




"""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        lowercase_      ( __UpperCAmelCase      )						->				str:
      lowerCAmelCase__			: Optional[Any]  					=       fname.split(os.path.sep      )[-1]
      return re.search(R"""^(.*)_\d+\.jpg$"""				,		__UpperCAmelCase      ).groups()[0]


class _lowerCamelCase				(     a_						):






   def __init__(		self   :		Tuple    ,	UpperCamelCase   :		str    ,	UpperCamelCase   :		str=None    ,	UpperCamelCase   :		Any=None    ) ->						Dict:

         """simple docstring"""
         lowerCAmelCase__			: Optional[Any]  					=       file_names
         lowerCAmelCase__			: List[Any]  					=       image_transform
         lowerCAmelCase__			: int  					=       label_to_id






   def __len__(		self   :		Any    ) ->						str:

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






   def __getitem__(		self   :		str    ,	UpperCamelCase   :		Optional[Any]    ) ->						Optional[int]:

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






def        lowercase_      ( __UpperCAmelCase				,		__UpperCAmelCase      )						->				Any:







      if args.with_tracking:
            lowerCAmelCase__			: List[str]  					=       Accelerator(
                cpu=args.cpu				,		mixed_precision=args.mixed_precision				,		log_with="""all"""				,		project_dir=args.project_dir      )
      else:
            lowerCAmelCase__			: int  					=       Accelerator(cpu=args.cpu				,		mixed_precision=args.mixed_precision      )

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase__			: Any  					=       config["lr"]
      lowerCAmelCase__			: Union[str, Any]  					=       int(config["""num_epochs"""]      )
      lowerCAmelCase__			: Optional[int]  					=       int(config["""seed"""]      )
      lowerCAmelCase__			: Dict  					=       int(config["""batch_size"""]      )
      lowerCAmelCase__			: int  					=       config["image_size"]
      if not isinstance(__UpperCAmelCase				,		(list, tuple)      ):
            lowerCAmelCase__			: int  					=       (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__			: List[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__			: Tuple  					=       None

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

      # Grab all the image filenames
      lowerCAmelCase__			: Optional[int]  					=       [os.path.join(args.data_dir				,		__UpperCAmelCase      ) for fname in os.listdir(args.data_dir      ) if fname.endswith(""".jpg"""      )]

      # Build the label correspondences
      lowerCAmelCase__			: List[str]  					=       [extract_label(__UpperCAmelCase      ) for fname in file_names]
      lowerCAmelCase__			: List[str]  					=       list(set(__UpperCAmelCase      )      )
      id_to_label.sort()
      lowerCAmelCase__			: Optional[Any]  					=       {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
      lowerCAmelCase__			: Any  					=       np.random.permutation(len(__UpperCAmelCase      )      )
      lowerCAmelCase__			: Dict  					=       int(0.8 * len(__UpperCAmelCase      )      )
      lowerCAmelCase__			: Any  					=       random_perm[:cut]
      lowerCAmelCase__			: Union[str, Any]  					=       random_perm[cut:]

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

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

      # Instantiate dataloaders.
      lowerCAmelCase__			: Optional[Any]  					=       DataLoader(__UpperCAmelCase				,		shuffle=__UpperCAmelCase				,		batch_size=__UpperCAmelCase				,		num_workers=4      )
      lowerCAmelCase__			: 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)
      lowerCAmelCase__			: int  					=       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).
      lowerCAmelCase__			: 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__			: Tuple  					=       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__			: int  					=       torch.tensor(model.default_cfg["""std"""]      )[None, :, None, None].to(accelerator.device      )

