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


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  file_names
         UpperCAmelCase__							:				Optional[Any]   				=  image_transform
         UpperCAmelCase__							:				Optional[int]   				=  label_to_id




      def __len__(				self							)			->			int:



         '''simple docstring'''



         return len(self.file_names							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



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







def __UpperCamelCase(   _A					:				str  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   # Initialize accelerator
   if args.with_tracking:
      UpperCAmelCase__							:				List[Any]   				=  Accelerator(
          cpu=args.cpu  ,			mixed_precision=args.mixed_precision  ,			log_with='''all'''  ,			project_dir=args.project_dir    )
   else:
      UpperCAmelCase__							:				Optional[Any]   				=  Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision    )

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

   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps  ,			'''isdigit'''    ):
      if args.checkpointing_steps == "epoch":
         UpperCAmelCase__							:				int   				=  args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         UpperCAmelCase__							:				str   				=  int(args.checkpointing_steps    )
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      UpperCAmelCase__							:				str   				=  None

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      UpperCAmelCase__							:				List[str]   				=  os.path.split(lowerCAmelCase__    )[-1].split('''.'''    )[0]
      accelerator.init_trackers(lowerCAmelCase__  ,			lowerCAmelCase__    )

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

   # Build the label correspondences
   UpperCAmelCase__							:				str   				=  [extract_label(lowerCAmelCase__    ) for fname in file_names]
   UpperCAmelCase__							:				Dict   				=  list(set(lowerCAmelCase__    )    )
   id_to_label.sort()
   UpperCAmelCase__							:				List[str]   				=  {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
   UpperCAmelCase__							:				Tuple   				=  np.random.permutation(len(lowerCAmelCase__    )    )
   UpperCAmelCase__							:				int   				=  int(0.8 * len(lowerCAmelCase__    )    )
   UpperCAmelCase__							:				Any   				=  random_perm[:cut]
   UpperCAmelCase__							:				List[str]   				=  random_perm[cut:]

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

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

   # Instantiate dataloaders.
   UpperCAmelCase__							:				Union[str, Any]   				=  DataLoader(lowerCAmelCase__  ,			shuffle=lowerCAmelCase__  ,			batch_size=lowerCAmelCase__  ,			num_workers=4    )
   UpperCAmelCase__							:				List[Any]   				=  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)
   UpperCAmelCase__							:				List[str]   				=  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).
   UpperCAmelCase__							:				List[Any]   				=  model.to(accelerator.device    )

   # Freezing the base model
   for param in model.parameters():
      UpperCAmelCase__							:				Optional[Any]   				=  False
   for param in model.get_classifier().parameters():
      UpperCAmelCase__							:				int   				=  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__							:				Union[str, Any]   				=  torch.tensor(model.default_cfg['''std''']    )[None, :, None, None].to(accelerator.device    )

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

   # Instantiate learning rate scheduler
   UpperCAmelCase__							:				Optional[int]   				=  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.
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  accelerator.prepare(
       lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__    )
   # We need to keep track of how many total steps we have iterated over
   UpperCAmelCase__							:				Optional[Any]   				=  0
   # We also need to keep track of the starting epoch so files are named properly
   UpperCAmelCase__							:				str   				=  0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         UpperCAmelCase__							:				Dict   				=  os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         UpperCAmelCase__							:				Optional[int]   				=  [f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         UpperCAmelCase__							:				Union[str, Any]   				=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      UpperCAmelCase__							:				int   				=  os.path.splitext(lowerCAmelCase__    )[0]

      if "epoch" in training_difference:
         UpperCAmelCase__							:				Optional[Any]   				=  int(training_difference.replace('''epoch_'''  ,			''''''    )    ) + 1
         UpperCAmelCase__							:				List[Any]   				=  None
      else:
         UpperCAmelCase__							:				Dict   				=  int(training_difference.replace('''step_'''  ,			''''''    )    )
         UpperCAmelCase__							:				int   				=  resume_step // len(lowerCAmelCase__    )
         resume_step -= starting_epoch * len(lowerCAmelCase__    )

