code: 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 		snake_case__  (		lowercase				):
     lowerCAmelCase_:       Union[str, Any]         =  fname.split(os.path.sep				)[-1]
     return re.search(r"^(.*)_\d+\.jpg$"       ,							lowercase				).groups()[0]
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=None					,	lowerCamelCase__=None   ):
         lowerCAmelCase_:       List[str]         =  file_names
         lowerCAmelCase_:       Dict         =  image_transform
         lowerCAmelCase_:       Optional[Any]         =  label_to_id
  
  
  
  
      def __len__(						self   ):
         return len(self.file_names   )
  
  
  
  
      def __getitem__(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[int]         =  self.file_names[idx]
         lowerCAmelCase_:       Union[str, Any]         =  PIL.Image.open(lowerCamelCase__   )
         lowerCAmelCase_:       Optional[int]         =  raw_image.convert("RGB"   )
         if self.image_transform is not None:
            lowerCAmelCase_:       Tuple         =  self.image_transform(lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  extract_label(lowerCamelCase__   )
         if self.label_to_id is not None:
            lowerCAmelCase_:       str         =  self.label_to_id[label]
         return {"image": image, "label": label}
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase				):
     # Initialize accelerator
     lowerCAmelCase_:       Dict         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
  
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_:       str         =  config["lr"]
     lowerCAmelCase_:       Union[str, Any]         =  int(config["num_epochs"]				)
     lowerCAmelCase_:       Any         =  int(config["seed"]				)
     lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)
     lowerCAmelCase_:       Any         =  config["image_size"]
     if not isinstance(lowercase       ,							(list, tuple)				):
        lowerCAmelCase_:       Tuple         =  (image_size, image_size)
  
     # Grab all the image filenames
     lowerCAmelCase_:       Tuple         =  [os.path.join(args.data_dir       ,							lowercase				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]
  
     # Build the label correspondences
     lowerCAmelCase_:       List[str]         =  [extract_label(lowercase				) for fname in file_names]
     lowerCAmelCase_:       List[Any]         =  list(set(lowercase				)				)
     id_to_label.sort()
     lowerCAmelCase_:       Tuple         =  {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
     lowerCAmelCase_:       Union[str, Any]         =  np.random.permutation(len(lowercase				)				)
     lowerCAmelCase_:       int         =  int(0.8 * len(lowercase				)				)
     lowerCAmelCase_:       Tuple         =  random_perm[:cut]
     lowerCAmelCase_:       Any         =  random_perm[cut:]
  
     # For training we use a simple RandomResizedCrop
     lowerCAmelCase_:       Union[str, Any]         =  Compose([RandomResizedCrop(lowercase       ,							scale=(0.5, 1.0)				), ToTensor()]				)
     lowerCAmelCase_:       Optional[int]         =  PetsDataset(
         [file_names[i] for i in train_split]       ,							image_transform=lowercase       ,							label_to_id=lowercase				)
  
     # For evaluation, we use a deterministic Resize
     lowerCAmelCase_:       Union[str, Any]         =  Compose([Resize(lowercase				), ToTensor()]				)
     lowerCAmelCase_:       Dict         =  PetsDataset([file_names[i] for i in eval_split]       ,							image_transform=lowercase       ,							label_to_id=lowercase				)
  
     # Instantiate dataloaders.
     lowerCAmelCase_:       List[Any]         =  DataLoader(lowercase       ,							shuffle=lowercase       ,							batch_size=lowercase       ,							num_workers=4				)
     lowerCAmelCase_:       Dict         =  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)
     lowerCAmelCase_:       Optional[int]         =  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).
     lowerCAmelCase_:       Union[str, Any]         =  model.to(accelerator.device				)
  
     # Freezing the base model
     for param in model.parameters():
        lowerCAmelCase_:       Optional[Any]         =  False
     for param in model.get_classifier().parameters():
        lowerCAmelCase_:       Optional[Any]         =  True
  
     # We normalize the batches of images to be a bit faster.
     lowerCAmelCase_:       Dict         =  torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
     lowerCAmelCase_:       List[str]         =  torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)
  
     # Instantiate optimizer
     lowerCAmelCase_:       Any         =  torch.optim.Adam(params=model.parameters()       ,							lr=lr / 25				)
  
     # Instantiate learning rate scheduler
     lowerCAmelCase_:       List[Any]         =  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.
     lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  accelerator.prepare(
         lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)
  
     # Now we train the model
     for epoch in range(lowercase				):
        model.train()
        for step, batch in enumerate(lowercase				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           lowerCAmelCase_:       str         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
           lowerCAmelCase_:       int         =  (batch["image"] - mean) / std
           lowerCAmelCase_:       str         =  model(lowercase				)
           lowerCAmelCase_:       int         =  torch.nn.functional.cross_entropy(lowercase       ,							batch["label"]				)
           accelerator.backward(lowercase				)
           optimizer.step()
           lr_scheduler.step()
           optimizer.zero_grad()
  
        model.eval()
        lowerCAmelCase_:       Tuple         =  0
        lowerCAmelCase_:       Optional[int]         =  0
        for _, batch in enumerate(lowercase				):
           # 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_:       Any         =  (batch["image"] - mean) / std
           with torch.no_grad():
              lowerCAmelCase_:       Optional[int]         =  model(lowercase				)
           lowerCAmelCase_:       List[str]         =  outputs.argmax(dim=-1				)
           lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  accelerator.gather_for_metrics((predictions, batch["label"])				)
           lowerCAmelCase_:       List[str]         =  predictions == references
           num_elems += accurate_preds.shape[0]
           accurate += accurate_preds.long().sum()
  
        lowerCAmelCase_:       int         =  accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''				)
  def 		snake_case__  (		):
     lowerCAmelCase_:       Any         =  argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument("--data_dir"       ,							required=lowercase       ,							help="The data folder on disk."				)
     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(
         "--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("--cpu"       ,							action="store_true"       ,							help="If passed, will train on the CPU."				)
     lowerCAmelCase_:       Optional[int]         =  parser.parse_args()
     lowerCAmelCase_:       Union[str, Any]         =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(lowercase       ,							lowercase				)
  
  
  if __name__ == "__main__":
   main()
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)