

"""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   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					fname.split(os.path.sep      )[-1]
 return re.search(r"""^(.*)_\d+\.jpg$"""							,    __UpperCamelCase      ).groups()[0]







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[Any]					,A		:			List[Any]					,A		:			Dict=None					,A		:			Any=None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					file_names
   UpperCAmelCase__					:  Optional[Any]					   =					image_transform
   UpperCAmelCase__					:  str					   =					label_to_id

  def __len__(  self		:			List[str]    ):






   '''simple docstring'''
   return len(self.file_names    )




  def __getitem__(  self		:			List[str]					,A		:			List[Any]    ):






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







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 if args.with_tracking:
  UpperCAmelCase__					:  Union[str, Any]					   =					Accelerator(
      cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    log_with="""all"""							,    project_dir=args.project_dir      )
 else:
  UpperCAmelCase__					:  Union[str, 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[str]					   =					config["""lr"""]
 UpperCAmelCase__					:  Optional[int]					   =					int(config["""num_epochs"""]      )
 UpperCAmelCase__					:  str					   =					int(config["""seed"""]      )
 UpperCAmelCase__					:  Tuple					   =					int(config["""batch_size"""]      )
 UpperCAmelCase__					:  int					   =					config["""image_size"""]
 if not isinstance(__UpperCamelCase							,    (list, tuple)      ):
  UpperCAmelCase__					:  Any					   =					(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__					:  Any					   =					args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   UpperCAmelCase__					:  Any					   =					int(args.checkpointing_steps      )
  else:
   raise ValueError(
       F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
 else:
  UpperCAmelCase__					:  Any					   =					None

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

 # Grab all the image filenames
 UpperCAmelCase__					:  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__					:  Any					   =					[extract_label(__UpperCamelCase      ) for fname in file_names]
 UpperCAmelCase__					:  Any					   =					list(set(__UpperCamelCase      )      )
 id_to_label.sort()
 UpperCAmelCase__					:  Tuple					   =					{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[int]					   =					np.random.permutation(len(__UpperCamelCase      )      )
 UpperCAmelCase__					:  Dict					   =					int(0.8 * len(__UpperCamelCase      )      )
 UpperCAmelCase__					:  List[str]					   =					random_perm[:cut]
 UpperCAmelCase__					:  int					   =					random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 UpperCAmelCase__					:  int					   =					Compose([RandomResizedCrop(__UpperCamelCase							,    scale=(0.5, 1.0)      ), ToTensor()]      )
 UpperCAmelCase__					:  Union[str, 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__					:  List[Any]					   =					Compose([Resize(__UpperCamelCase      ), ToTensor()]      )
 UpperCAmelCase__					:  Dict					   =					PetsDataset([file_names[i] for i in eval_split]							,    image_transform=__UpperCamelCase							,    label_to_id=__UpperCamelCase      )

 # Instantiate dataloaders.
 UpperCAmelCase__					:  Union[str, Any]					   =					DataLoader(__UpperCamelCase							,    shuffle=__UpperCamelCase							,    batch_size=__UpperCamelCase							,    num_workers=4      )
 UpperCAmelCase__					:  Optional[int]					   =					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__					:  Dict					   =					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__					:  Optional[int]					   =					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__					:  Tuple					   =					True

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

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

 # Instantiate learning rate scheduler
 UpperCAmelCase__					:  Optional[Any]					   =					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__					:  Tuple					   =					accelerator.prepare(
     __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )
 # 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__					:  List[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__					:  Union[str, Any]					   =					[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
   dirs.sort(key=os.path.getctime      )
   UpperCAmelCase__					:  Optional[int]					   =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  UpperCAmelCase__					:  Dict					   =					os.path.splitext(__UpperCamelCase      )[0]

  if "epoch" in training_difference:
   UpperCAmelCase__					:  Any					   =					int(training_difference.replace("""epoch_"""							,    """"""      )      ) + 1
   UpperCAmelCase__					:  Union[str, Any]					   =					None
  else:
   UpperCAmelCase__					:  Tuple					   =					int(training_difference.replace("""step_"""							,    """"""      )      )
   UpperCAmelCase__					:  List[str]					   =					resume_step // len(__UpperCamelCase      )
   resume_step -= starting_epoch * len(__UpperCamelCase      )

    # Now we train the model
 for epoch in range(__UpperCamelCase							,    __UpperCamelCase      ):
  model.train()
  if args.with_tracking:
   UpperCAmelCase__					:  Optional[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__					:  Dict					   =					accelerator.skip_first_batches(__UpperCamelCase							,    __UpperCamelCase      )
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   UpperCAmelCase__					:  List[Any]					   =					train_dataloader
  for batch in active_dataloader:
   # 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__					:  Union[str, Any]					   =					(batch["""image"""] - mean) / std
   UpperCAmelCase__					:  Tuple					   =					model(__UpperCamelCase      )
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.nn.functional.cross_entropy(__UpperCamelCase							,    batch["""label"""]      )
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(__UpperCamelCase      )
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(__UpperCamelCase							,    __UpperCamelCase      ):
    UpperCAmelCase__					:  Tuple					   =					F"step_{overall_step}"
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      UpperCAmelCase__					:  Dict					   =					os.path.join(args.output_dir							,    __UpperCamelCase      )
     accelerator.save_state(__UpperCamelCase      )
  model.eval()
  UpperCAmelCase__					:  Any					   =					0
  UpperCAmelCase__					:  List[Any]					   =					0
  for step, 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__					:  Tuple					   =					(batch["""image"""] - mean) / std
   with torch.no_grad():
    UpperCAmelCase__					:  List[str]					   =					model(__UpperCamelCase      )
   UpperCAmelCase__					:  List[Any]					   =					outputs.argmax(dim=-1      )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					accelerator.gather_for_metrics((predictions, batch["""label"""])      )
   UpperCAmelCase__					:  List[str]					   =					predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  UpperCAmelCase__					:  str					   =					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(__UpperCamelCase      ),
           """epoch""": epoch,
       }							,    step=__UpperCamelCase							,    )
  if checkpointing_steps == "epoch":
   UpperCAmelCase__					:  Optional[int]					   =					F"epoch_{epoch}"
   if args.output_dir is not None:
    UpperCAmelCase__					:  Union[str, Any]					   =					os.path.join(args.output_dir							,    __UpperCamelCase      )
   accelerator.save_state(__UpperCamelCase      )

