



'''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_					(							_lowerCAmelCase	)				->				Tuple:
  __lowerCamelCase	:	Tuple   	=			fname.split(os.path.sep	)[-1]
  return re.search(r'^(.*)_\d+\.jpg$'	,_lowerCAmelCase	).groups()[0]






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def __init__(			self				:     Tuple      ,     _a				:     Tuple      ,     _a				:     Any=None      ,     _a				:     str=None       ) -> List[str]:
    __lowerCamelCase	:	Optional[int]   	=			file_names
    __lowerCamelCase	:	List[Any]   	=			image_transform
    __lowerCamelCase	:	Any   	=			label_to_id




  def __len__(			self				:     Optional[int]       ) -> Tuple:
    return len(self.file_names       )




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




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize accelerator
  __lowerCamelCase	:	Optional[int]   	=			Accelerator(cpu=args.cpu	,mixed_precision=args.mixed_precision	)

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

  # Grab all the image filenames
  __lowerCamelCase	:	Optional[Any]   	=			[os.path.join(args.data_dir	,_lowerCAmelCase	) for fname in os.listdir(args.data_dir	) if fname.endswith('.jpg'	)]

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

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

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

  # Instantiate dataloaders.
  __lowerCamelCase	:	int   	=			DataLoader(_lowerCAmelCase	,shuffle=_lowerCAmelCase	,batch_size=_lowerCAmelCase	,num_workers=4	)
  __lowerCamelCase	:	Tuple   	=			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)
  __lowerCamelCase	:	Any   	=			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).
  __lowerCamelCase	:	Dict   	=			model.to(accelerator.device	)

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

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

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

  # Instantiate learning rate scheduler
  __lowerCamelCase	:	Any   	=			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.
  __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	Union[str, Any]   	=			accelerator.prepare(
      _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

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

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

    __lowerCamelCase	:	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_					(							)				->				Optional[Any]:
  __lowerCamelCase	:	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(
      '--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(
      '--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('--cpu'	,action='store_true'	,help='If passed, will train on the CPU.'	)
  __lowerCamelCase	:	Optional[int]   	=			parser.parse_args()
  __lowerCamelCase	:	Dict   	=			{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
  training_function(_lowerCAmelCase	,_lowerCAmelCase	)


if __name__ == "__main__":
   main()











'''simple docstring'''





import unittest

from diffusers import __version__
from diffusers.utils import deprecate






class  lowerCamelCase_   (    unittest.TestCase					):
  """simple docstring"""




  a_							 =""".""".join([str(int(__version__.split("""."""					)[0]					) + 1					)] + __version__.split("""."""					)[1:]					)
  a_							 ="""0.0.1"""




  def 						_lowercase					(			self				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[int]   	=			deprecate('deprecated_arg'      ,     self.higher_version      ,     'message'      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    __lowerCamelCase	:	int   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[Any]   	=			deprecate(('deprecated_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     int       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	Any   	=			deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     take_from=_a      ,     )
    assert output_a == 4
    assert output_a == 8
    assert (
        str(warning.warnings[0].message       )
        == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     str       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
    assert "line" in str(error.exception       )
    assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'message')       )

    assert (
        str(warning.warning       )
        == f'`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     )
    assert (
        str(warning.warnings[0].message       )
        == f'`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Dict:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	int   	=			deprecate(('arg', self.higher_version, 'message')      ,     take_from=Args()       )

    assert arg == 5
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Dict       ) -> Union[str, Any]:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5
      a_							 =7

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	List[str]   	=			deprecate(
          ('arg', self.higher_version, 'message')      ,     ('foo', self.higher_version, 'message')      ,     ('does not exist', self.higher_version, 'message')      ,     take_from=Args()      ,     )

    assert arg_a == 5
    assert arg_a == 7
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[0].message       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.lower_version, 'message')      ,     take_from=_a       )

    assert (
        str(error.exception       )
        == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
        f' {__version__} is >= {self.lower_version}'
    )




  def 						_lowercase					(			self				:     Optional[int]       ) -> str:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )

    assert str(warning.warning       ) == "This message is better!!!"




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )
    assert str(warning.warning       ) == "This message is better!!!"
    assert "diffusers/tests/others/test_utils.py" in warning.filename






