


'''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_	:			Union[str, Any]	):
   UpperCAmelCase__  :List[Any]            =			fname.split(os.path.sep	)[-1]
   return re.search(r'''^(.*)_\d+\.jpg$''',      UpperCamelCase_	).groups()[0]




class 							UpperCAmelCase ( _snake_case	):


 def __init__(			self       :  Optional[int] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Any=None ,					__lowerCamelCase       :  Optional[Any]=None   ):
    UpperCAmelCase__  :Dict            =			file_names
    UpperCAmelCase__  :Optional[Any]            =			image_transform
    UpperCAmelCase__  :List[Any]            =			label_to_id


 def __len__(			self       :  List[str]   ):
    return len(self.file_names   )





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


def 					a__    (				UpperCamelCase_	:			int,      UpperCamelCase_	:			Any	):
   # Initialize accelerator
   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__  :Dict            =			int(config['''num_epochs''']	)
   UpperCAmelCase__  :Optional[Any]            =			int(config['''seed''']	)
   UpperCAmelCase__  :Tuple            =			int(config['''batch_size''']	)
   UpperCAmelCase__  :str            =			config['''image_size''']
   if not isinstance(UpperCamelCase_,      (list, tuple)	):
      UpperCAmelCase__  :str            =			(image_size, image_size)

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

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

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

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

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

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

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

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

   # Instantiate learning rate scheduler
   UpperCAmelCase__  :str            =			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.
   UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :str            =			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`.
         UpperCAmelCase__  :Dict            =			{k: v.to(accelerator.device	) for k, v in batch.items()}
         UpperCAmelCase__  :List[str]            =			(batch['''image'''] - mean) / std
         UpperCAmelCase__  :Any            =			model(UpperCamelCase_	)
         UpperCAmelCase__  :int            =			torch.nn.functional.cross_entropy(UpperCamelCase_,      batch['''label''']	)
         accelerator.backward(UpperCamelCase_	)
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()

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

      UpperCAmelCase__  :Union[str, Any]            =			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__    (				):
   UpperCAmelCase__  :List[str]            =			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.'''	)
   UpperCAmelCase__  :Any            =			parser.parse_args()
   UpperCAmelCase__  :Optional[Any]            =			{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
   training_function(UpperCamelCase_,      UpperCamelCase_	)


if __name__ == "__main__":
    main()







'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

__lowerCamelCase       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


    def 					a__    (				cls	:			Optional[int]	):
       return cls


def 					a__    (				UpperCamelCase_	:			Union[Tuple, List],      UpperCamelCase_	:			Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,      UpperCamelCase_	:			Optional["torch.device"] = None,      UpperCamelCase_	:			Optional["torch.dtype"] = None,      UpperCamelCase_	:			Optional["torch.layout"] = None,      ):
   UpperCAmelCase__  :Union[str, Any]            =			device
   UpperCAmelCase__  :List[Any]            =			shape[0]

   UpperCAmelCase__  :Tuple            =			layout or torch.strided
   UpperCAmelCase__  :Union[str, Any]            =			device or torch.device('''cpu'''	)

   if generator is not None:
      UpperCAmelCase__  :str            =			generator.device.type if not isinstance(UpperCamelCase_,      UpperCamelCase_	) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         UpperCAmelCase__  :Tuple            =			'''cpu'''
         if device != "mps":
            logger.info(
                F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
                F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
                F''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)

   if isinstance(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :List[str]            =			(1,) + shape[1:]
      UpperCAmelCase__  :Optional[int]            =			[
          torch.randn(UpperCamelCase_,      generator=generator[i],      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	)
          for i in range(UpperCamelCase_	)
      ]
      UpperCAmelCase__  :int            =			torch.cat(UpperCamelCase_,      dim=0	).to(UpperCamelCase_	)
   else:
      UpperCAmelCase__  :Optional[int]            =			torch.randn(UpperCamelCase_,      generator=UpperCamelCase_,      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	).to(UpperCamelCase_	)

   return latents


def 					a__    (				UpperCamelCase_	:			Dict	):
   if is_torch_version('''<''',      '''2.0.0'''	) or not hasattr(UpperCamelCase_,      '''_dynamo'''	):
      return False
   return isinstance(UpperCamelCase_,      torch._dynamo.eval_frame.OptimizedModule	)



