"""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      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   fname.split(os.path.sep		)[-1]
  return re.search(R"""^(.*)_\d+\.jpg$""" ,lowercase		).groups()[0]


class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	str ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int]=None ,					__lowerCAmelCase						:	List[str]=None       ):
      _UpperCAmelCase    			=   file_names
      _UpperCAmelCase    			=   image_transform
      _UpperCAmelCase    			=   label_to_id


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


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






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # Initialize accelerator
  if args.with_tracking:
    _UpperCAmelCase    			=   Accelerator(
        cpu=args.cpu ,mixed_precision=args.mixed_precision ,log_with="""all""" ,project_dir=args.project_dir		)
  else:
    _UpperCAmelCase    			=   Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision		)

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

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

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

  # Build the label correspondences
  _UpperCAmelCase    			=   [extract_label(lowercase		) for fname in file_names]
  _UpperCAmelCase    			=   list(set(lowercase		)		)
  id_to_label.sort()
  _UpperCAmelCase    			=   {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
  _UpperCAmelCase    			=   np.random.permutation(len(lowercase		)		)
  _UpperCAmelCase    			=   int(0.8 * len(lowercase		)		)
  _UpperCAmelCase    			=   random_perm[:cut]
  _UpperCAmelCase    			=   random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCAmelCase    			=   Compose([RandomResizedCrop(lowercase ,scale=(0.5, 1.0)		), ToTensor()]		)
  _UpperCAmelCase    			=   PetsDataset(
      [file_names[i] for i in train_split] ,image_transform=lowercase ,label_to_id=lowercase		)

  # For evaluation, we use a deterministic Resize
  _UpperCAmelCase    			=   Compose([Resize(lowercase		), ToTensor()]		)
  _UpperCAmelCase    			=   PetsDataset([file_names[i] for i in eval_split] ,image_transform=lowercase ,label_to_id=lowercase		)

  # Instantiate dataloaders.
  _UpperCAmelCase    			=   DataLoader(lowercase ,shuffle=lowercase ,batch_size=lowercase ,num_workers=4		)
  _UpperCAmelCase    			=   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)
  _UpperCAmelCase    			=   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).
  _UpperCAmelCase    			=   model.to(accelerator.device		)

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

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

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

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

    if "epoch" in training_difference:
      _UpperCAmelCase    			=   int(training_difference.replace("""epoch_""" ,""""""		)		) + 1
      _UpperCAmelCase    			=   None
    else:
      _UpperCAmelCase    			=   int(training_difference.replace("""step_""" ,""""""		)		)
      _UpperCAmelCase    			=   resume_step // len(lowercase		)
      resume_step -= starting_epoch * len(lowercase		)

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

    _UpperCAmelCase    			=   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(lowercase		),
              """epoch""": epoch,
          } ,step=lowercase ,)
    if checkpointing_steps == "epoch":
      _UpperCAmelCase    			=   f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _UpperCAmelCase    			=   os.path.join(args.output_dir ,lowercase		)
      accelerator.save_state(lowercase		)

  if args.with_tracking:
    accelerator.end_training()






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   argparse.ArgumentParser(description="""Simple example of training script."""		)
  parser.add_argument("""--data_dir""" ,required=lowercase ,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=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("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU."""		)
  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(
      """--output_dir""" ,type=lowercase ,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=lowercase ,default=lowercase ,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=lowercase ,default="""logs""" ,help="""Location on where to store experiment tracking logs` and relevent project information""" ,)
  _UpperCAmelCase    			=   parser.parse_args()
  _UpperCAmelCase    			=   {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
  training_function(lowercase ,lowercase		)


if __name__ == "__main__":
    main()

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				'realm'


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	List[str]=3_0522 ,					__lowerCAmelCase						:	Tuple=768 ,					__lowerCAmelCase						:	List[Any]=128 ,					__lowerCAmelCase						:	Tuple=12 ,					__lowerCAmelCase						:	List[str]=12 ,					__lowerCAmelCase						:	Union[str, Any]=8 ,					__lowerCAmelCase						:	List[str]=3072 ,					__lowerCAmelCase						:	Union[str, Any]="gelu_new" ,					__lowerCAmelCase						:	str=0.1 ,					__lowerCAmelCase						:	Tuple=0.1 ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	Dict=2 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[int]=1e-1_2 ,					__lowerCAmelCase						:	Any=256 ,					__lowerCAmelCase						:	Union[str, Any]=10 ,					__lowerCAmelCase						:	Dict=1e-3 ,					__lowerCAmelCase						:	str=5 ,					__lowerCAmelCase						:	str=320 ,					__lowerCAmelCase						:	Tuple=1335_3718 ,					__lowerCAmelCase						:	Optional[Any]=5000 ,					__lowerCAmelCase						:	int=1 ,					__lowerCAmelCase						:	Dict=0 ,					__lowerCAmelCase						:	Any=2 ,					**__lowerCAmelCase						:	Optional[int] ,					):
      super().__init__(pad_token_id=__lowerCAmelCase ,					bos_token_id=__lowerCAmelCase ,					eos_token_id=__lowerCAmelCase ,					**__lowerCAmelCase       )

      # Common config
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   max_position_embeddings
      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   retriever_proj_size
      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   num_candidates
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   type_vocab_size
      _UpperCAmelCase    			=   layer_norm_eps

      # Reader config
      _UpperCAmelCase    			=   span_hidden_size
      _UpperCAmelCase    			=   max_span_width
      _UpperCAmelCase    			=   reader_layer_norm_eps
      _UpperCAmelCase    			=   reader_beam_size
      _UpperCAmelCase    			=   reader_seq_len

      # Retrieval config
      _UpperCAmelCase    			=   num_block_records
      _UpperCAmelCase    			=   searcher_beam_size
