





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 (  _a   :					Union[str, Any]			)	->			List[Any]:
     lowerCAmelCase_					:       Dict    =		fname.split(os.path.sep			)[-1]
     return re.search(R"""^(.*)_\d+\.jpg$"""					,   _a			).groups()[0]





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase=None    ,						_lowercase=None						):
       lowerCAmelCase_					:       str    =		file_names
       lowerCAmelCase_					:       Optional[int]    =		image_transform
       lowerCAmelCase_					:       Union[str, Any]    =		label_to_id







  def __len__(	self						):
       return len(self.file_names						)







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






def 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Tuple			)	->			List[Any]:
     # Initialize accelerator
     if args.with_tracking:
          lowerCAmelCase_					:       int    =		Accelerator(
              cpu=args.cpu					,   mixed_precision=args.mixed_precision					,   log_with="""all"""					,   project_dir=args.project_dir			)
     else:
          lowerCAmelCase_					:       Any    =		Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision			)

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

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

     # Grab all the image filenames
     lowerCAmelCase_					:       Dict    =		[os.path.join(args.data_dir					,   _a			) for fname in os.listdir(args.data_dir			) if fname.endswith(""".jpg"""			)]

     # Build the label correspondences
     lowerCAmelCase_					:       Union[str, Any]    =		[extract_label(_a			) for fname in file_names]
     lowerCAmelCase_					:       Optional[int]    =		list(set(_a			)			)
     id_to_label.sort()
     lowerCAmelCase_					:       str    =		{lbl: i for i, lbl in enumerate(_a			)}

     # Set the seed before splitting the data.
     np.random.seed(_a			)
     torch.manual_seed(_a			)
     torch.cuda.manual_seed_all(_a			)

     # Split our filenames between train and validation
     lowerCAmelCase_					:       Optional[Any]    =		np.random.permutation(len(_a			)			)
     lowerCAmelCase_					:       Tuple    =		int(0.8 * len(_a			)			)
     lowerCAmelCase_					:       Union[str, Any]    =		random_perm[:cut]
     lowerCAmelCase_					:       Optional[int]    =		random_perm[cut:]

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

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

     # Instantiate dataloaders.
     lowerCAmelCase_					:       Tuple    =		DataLoader(_a					,   shuffle=_a					,   batch_size=_a					,   num_workers=4			)
     lowerCAmelCase_					:       Optional[int]    =		DataLoader(_a					,   shuffle=_a					,   batch_size=_a					,   num_workers=4			)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_					:       Optional[Any]    =		create_model("""resnet50d"""					,   pretrained=_a					,   num_classes=len(_a			)			)

     # 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_					:       int    =		model.to(accelerator.device			)

     # Freezing the base model
     for param in model.parameters():
          lowerCAmelCase_					:       Any    =		False
     for param in model.get_classifier().parameters():
          lowerCAmelCase_					:       Any    =		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_					:       Optional[int]    =		torch.tensor(model.default_cfg["""std"""]			)[None, :, None, None].to(accelerator.device			)

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

     # Instantiate learning rate scheduler
     lowerCAmelCase_					:       Union[str, Any]    =		OneCycleLR(optimizer=_a					,   max_lr=_a					,   epochs=_a					,   steps_per_epoch=len(_a			)			)

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

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

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

     if args.with_tracking:
          accelerator.end_training()






def 							_lowerCAmelCase (  )	->			Optional[Any]:
     lowerCAmelCase_					:       Optional[int]    =		argparse.ArgumentParser(description="""Simple example of training script."""			)
     parser.add_argument("""--data_dir"""					,   required=_a					,   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=_a					,   default=_a					,   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=_a					,   default=_a					,   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=_a					,   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=_a					,   default=_a					,   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=_a					,   default="""logs"""					,   help="""Location on where to store experiment tracking logs` and relevent project information"""					,   )
     lowerCAmelCase_					:       Any    =		parser.parse_args()
     lowerCAmelCase_					:       Any    =		{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
     training_function(_a					,   _a			)


if __name__ == "__main__":
      main()







import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_					:   Tuple 							=						"""src/diffusers"""
UpperCAmelCase_					:   str 							=						"""."""


# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase_					:   Any 							=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase_					:   Union[str, Any] 							=						spec.loader.load_module()






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Optional[int]			)	->			Tuple:
     return line.startswith(_a			) or len(_a			) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$"""					,   _a			) is not None






def 							_lowerCAmelCase (  _a   :					List[Any]			)	->			int:
     lowerCAmelCase_					:       List[str]    =		object_name.split("""."""			)
     lowerCAmelCase_					:       List[Any]    =		0

     # First let's find the module where our object lives.
     lowerCAmelCase_					:       Optional[int]    =		parts[i]
     while i < len(_a			) and not os.path.isfile(os.path.join(_a					,   F'{module}.py'			)			):
          i += 1
          if i < len(_a			):
               lowerCAmelCase_					:       Dict    =		os.path.join(_a					,   parts[i]			)
     if i >= len(_a			):
          raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'			)

     with open(os.path.join(_a					,   F'{module}.py'			)					,   """r"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
          lowerCAmelCase_					:       Union[str, Any]    =		f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase_					:       List[Any]    =		""""""
     lowerCAmelCase_					:       Optional[int]    =		0
     for name in parts[i + 1 :]:
          while (
              line_index < len(_a			) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'					,   lines[line_index]			) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(_a			):
          raise ValueError(F' {object_name} does not match any function or class in {module}.'			)

