





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__							(    SCREAMING_SNAKE_CASE__)   ->    List[str]:
  __snake_case:						Dict   =						fname.split(os.path.sep)[-1]
  return re.search(r"""^(.*)_\d+\.jpg$"""					,					SCREAMING_SNAKE_CASE__).groups()[0]







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







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




       def __len__(  self :    Union[str, Any]		):
         return len(self.file_names		)






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







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  # Initialize accelerator
  if args.with_tracking:
    __snake_case:						str   =						Accelerator(
        cpu=args.cpu					,					mixed_precision=args.mixed_precision					,					log_with="""all"""					,					project_dir=args.project_dir)
  else:
    __snake_case:						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
  __snake_case:						Dict   =						config["""lr"""]
  __snake_case:						List[Any]   =						int(config["""num_epochs"""])
  __snake_case:						Optional[Any]   =						int(config["""seed"""])
  __snake_case:						List[Any]   =						int(config["""batch_size"""])
  __snake_case:						int   =						config["""image_size"""]
  if not isinstance(SCREAMING_SNAKE_CASE__					,					(list, tuple)):
    __snake_case:						Optional[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":
      __snake_case:						int   =						args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      __snake_case:						str   =						int(args.checkpointing_steps)
    else:
      raise ValueError(
          F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
  else:
    __snake_case:						Dict   =						None

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

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

  # Build the label correspondences
  __snake_case:						Tuple   =						[extract_label(SCREAMING_SNAKE_CASE__) for fname in file_names]
  __snake_case:						Dict   =						list(set(SCREAMING_SNAKE_CASE__))
  id_to_label.sort()
  __snake_case:						Optional[Any]   =						{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__)}

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

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

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

  # For evaluation, we use a deterministic Resize
  __snake_case:						Optional[int]   =						Compose([Resize(SCREAMING_SNAKE_CASE__), ToTensor()])
  __snake_case:						List[str]   =						PetsDataset([file_names[i] for i in eval_split]					,					image_transform=SCREAMING_SNAKE_CASE__					,					label_to_id=SCREAMING_SNAKE_CASE__)

  # Instantiate dataloaders.
  __snake_case:						List[str]   =						DataLoader(SCREAMING_SNAKE_CASE__					,					shuffle=SCREAMING_SNAKE_CASE__					,					batch_size=SCREAMING_SNAKE_CASE__					,					num_workers=4)
  __snake_case:						Dict   =						DataLoader(SCREAMING_SNAKE_CASE__					,					shuffle=SCREAMING_SNAKE_CASE__					,					batch_size=SCREAMING_SNAKE_CASE__					,					num_workers=4)

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

  # 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).
  __snake_case:						str   =						model.to(accelerator.device)

  # Freezing the base model
  for param in model.parameters():
    __snake_case:						Dict   =						False
  for param in model.get_classifier().parameters():
    __snake_case:						str   =						True

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

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

  # Instantiate learning rate scheduler
  __snake_case:						Dict   =						OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__					,					max_lr=SCREAMING_SNAKE_CASE__					,					epochs=SCREAMING_SNAKE_CASE__					,					steps_per_epoch=len(SCREAMING_SNAKE_CASE__))

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

    if "epoch" in training_difference:
      __snake_case:						Optional[Any]   =						int(training_difference.replace("""epoch_"""					,					"""""")) + 1
      __snake_case:						Any   =						None
    else:
      __snake_case:						Optional[Any]   =						int(training_difference.replace("""step_"""					,					""""""))
      __snake_case:						Tuple   =						resume_step // len(SCREAMING_SNAKE_CASE__)
      resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__)

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

    __snake_case:						int   =						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(SCREAMING_SNAKE_CASE__),
              """epoch""": epoch,
          }					,					step=SCREAMING_SNAKE_CASE__					,					)
    if checkpointing_steps == "epoch":
      __snake_case:						Dict   =						F'''epoch_{epoch}'''
      if args.output_dir is not None:
        __snake_case:						int   =						os.path.join(args.output_dir					,					SCREAMING_SNAKE_CASE__)
      accelerator.save_state(SCREAMING_SNAKE_CASE__)

  if args.with_tracking:
    accelerator.end_training()







def 			A__							(    )   ->    Optional[Any]:
  __snake_case:						Dict   =						argparse.ArgumentParser(description="""Simple example of training script.""")
  parser.add_argument("""--data_dir"""					,					required=SCREAMING_SNAKE_CASE__					,					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=SCREAMING_SNAKE_CASE__					,					default=SCREAMING_SNAKE_CASE__					,					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=SCREAMING_SNAKE_CASE__					,					default=SCREAMING_SNAKE_CASE__					,					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=SCREAMING_SNAKE_CASE__					,					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=SCREAMING_SNAKE_CASE__					,					default=SCREAMING_SNAKE_CASE__					,					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=SCREAMING_SNAKE_CASE__					,					default="""logs"""					,					help="""Location on where to store experiment tracking logs` and relevent project information"""					,					)
  __snake_case:						Optional[int]   =						parser.parse_args()
  __snake_case:						Optional[int]   =						{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  training_function(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      main()













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """ibert"""




       def __init__(  self :    Dict							,				A :    Union[str, Any]=30_522							,				A :    List[Any]=768							,				A :    List[Any]=12							,				A :    Optional[int]=12							,				A :    Optional[Any]=3_072							,				A :    int="gelu"							,				A :    str=0.1							,				A :    List[Any]=0.1							,				A :    Optional[Any]=512							,				A :    int=2							,				A :    Union[str, Any]=0.02							,				A :    List[str]=1E-12							,				A :    Optional[int]=1							,				A :    Optional[int]=0							,				A :    List[str]=2							,				A :    str="absolute"							,				A :    Any=False							,				A :    Optional[Any]="none"							,				**A :    Any							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)

         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						hidden_size
         __snake_case:						List[str]   =						num_hidden_layers
         __snake_case:						Tuple   =						num_attention_heads
         __snake_case:						List[str]   =						hidden_act
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_dropout_prob
         __snake_case:						List[str]   =						attention_probs_dropout_prob
         __snake_case:						Any   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[str]   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						Optional[int]   =						position_embedding_type
         __snake_case:						str   =						quant_mode
         __snake_case:						Optional[int]   =						force_dequant







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         if self.task == "multiple-choice":
           __snake_case:						List[Any]   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:						List[str]   =						{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]		)