 if args.with_tracking:
  accelerator.end_training()
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					argparse.ArgumentParser(description="""Simple example of training script."""      )
 parser.add_argument("""--data_dir"""							,    required=__UpperCamelCase							,    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=__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("""--cpu"""							,    action="""store_true"""							,    help="""If passed, will train on the CPU."""      )
 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(
     """--output_dir"""							,    type=__UpperCamelCase							,    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=__UpperCamelCase							,    default=__UpperCamelCase							,    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=__UpperCamelCase							,    default="""logs"""							,    help="""Location on where to store experiment tracking logs` and relevent project information"""							,    )
 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"""




import argparse
import os
import re

import packaging.version


__UpperCAmelCase =				'examples/'
__UpperCAmelCase =				{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__UpperCAmelCase =				{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
__UpperCAmelCase =				'README.md'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  Dict					   =					f.read()
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					REPLACE_PATTERNS[pattern]
 UpperCAmelCase__					:  List[str]					   =					replace.replace("""VERSION"""							,    __UpperCamelCase      )
 UpperCAmelCase__					:  Tuple					   =					re_pattern.sub(__UpperCamelCase							,    __UpperCamelCase      )
 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.write(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for folder, directories, fnames in os.walk(__UpperCamelCase      ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove("""research_projects"""      )
  if "legacy" in directories:
   directories.remove("""legacy"""      )
  for fname in fnames:
   if fname.endswith(""".py"""      ):
    update_version_in_file(os.path.join(__UpperCamelCase							,    __UpperCamelCase      )							,    __UpperCamelCase							,    pattern="""examples"""      )
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=False      ):
 '''simple docstring'''





 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )
 if not patch:
  update_version_in_examples(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					"""🤗 Transformers currently provides the following architectures"""
 UpperCAmelCase__					:  Union[str, Any]					   =					"""1. Want to contribute a new model?"""
 with open(__UpperCamelCase							,    """r"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  UpperCAmelCase__					:  str					   =					f.readlines()

 # Find the start of the list.
 UpperCAmelCase__					:  List[Any]					   =					0
 while not lines[start_index].startswith(_start_prompt      ):
  start_index += 1
 start_index += 1

 UpperCAmelCase__					:  int					   =					start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt      ):
  if lines[index].startswith("""1."""      ):
   UpperCAmelCase__					:  List[Any]					   =					lines[index].replace(
       """https://huggingface.co/docs/transformers/main/model_doc"""							,    """https://huggingface.co/docs/transformers/model_doc"""							,    )
  index += 1

 with open(__UpperCamelCase							,    """w"""							,    encoding="""utf-8"""							,    newline="""\n"""      ) as f:
  f.writelines(__UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 with open(REPLACE_FILES["""init"""]							,    """r"""      ) as f:
  UpperCAmelCase__					:  Optional[Any]					   =					f.read()
 UpperCAmelCase__					:  int					   =					REPLACE_PATTERNS["""init"""][0].search(__UpperCamelCase      ).groups()[0]
 return packaging.version.parse(__UpperCamelCase      )
def   lowerCAmelCase						(					__UpperCamelCase=False      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					get_version()
 if patch and default_version.is_devrelease:
  raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
 if default_version.is_devrelease:
  UpperCAmelCase__					:  List[str]					   =					default_version.base_version
 elif patch:
  UpperCAmelCase__					:  Any					   =					F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  UpperCAmelCase__					:  Optional[Any]					   =					F"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 UpperCAmelCase__					:  Optional[int]					   =					input(F"Which version are you releasing? [{default_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  Union[str, Any]					   =					default_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase							,    patch=__UpperCamelCase      )
 if not patch:
  print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
  clean_main_ref_in_model_list()
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					get_version()
 UpperCAmelCase__					:  Any					   =					F"{current_version.major}.{current_version.minor + 1}.0.dev0"
 UpperCAmelCase__					:  List[Any]					   =					current_version.base_version

 # Check with the user we got that right.
 UpperCAmelCase__					:  Dict					   =					input(F"Which version are we developing now? [{dev_version}]"      )
 if len(__UpperCamelCase      ) == 0:
  UpperCAmelCase__					:  List[Any]					   =					dev_version

 print(F"Updating version to {version}."      )
 global_version_update(__UpperCamelCase      )
 print("""Cleaning main README, don't forget to run `make fix-copies`."""      )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      __UpperCAmelCase =				parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()





