"""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   _lowerCamelCase ( UpperCAmelCase__					)							->     List[Any]:


   '''simple docstring'''


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

class SCREAMING_SNAKE_CASE     (    A__	):







 """simple docstring"""


 def __init__(   self		:      Any					,   _snake_case		:      List[str]					,   _snake_case		:      List[Any]=None					,   _snake_case		:      Optional[Any]=None				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		file_names
    a__     				=		image_transform
    a__     				=		label_to_id


 def __len__(   self		:      List[Any]				)				->			Optional[Any]:




    '''simple docstring'''






    return len(self.file_names				)



 def __getitem__(   self		:      List[str]					,   _snake_case		:      int				)				->			List[Any]:




    '''simple docstring'''






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





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		Accelerator(cpu=args.cpu,mixed_precision=args.mixed_precision					)

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   a__     				=		config['lr']
   a__     				=		int(config['num_epochs']					)
   a__     				=		int(config['seed']					)
   a__     				=		int(config['batch_size']					)
   a__     				=		config['image_size']
   if not isinstance(UpperCAmelCase__,(list, tuple)					):
      a__     				=		(image_size, image_size)

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

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

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

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

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

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

   # Instantiate dataloaders.
   a__     				=		DataLoader(UpperCAmelCase__,shuffle=UpperCAmelCase__,batch_size=UpperCAmelCase__,num_workers=4					)
   a__     				=		DataLoader(UpperCAmelCase__,shuffle=UpperCAmelCase__,batch_size=UpperCAmelCase__,num_workers=4					)

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

   # 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).
   a__     				=		model.to(accelerator.device					)

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

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

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

   # Instantiate learning rate scheduler
   a__     				=		OneCycleLR(optimizer=UpperCAmelCase__,max_lr=UpperCAmelCase__,epochs=UpperCAmelCase__,steps_per_epoch=len(UpperCAmelCase__					)					)

   # 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.
   a__				,       a__				,       a__				,       a__				,       a__     				=		accelerator.prepare(
       UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

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

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

      a__     				=		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   _lowerCamelCase ( )							->     str:


   '''simple docstring'''


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


if __name__ == "__main__":
   main()



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__    				=					{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["DonutFeatureExtractor"]
     __magic_name__    				=					["DonutImageProcessor"]


if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor

else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


