





'''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__							(							lowercase   :	Union[str, Any] )						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			fname.split(os.path.sep )[-1]
     return re.search(r'''^(.*)_\d+\.jpg$''',   lowercase ).groups()[0]
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase							       =			file_names
            _UpperCamelCase							       =			image_transform
            _UpperCamelCase							       =			label_to_id





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

            '''simple docstring'''




            return len(self.file_names	)







       def __getitem__(				self  :						Any			,					lowerCAmelCase__  :						int	)       ->	Dict:

            '''simple docstring'''




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







def    a__							(							lowercase   :	Optional[int],   lowercase   :	List[Any] )						->						List[Any]:





     """simple docstring"""

     if args.with_tracking:
          _UpperCamelCase							       =			Accelerator(
              cpu=args.cpu,   mixed_precision=args.mixed_precision,   log_with='''all''',   project_dir=args.project_dir )
     else:
          _UpperCamelCase							       =			Accelerator(cpu=args.cpu,   mixed_precision=args.mixed_precision )

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCamelCase							       =			config['''lr''']
     _UpperCamelCase							       =			int(config['''num_epochs'''] )
     _UpperCamelCase							       =			int(config['''seed'''] )
     _UpperCamelCase							       =			int(config['''batch_size'''] )
     _UpperCamelCase							       =			config['''image_size''']
     if not isinstance(lowercase,   (list, tuple) ):
          _UpperCamelCase							       =			(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							       =			args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _UpperCamelCase							       =			int(args.checkpointing_steps )
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
     else:
          _UpperCamelCase							       =			None

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

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

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

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

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

     # Instantiate dataloaders.
     _UpperCamelCase							       =			DataLoader(lowercase,   shuffle=lowercase,   batch_size=lowercase,   num_workers=4 )
     _UpperCamelCase							       =			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)
     _UpperCamelCase							       =			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).
     _UpperCamelCase							       =			model.to(accelerator.device )

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

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

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

     # Instantiate learning rate scheduler
     _UpperCamelCase							       =			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.
     _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			accelerator.prepare(
         lowercase,   lowercase,   lowercase,   lowercase,   lowercase )
     # We need to keep track of how many total steps we have iterated over
     _UpperCamelCase							       =			0
     # We also need to keep track of the starting epoch so files are named properly
     _UpperCamelCase							       =			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							       =			os.path.basename(args.resume_from_checkpoint )
          else:
               # Get the most recent checkpoint
               _UpperCamelCase							       =			[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
               dirs.sort(key=os.path.getctime )
               _UpperCamelCase							       =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _UpperCamelCase							       =			os.path.splitext(lowercase )[0]

          if "epoch" in training_difference:
               _UpperCamelCase							       =			int(training_difference.replace('''epoch_''',   '''''' ) ) + 1
               _UpperCamelCase							       =			None
          else:
               _UpperCamelCase							       =			int(training_difference.replace('''step_''',   '''''' ) )
               _UpperCamelCase							       =			resume_step // len(lowercase )
               resume_step -= starting_epoch * len(lowercase )

    # Now we train the model
     for epoch in range(lowercase,   lowercase ):
          model.train()
          if args.with_tracking:
               _UpperCamelCase							       =			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							       =			accelerator.skip_first_batches(lowercase,   lowercase )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               _UpperCamelCase							       =			train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               _UpperCamelCase							       =			{k: v.to(accelerator.device ) for k, v in batch.items()}
               _UpperCamelCase							       =			(batch['''image'''] - mean) / std
               _UpperCamelCase							       =			model(lowercase )
               _UpperCamelCase							       =			torch.nn.functional.cross_entropy(lowercase,   batch['''label'''] )
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(lowercase )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(lowercase,   lowercase ):
                    _UpperCamelCase							       =			F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _UpperCamelCase							       =			os.path.join(args.output_dir,   lowercase )
                         accelerator.save_state(lowercase )
          model.eval()
          _UpperCamelCase							       =			0
          _UpperCamelCase							       =			0
          for step, batch in enumerate(lowercase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               _UpperCamelCase							       =			{k: v.to(accelerator.device ) for k, v in batch.items()}
               _UpperCamelCase							       =			(batch['''image'''] - mean) / std
               with torch.no_grad():
                    _UpperCamelCase							       =			model(lowercase )
               _UpperCamelCase							       =			outputs.argmax(dim=-1 )
               _UpperCamelCase       ,							_UpperCamelCase							       =			accelerator.gather_for_metrics((predictions, batch['''label''']) )
               _UpperCamelCase							       =			predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          _UpperCamelCase							       =			accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}""" )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': 100 * eval_metric,
                       '''train_loss''': total_loss.item() / len(lowercase ),
                       '''epoch''': epoch,
                   },   step=lowercase,   )
          if checkpointing_steps == "epoch":
               _UpperCamelCase							       =			F"""epoch_{epoch}"""
               if args.output_dir is not None:
                    _UpperCamelCase							       =			os.path.join(args.output_dir,   lowercase )
               accelerator.save_state(lowercase )

     if args.with_tracking:
          accelerator.end_training()







def    a__							(							)						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			argparse.ArgumentParser(description='''Simple example of training script.''' )
     parser.add_argument('''--data_dir''',   required=lowercase,   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=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('''--cpu''',   action='''store_true''',   help='''If passed, will train on the CPU.''' )
     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(
         '''--output_dir''',   type=lowercase,   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=lowercase,   default=lowercase,   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=lowercase,   default='''logs''',   help='''Location on where to store experiment tracking logs` and relevent project information''',   )
     _UpperCamelCase							       =			parser.parse_args()
     _UpperCamelCase							       =			{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
     training_function(lowercase,   lowercase )


if __name__ == "__main__":
   main()








from collections.abc import Callable

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	Callable			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float				)       ->							np.array:

							'''simple docstring'''






							A      						=    int(np.ceil((x_end - xa) / step_size				)				)
							A      						=    np.zeros((n + 1,)				)
							A      						=    ya
							A      						=    xa

							for k in range(lowerCAmelCase__				):
														A      						=    y[k] + step_size * ode_func(lowerCAmelCase__			,	y[k]				)
														A      						=    y[k] + (
														    (step_size / 2) * (ode_func(lowerCAmelCase__			,	y[k]				) + ode_func(x + step_size			,	lowerCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()