


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   (							_UpperCamelCase		)    ->				Dict:






      '''simple docstring'''

      lowerCamelCase__:						Optional[int]							   = fname.split(os.path.sep		)[-1]
      return re.search(r"""^(.*)_\d+\.jpg$"""       ,			lowerCamelCase_		).groups()[0]



class 	lowerCamelCase__			(      _a      ):




   def __init__(   self						:						List[str]    ,    __a						:						Any    ,    __a						:						Optional[int]=None    ,    __a						:						List[Any]=None							):




         '''simple docstring'''







         lowerCamelCase__:						int							   = file_names
         lowerCamelCase__:						List[str]							   = image_transform
         lowerCamelCase__:						List[Any]							   = label_to_id




   def __len__(   self						:						Dict							):




         '''simple docstring'''







         return len(self.file_names							)




   def __getitem__(   self						:						int    ,    __a						:						List[Any]							):




         '''simple docstring'''







         lowerCamelCase__:						Tuple							   = self.file_names[idx]
         lowerCamelCase__:						Tuple							   = PIL.Image.open(__a							)
         lowerCamelCase__:						Any							   = raw_image.convert("""RGB"""							)
         if self.image_transform is not None:
               lowerCamelCase__:						Dict							   = self.image_transform(__a							)
         lowerCamelCase__:						Dict							   = extract_label(__a							)
         if self.label_to_id is not None:
               lowerCamelCase__:						List[Any]							   = self.label_to_id[label]
         return {"image": image, "label": label}

def 		__lowerCAmelCase   (							_UpperCamelCase       ,			_UpperCamelCase		)    ->				Union[str, Any]:






      '''simple docstring'''

      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__:						Tuple							   = Accelerator(cpu=args.cpu       ,			mixed_precision=args.mixed_precision		)

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

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

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

      # Build the label correspondences
      lowerCamelCase__:						List[str]							   = [extract_label(lowerCamelCase_		) for fname in file_names]
      lowerCamelCase__:						Dict							   = list(set(lowerCamelCase_		)		)
      id_to_label.sort()
      lowerCamelCase__:						Optional[int]							   = {lbl: i for i, lbl in enumerate(lowerCamelCase_		)}

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

      # Split our filenames between train and validation
      lowerCamelCase__:						Optional[Any]							   = np.random.permutation(len(lowerCamelCase_		)		)
      lowerCamelCase__:						str							   = int(0.8 * len(lowerCamelCase_		)		)
      lowerCamelCase__:						Any							   = random_perm[:cut]
      lowerCamelCase__:						Optional[int]							   = random_perm[cut:]

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

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

      # Instantiate dataloaders.
      lowerCamelCase__:						List[str]							   = DataLoader(lowerCamelCase_       ,			shuffle=lowerCamelCase_       ,			batch_size=lowerCamelCase_       ,			num_workers=4		)
      lowerCamelCase__:						List[Any]							   = DataLoader(lowerCamelCase_       ,			shuffle=lowerCamelCase_       ,			batch_size=lowerCamelCase_       ,			num_workers=4		)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase__:						List[str]							   = create_model("""resnet50d"""       ,			pretrained=lowerCamelCase_       ,			num_classes=len(lowerCamelCase_		)		)

      # 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__:						Dict							   = False
      for param in model.get_classifier().parameters():
            lowerCamelCase__:						Any							   = True

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

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

      # Instantiate learning rate scheduler
      lowerCamelCase__:						List[Any]							   = OneCycleLR(optimizer=lowerCamelCase_       ,			max_lr=lowerCamelCase_       ,			epochs=lowerCamelCase_       ,			steps_per_epoch=len(lowerCamelCase_		)		)

      # 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__:						Union[str, Any]							   = accelerator.prepare(
          lowerCamelCase_       ,			lowerCamelCase_       ,			lowerCamelCase_       ,			lowerCamelCase_       ,			lowerCamelCase_		)
      # 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__:						List[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[Any]							   = os.path.basename(args.resume_from_checkpoint		)
            else:
                  # Get the most recent checkpoint
                  lowerCamelCase__:						List[str]							   = [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
                  dirs.sort(key=os.path.getctime		)
                  lowerCamelCase__:						str							   = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCamelCase__:						Union[str, Any]							   = os.path.splitext(lowerCamelCase_		)[0]

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

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

            lowerCamelCase__:						Union[str, 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(lowerCamelCase_		),
                          """epoch""": epoch,
                      }       ,			step=lowerCamelCase_       ,			)
            if checkpointing_steps == "epoch":
                  lowerCamelCase__:						Any							   = f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase__:						Dict							   = os.path.join(args.output_dir       ,			lowerCamelCase_		)
                  accelerator.save_state(lowerCamelCase_		)

      if args.with_tracking:
            accelerator.end_training()

def 		__lowerCAmelCase   (							)    ->				Optional[Any]:






      '''simple docstring'''

      lowerCamelCase__:						Tuple							   = argparse.ArgumentParser(description="""Simple example of training script."""		)
      parser.add_argument("""--data_dir"""       ,			required=lowerCamelCase_       ,			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=lowerCamelCase_       ,			default=lowerCamelCase_       ,			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=lowerCamelCase_       ,			default=lowerCamelCase_       ,			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=lowerCamelCase_       ,			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=lowerCamelCase_       ,			default=lowerCamelCase_       ,			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=lowerCamelCase_       ,			default="""logs"""       ,			help="""Location on where to store experiment tracking logs` and relevent project information"""       ,			)
      lowerCamelCase__:						str							   = parser.parse_args()
      lowerCamelCase__:						List[str]							   = {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
      training_function(lowerCamelCase_       ,			lowerCamelCase_		)


if __name__ == "__main__":
      main()










import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