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     lowerCAmelCase_					:       str    =		line_index
     while line_index < len(_a			) and _should_continue(lines[line_index]					,   _a			):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]			) <= 1:
          line_index -= 1

     lowerCAmelCase_					:       int    =		lines[start_index:line_index]
     return "".join(_a			)


UpperCAmelCase_					:   str 							=						re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
UpperCAmelCase_					:   int 							=						re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
UpperCAmelCase_					:   List[str] 							=						re.compile(r"""<FILL\s+[^>]*>""")






def 							_lowerCAmelCase (  _a   :					Dict			)	->			List[Any]:
     lowerCAmelCase_					:       int    =		code.split("""\n"""			)
     lowerCAmelCase_					:       Any    =		0
     while idx < len(_a			) and len(lines[idx]			) == 0:
          idx += 1
     if idx < len(_a			):
          return re.search(R"""^(\s*)\S"""					,   lines[idx]			).groups()[0]
     return ""






def 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			Union[str, Any]:
     lowerCAmelCase_					:       Tuple    =		len(get_indent(_a			)			) > 0
     if has_indent:
          lowerCAmelCase_					:       Union[str, Any]    =		F'class Bla:\n{code}'
     lowerCAmelCase_					:       Tuple    =		black.Mode(target_versions={black.TargetVersion.PYaa}					,   line_length=1_19					,   preview=_a			)
     lowerCAmelCase_					:       str    =		black.format_str(_a					,   mode=_a			)
     lowerCAmelCase_   ,  lowerCAmelCase_					:       List[str]    =		style_docstrings_in_code(_a			)
     return result[len("""class Bla:\n"""			) :] if has_indent else result






def 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					str=False			)	->			Tuple:
     with open(_a					,   """r"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
          lowerCAmelCase_					:       List[str]    =		f.readlines()
     lowerCAmelCase_					:       Union[str, Any]    =		[]
     lowerCAmelCase_					:       Dict    =		0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(_a			):
          lowerCAmelCase_					:       str    =		_re_copy_warning.search(lines[line_index]			)
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		search.groups()
          lowerCAmelCase_					:       List[Any]    =		find_code_in_diffusers(_a			)
          lowerCAmelCase_					:       Tuple    =		get_indent(_a			)

          lowerCAmelCase_					:       Union[str, Any]    =		line_index + 1 if indent == theoretical_indent else line_index + 2
          lowerCAmelCase_					:       str    =		theoretical_indent
          lowerCAmelCase_					:       Tuple    =		start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase_					:       List[Any]    =		True
          while line_index < len(_a			) and should_continue:
               line_index += 1
               if line_index >= len(_a			):
                    break
               lowerCAmelCase_					:       List[Any]    =		lines[line_index]
               lowerCAmelCase_					:       Tuple    =		_should_continue(_a					,   _a			) and re.search(F'^{indent}# End copy'					,   _a			) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]			) <= 1:
               line_index -= 1

          lowerCAmelCase_					:       str    =		lines[start_index:line_index]
          lowerCAmelCase_					:       Optional[Any]    =		"""""".join(_a			)

          # Remove any nested `Copied from` comments to avoid circular copies
          lowerCAmelCase_					:       Optional[int]    =		[line for line in theoretical_code.split("""\n"""			) if _re_copy_warning.search(_a			) is None]
          lowerCAmelCase_					:       str    =		"""\n""".join(_a			)

          # Before comparing, use the `replace_pattern` on the original code.
          if len(_a			) > 0:
               lowerCAmelCase_					:       List[str]    =		replace_pattern.replace("""with"""					,   """"""			).split(""","""			)
               lowerCAmelCase_					:       Any    =		[_re_replace_pattern.search(_a			) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       List[Any]    =		pattern.groups()
                    lowerCAmelCase_					:       Union[str, Any]    =		re.sub(_a					,   _a					,   _a			)
                    if option.strip() == "all-casing":
                         lowerCAmelCase_					:       Dict    =		re.sub(obja.lower()					,   obja.lower()					,   _a			)
                         lowerCAmelCase_					:       Dict    =		re.sub(obja.upper()					,   obja.upper()					,   _a			)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               lowerCAmelCase_					:       str    =		blackify(lines[start_index - 1] + theoretical_code			)
               lowerCAmelCase_					:       Optional[Any]    =		theoretical_code[len(lines[start_index - 1]			) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]			)
               if overwrite:
                    lowerCAmelCase_					:       Any    =		lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase_					:       Dict    =		start_index + 1

     if overwrite and len(_a			) > 0:
          # Warn the user a file has been modified.
          print(F'Detected changes, rewriting {filename}.'			)
          with open(_a					,   """w"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
               f.writelines(_a			)
     return diffs






def 							_lowerCAmelCase (  _a   :					bool = False			)	->			List[Any]:
     lowerCAmelCase_					:       Optional[Any]    =		glob.glob(os.path.join(_a					,   """**/*.py"""			)					,   recursive=_a			)
     lowerCAmelCase_					:       Union[str, Any]    =		[]
     for filename in all_files:
          lowerCAmelCase_					:       Union[str, Any]    =		is_copy_consistent(_a					,   _a			)
          diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(_a			) > 0:
          lowerCAmelCase_					:       List[Any]    =		"""\n""".join(_a			)
          raise Exception(
              """Found the following copy inconsistencies:\n"""
              + diff
              + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""			)


if __name__ == "__main__":
      UpperCAmelCase_					:   Optional[int] 							=						argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      UpperCAmelCase_					:   List[str] 							=						parser.parse_args()

      check_copies(args.fix_and_overwrite)
