
     __snake_case    					=  {lbl: i for i, lbl in enumerate(_lowerCamelCase	)}

     # Set the seed before splitting the data.
     np.random.seed(_lowerCamelCase	)
     torch.manual_seed(_lowerCamelCase	)
     torch.cuda.manual_seed_all(_lowerCamelCase	)

     # Split our filenames between train and validation
     __snake_case    					=  np.random.permutation(len(_lowerCamelCase	)	)
     __snake_case    					=  int(0.8 * len(_lowerCamelCase	)	)
     __snake_case    					=  random_perm[:cut]
     __snake_case    					=  random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     __snake_case    					=  Compose([RandomResizedCrop(_lowerCamelCase   ,	scale=(0.5, 1.0)	), ToTensor()]	)
     __snake_case    					=  PetsDataset(
         [file_names[i] for i in train_split]   ,	image_transform=_lowerCamelCase   ,	label_to_id=_lowerCamelCase	)

     # For evaluation, we use a deterministic Resize
     __snake_case    					=  Compose([Resize(_lowerCamelCase	), ToTensor()]	)
     __snake_case    					=  PetsDataset([file_names[i] for i in eval_split]   ,	image_transform=_lowerCamelCase   ,	label_to_id=_lowerCamelCase	)

     # Instantiate dataloaders.
     __snake_case    					=  DataLoader(_lowerCamelCase   ,	shuffle=_lowerCamelCase   ,	batch_size=_lowerCamelCase   ,	num_workers=4	)
     __snake_case    					=  DataLoader(_lowerCamelCase   ,	shuffle=_lowerCamelCase   ,	batch_size=_lowerCamelCase   ,	num_workers=4	)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __snake_case    					=  create_model('''resnet50d'''   ,	pretrained=_lowerCamelCase   ,	num_classes=len(_lowerCamelCase	)	)

     # 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    					=  model.to(accelerator.device	)

     # Freezing the base model
     for param in model.parameters():
          __snake_case    					=  False
     for param in model.get_classifier().parameters():
          __snake_case    					=  True

     # We normalize the batches of images to be a bit faster.
     __snake_case    					=  torch.tensor(model.default_cfg['''mean''']	)[None, :, None, None].to(accelerator.device	)
     __snake_case    					=  torch.tensor(model.default_cfg['''std''']	)[None, :, None, None].to(accelerator.device	)

     # Instantiate optimizer
     __snake_case    					=  torch.optim.Adam(params=model.parameters()   ,	lr=lr / 25	)

     # Instantiate learning rate scheduler
     __snake_case    					=  OneCycleLR(optimizer=_lowerCamelCase   ,	max_lr=_lowerCamelCase   ,	epochs=_lowerCamelCase   ,	steps_per_epoch=len(_lowerCamelCase	)	)

     # 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					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case    					=  accelerator.prepare(
         _lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase	)

     # Now we train the model
     for epoch in range(_lowerCamelCase	):
          model.train()
          for step, batch in enumerate(_lowerCamelCase	):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __snake_case    					=  {k: v.to(accelerator.device	) for k, v in batch.items()}
               __snake_case    					=  (batch['''image'''] - mean) / std
               __snake_case    					=  model(_lowerCamelCase	)
               __snake_case    					=  torch.nn.functional.cross_entropy(_lowerCamelCase   ,	batch['''label''']	)
               accelerator.backward(_lowerCamelCase	)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

          model.eval()
          __snake_case    					=  0
          __snake_case    					=  0
          for _, batch in enumerate(_lowerCamelCase	):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __snake_case    					=  {k: v.to(accelerator.device	) for k, v in batch.items()}
               __snake_case    					=  (batch['''image'''] - mean) / std
               with torch.no_grad():
                    __snake_case    					=  model(_lowerCamelCase	)
               __snake_case    					=  outputs.argmax(dim=-1	)
               __snake_case					,    __snake_case    					=  accelerator.gather_for_metrics((predictions, batch['''label'''])	)
               __snake_case    					=  predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          __snake_case    					=  accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}'''	)







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     __snake_case    					=  argparse.ArgumentParser(description='''Simple example of training script.'''	)
     parser.add_argument('''--data_dir'''   ,	required=_lowerCamelCase   ,	help='''The data folder on disk.'''	)
     parser.add_argument(
         '''--mixed_precision'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	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=_lowerCamelCase   ,	default=_lowerCamelCase   ,	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.'''	)
     __snake_case    					=  parser.parse_args()
     __snake_case    					=  {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
     training_function(_lowerCamelCase   ,	_lowerCamelCase	)


if __name__ == "__main__":
     main()












'''simple docstring'''



from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


UpperCAmelCase_       :  Optional[int]      =		AttentionProcessor





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"  )  )} instead.'''
            deprecate('''cross_attention'''    ,  '''0.20.0'''    ,  __SCREAMING_SNAKE_CASE    ,  standard_warn=__SCREAMING_SNAKE_CASE  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






