'''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 SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]  )   ->				Optional[int]:







     """simple docstring"""
     a					:							Tuple							= fname.split(os.path.sep  )[-1]
     return re.search(R'^(.*)_\d+\.jpg$'				,	lowercase__  ).groups()[0]



class   UpperCamelCase					(       lowerCamelCase_       ):







    """simple docstring"""





    def __init__(					self     :						int							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Union[str, Any]=None							,							UpperCAmelCase_     :						int=None):


         """simple docstring"""







         a					:							Optional[Any]							= file_names
         a					:							List[Any]							= image_transform
         a					:							Any							= label_to_id





    def __len__(					self     :						Optional[int]):


         """simple docstring"""







         return len(self.file_names)







    def __getitem__(					self     :						Dict							,							UpperCAmelCase_     :						List[Any]):


         """simple docstring"""







         a					:							Tuple							= self.file_names[idx]
         a					:							str							= PIL.Image.open(lowerCamelCase_)
         a					:							Optional[int]							= raw_image.convert('RGB')
         if self.image_transform is not None:
              a					:							str							= self.image_transform(lowerCamelCase_)
         a					:							Any							= extract_label(lowerCamelCase_)
         if self.label_to_id is not None:
              a					:							List[str]							= self.label_to_id[label]
         return {"image": image, "label": label}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[Any]				,	snake_case       :				Union[str, Any]  )   ->				Dict:







     """simple docstring"""
     a					:							Dict							= Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision  )

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     a					:							Dict							= config['lr']
     a					:							Optional[Any]							= int(config['num_epochs']  )
     a					:							Tuple							= int(config['seed']  )
     a					:							Optional[int]							= int(config['batch_size']  )
     a					:							Any							= config['image_size']
     if not isinstance(lowercase__				,	(list, tuple)  ):
          a					:							Union[str, Any]							= (image_size, image_size)

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

     # Build the label correspondences
     a					:							str							= [extract_label(lowercase__  ) for fname in file_names]
     a					:							int							= list(set(lowercase__  )  )
     id_to_label.sort()
     a					:							Tuple							= {lbl: i for i, lbl in enumerate(lowercase__  )}

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

     # Split our filenames between train and validation
     a					:							Dict							= np.random.permutation(len(lowercase__  )  )
     a					:							int							= int(0.8 * len(lowercase__  )  )
     a					:							int							= random_perm[:cut]
     a					:							Optional[Any]							= random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     a					:							List[str]							= Compose([RandomResizedCrop(lowercase__				,	scale=(0.5, 1.0)  ), ToTensor()]  )
     a					:							Tuple							= PetsDataset(
         [file_names[i] for i in train_split]				,	image_transform=lowercase__				,	label_to_id=lowercase__  )

     # For evaluation, we use a deterministic Resize
     a					:							Optional[int]							= Compose([Resize(lowercase__  ), ToTensor()]  )
     a					:							Any							= PetsDataset([file_names[i] for i in eval_split]				,	image_transform=lowercase__				,	label_to_id=lowercase__  )

     # Instantiate dataloaders.
     a					:							List[str]							= DataLoader(lowercase__				,	shuffle=lowercase__				,	batch_size=lowercase__				,	num_workers=4  )
     a					:							Any							= DataLoader(lowercase__				,	shuffle=lowercase__				,	batch_size=lowercase__				,	num_workers=4  )

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

     # 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					:							Optional[Any]							= model.to(accelerator.device  )

     # Freezing the base model
     for param in model.parameters():
          a					:							Dict							= False
     for param in model.get_classifier().parameters():
          a					:							List[Any]							= True

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

     # Instantiate optimizer
     a					:							List[Any]							= torch.optim.Adam(params=model.parameters()				,	lr=lr / 25  )

     # Instantiate learning rate scheduler
     a					:							int							= OneCycleLR(optimizer=lowercase__				,	max_lr=lowercase__				,	epochs=lowercase__				,	steps_per_epoch=len(lowercase__  )  )

     # 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					:							Dict							= accelerator.prepare(
         lowercase__				,	lowercase__				,	lowercase__				,	lowercase__				,	lowercase__  )

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

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

          a					:							Optional[int]							= accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""  )






def SCREAMING_SNAKE_CASE__  (				)   ->				int:







     """simple docstring"""
     a					:							Dict							= argparse.ArgumentParser(description='Simple example of training script.'  )
     parser.add_argument('--data_dir'				,	required=lowercase__				,	help='The data folder on disk.'  )
     parser.add_argument(
         '--mixed_precision'				,	type=lowercase__				,	default=lowercase__				,	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=lowercase__				,	default=lowercase__				,	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					:							Tuple							= parser.parse_args()
     a					:							int							= {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
     training_function(lowercase__				,	lowercase__  )


if __name__ == "__main__":
      main()





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