    # Now we train the model
   for epoch in range(lowerCAmelCase__  ,			lowerCAmelCase__    ):
      model.train()
      if args.with_tracking:
         UpperCAmelCase__							:				Any   				=  0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         UpperCAmelCase__							:				List[Any]   				=  accelerator.skip_first_batches(lowerCAmelCase__  ,			lowerCAmelCase__    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         UpperCAmelCase__							:				Optional[Any]   				=  train_dataloader
      for batch in active_dataloader:
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCAmelCase__							:				Union[str, Any]   				=  {k: v.to(accelerator.device    ) for k, v in batch.items()}
         UpperCAmelCase__							:				Union[str, Any]   				=  (batch['''image'''] - mean) / std
         UpperCAmelCase__							:				Optional[Any]   				=  model(lowerCAmelCase__    )
         UpperCAmelCase__							:				int   				=  torch.nn.functional.cross_entropy(lowerCAmelCase__  ,			batch['''label''']    )
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(lowerCAmelCase__    )
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         overall_step += 1
         if isinstance(lowerCAmelCase__  ,			lowerCAmelCase__    ):
            UpperCAmelCase__							:				Optional[int]   				=  F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  UpperCAmelCase__							:				List[str]   				=  os.path.join(args.output_dir  ,			lowerCAmelCase__    )
               accelerator.save_state(lowerCAmelCase__    )
      model.eval()
      UpperCAmelCase__							:				Tuple   				=  0
      UpperCAmelCase__							:				Union[str, Any]   				=  0
      for step, batch in enumerate(lowerCAmelCase__    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCAmelCase__							:				Optional[Any]   				=  {k: v.to(accelerator.device    ) for k, v in batch.items()}
         UpperCAmelCase__							:				Any   				=  (batch['''image'''] - mean) / std
         with torch.no_grad():
            UpperCAmelCase__							:				Union[str, Any]   				=  model(lowerCAmelCase__    )
         UpperCAmelCase__							:				List[Any]   				=  outputs.argmax(dim=-1    )
         UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  accelerator.gather_for_metrics((predictions, batch['''label'''])    )
         UpperCAmelCase__							:				Optional[Any]   				=  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_00 * eval_metric:.2f}'''    )
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': 1_00 * eval_metric,
                 '''train_loss''': total_loss.item() / len(lowerCAmelCase__    ),
                 '''epoch''': epoch,
             }  ,			step=lowerCAmelCase__  ,			)
      if checkpointing_steps == "epoch":
         UpperCAmelCase__							:				Optional[int]   				=  F'''epoch_{epoch}'''
         if args.output_dir is not None:
            UpperCAmelCase__							:				List[Any]   				=  os.path.join(args.output_dir  ,			lowerCAmelCase__    )
         accelerator.save_state(lowerCAmelCase__    )

   if args.with_tracking:
      accelerator.end_training()







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
   parser.add_argument('''--data_dir'''  ,			required=lowerCAmelCase__  ,			help='''The data folder on disk.'''    )
   parser.add_argument('''--fp16'''  ,			action='''store_true'''  ,			help='''If passed, will use FP16 training.'''    )
   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('''--cpu'''  ,			action='''store_true'''  ,			help='''If passed, will train on the CPU.'''    )
   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(
       '''--output_dir'''  ,			type=lowerCAmelCase__  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
   parser.add_argument(
       '''--resume_from_checkpoint'''  ,			type=lowerCAmelCase__  ,			default=lowerCAmelCase__  ,			help='''If the training should continue from a checkpoint folder.'''  ,			)
   parser.add_argument(
       '''--with_tracking'''  ,			action='''store_true'''  ,			help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,			)
   parser.add_argument(
       '''--project_dir'''  ,			type=lowerCAmelCase__  ,			default='''logs'''  ,			help='''Location on where to store experiment tracking logs` and relevent project information'''  ,			)
   UpperCAmelCase__							:				Dict   				=  parser.parse_args()
   UpperCAmelCase__							:				Union[str, Any]   				=  {'''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'''

import re







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''

   if len(re.findall('''[ATCG]'''  ,			_A    )    ) != len(_A    ):
      raise ValueError('''Invalid Strand'''    )

   return dna.translate(dna.maketrans('''ATCG'''  ,			'''TAGC'''    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