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

      # Instantiate learning rate scheduler
      lowerCAmelCase__			: 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.
      lowerCAmelCase__			: Optional[Any]  					=       accelerator.prepare(
          __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase      )
      # We need to keep track of how many total steps we have iterated over
      lowerCAmelCase__			: Optional[int]  					=       0
      # We also need to keep track of the starting epoch so files are named properly
      lowerCAmelCase__			: 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      )
                  lowerCAmelCase__			: Any  					=       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__			: Optional[Any]  					=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCAmelCase__			: List[str]  					=       os.path.splitext(__UpperCAmelCase      )[0]

            if "epoch" in training_difference:
                  lowerCAmelCase__			: Dict  					=       int(training_difference.replace("""epoch_"""				,		""""""      )      ) + 1
                  lowerCAmelCase__			: str  					=       None
            else:
                  lowerCAmelCase__			: int  					=       int(training_difference.replace("""step_"""				,		""""""      )      )
                  lowerCAmelCase__			: 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:
                  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__			: Union[str, Any]  					=       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
                  lowerCAmelCase__			: Optional[Any]  					=       train_dataloader
            for batch in active_dataloader:
                  # 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__			: int  					=       (batch["image"] - mean) / std
                  lowerCAmelCase__			: Optional[Any]  					=       model(__UpperCAmelCase      )
                  lowerCAmelCase__			: str  					=       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      ):
                        lowerCAmelCase__			: List[str]  					=       f"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCAmelCase__			: str  					=       os.path.join(args.output_dir				,		__UpperCAmelCase      )
                              accelerator.save_state(__UpperCAmelCase      )
            model.eval()
            lowerCAmelCase__			: List[Any]  					=       0
            lowerCAmelCase__			: Dict  					=       0
            for step, batch in enumerate(__UpperCAmelCase      ):
                  # 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__			: List[str]  					=       (batch["image"] - mean) / std
                  with torch.no_grad():
                        lowerCAmelCase__			: List[str]  					=       model(__UpperCAmelCase      )
                  lowerCAmelCase__			: Optional[int]  					=       outputs.argmax(dim=-1      )
                  lowerCAmelCase__			: List[Any]  					=       accelerator.gather_for_metrics((predictions, batch["""label"""])      )
                  lowerCAmelCase__			: List[Any]  					=       predictions == references
                  num_elems += accurate_preds.shape[0]
                  accurate += accurate_preds.long().sum()

            lowerCAmelCase__			: Optional[Any]  					=       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":
                  lowerCAmelCase__			: List[str]  					=       f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCAmelCase__			: int  					=       os.path.join(args.output_dir				,		__UpperCAmelCase      )
                  accelerator.save_state(__UpperCAmelCase      )

      if args.with_tracking:
            accelerator.end_training()



def        lowercase_      ( )						->				int:
      lowerCAmelCase__			: List[str]  					=       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"""				,		)
      lowerCAmelCase__			: List[str]  					=       parser.parse_args()
      lowerCAmelCase__			: Optional[int]  					=       {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(__UpperCAmelCase				,		__UpperCAmelCase      )


if __name__ == "__main__":
  main()









"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="data2vec-vision"

       def __init__(						self		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=224		,     snake_case__=16		,     snake_case__=3		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=True		,     snake_case__=[3, 5, 7, 11]		,     snake_case__=[1, 2, 3, 6]		,     snake_case__=True		,     snake_case__=0.4		,     snake_case__=256		,     snake_case__=1		,     snake_case__=False		,     snake_case__=255		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Tuple								=							hidden_size
         lowerCAmelCase     :     List[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Tuple								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Dict								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Dict								=							layer_norm_eps

         lowerCAmelCase     :     Optional[int]								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     Union[str, Any]								=							use_mask_token
         lowerCAmelCase     :     str								=							use_absolute_position_embeddings
         lowerCAmelCase     :     Any								=							use_relative_position_bias
         lowerCAmelCase     :     List[str]								=							use_shared_relative_position_bias
         lowerCAmelCase     :     str								=							layer_scale_init_value
         lowerCAmelCase     :     Union[str, Any]								=							drop_path_rate
         lowerCAmelCase     :     Any								=							use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase     :     Optional[int]								=							out_indices
         lowerCAmelCase     :     Union[str, Any]								=							pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase     :     str								=							use_auxiliary_head
         lowerCAmelCase     :     int								=							auxiliary_loss_weight
         lowerCAmelCase     :     Tuple								=							auxiliary_channels
         lowerCAmelCase     :     List[str]								=							auxiliary_num_convs
         lowerCAmelCase     :     Tuple								=							auxiliary_concat_input
         lowerCAmelCase     :     List[str]								=							semantic_loss_ignore_index


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4





