




'''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			(     _UpperCamelCase:					List[str]					)		->				Dict:

     """simple docstring"""




     _snake_case							=   fname.split(os.path.sep					)[-1]
     return re.search(r"^(.*)_\d+\.jpg$"      ,		_UpperCamelCase					).groups()[0]

class      _a       (		__lowerCAmelCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None					)							->    Optional[Any]:
          _snake_case							=   file_names
          _snake_case							=   image_transform
          _snake_case							=   label_to_id






     def __len__(				self					)							->    int:
          return len(self.file_names					)






     def __getitem__(				self      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          _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 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Union[str, Any]					)		->				Dict:

     """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(_UpperCamelCase      ,		(list, tuple)					):
          _snake_case							=   (image_size, image_size)

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

     # Build the label correspondences
     _snake_case							=   [extract_label(_UpperCamelCase					) for fname in file_names]
     _snake_case							=   list(set(_UpperCamelCase					)					)
     id_to_label.sort()
     _snake_case							=   {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
     _snake_case							=   np.random.permutation(len(_UpperCamelCase					)					)
     _snake_case							=   int(0.8 * len(_UpperCamelCase					)					)
     _snake_case							=   random_perm[:cut]
     _snake_case							=   random_perm[cut:]

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

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

     # Instantiate dataloaders.
     _snake_case							=   DataLoader(_UpperCamelCase      ,		shuffle=_UpperCamelCase      ,		batch_size=_UpperCamelCase      ,		num_workers=4					)
     _snake_case							=   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)
     _snake_case							=   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).
     _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=_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.
     _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   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`.
               _snake_case							=   {k: v.to(accelerator.device					) for k, v in batch.items()}
               _snake_case							=   (batch["image"] - mean) / std
               _snake_case							=   model(_UpperCamelCase					)
               _snake_case							=   torch.nn.functional.cross_entropy(_UpperCamelCase      ,		batch["label"]					)
               accelerator.backward(_UpperCamelCase					)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

          model.eval()
          _snake_case							=   0
          _snake_case							=   0
          for _, batch in enumerate(_UpperCamelCase					):
               # 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(_UpperCamelCase					)
               _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}: {100 * eval_metric:.2f}"""					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   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."					)
     _snake_case							=   parser.parse_args()
     _snake_case							=   {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(_UpperCamelCase      ,		_UpperCamelCase					)


if __name__ == "__main__":
   main()








'''simple docstring'''





import argparse
import os


UpperCamelCase_		:    Union[str, Any]									=		{
    '''pytorch''': {
        '''1.13''': {
            '''torch''': '''1.13.1''',
            '''torchvision''': '''0.14.1''',
            '''torchaudio''': '''0.13.1''',
            '''python''': 3.9,
            '''cuda''': '''cu116''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu116'''
            ),
            '''base_image''': '''nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.12''': {
            '''torch''': '''1.12.1''',
            '''torchvision''': '''0.13.1''',
            '''torchaudio''': '''0.12.1''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.11''': {
            '''torch''': '''1.11.0''',
            '''torchvision''': '''0.12.0''',
            '''torchaudio''': '''0.11.0''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.10''': {
            '''torch''': '''1.10.2''',
            '''torchvision''': '''0.11.3''',
            '''torchaudio''': '''0.10.2''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        '''1.9''': {
            '''torch''': '''1.9.1''',
            '''torchvision''': '''0.10.1''',
            '''torchaudio''': '''0.9.1''',
            '''python''': 3.9,
            '''cuda''': '''cu111''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu111'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
    '''tensorflow''': {
        '''2.11''': {
            '''tensorflow''': '''2.11.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.11.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.10''': {
            '''tensorflow''': '''2.10.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.10.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.9''': {
            '''tensorflow''': '''2.9.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.9.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.8''': {
            '''tensorflow''': '''2.8.2''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.8.2''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.7''': {
            '''tensorflow''': '''2.7.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.7.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.6''': {
            '''tensorflow''': '''2.6.5''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.6.5''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.5''': {
            '''tensorflow''': '''2.5.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.5.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
}


if __name__ == "__main__":
   UpperCamelCase_		:    Optional[Any]									=		argparse.ArgumentParser('''Choose the framework and version to install''')
   parser.add_argument(
       '''--framework''', help='''The framework to install. Should be `torch` or `tensorflow`''', type=str, required=True
   )
   parser.add_argument('''--version''', help='''The version of the framework to install.''', type=str, required=True)
   UpperCamelCase_		:    Tuple									=		parser.parse_args()

   UpperCamelCase_		:    Optional[int]									=		past_versions_testing[args.framework][args.version]

   os.system(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
   print(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')

   UpperCamelCase_		:    List[Any]									=		''''''
   if args.framework == "pytorch":
      UpperCamelCase_		:    str									=		info['''cuda''']
   os.system(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
   print(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')


