





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_	(		A__				)  ->		Dict:
       a__						:	Any =				fname.split(os.path.sep				)[-1]
       return re.search(R'^(.*)_\d+\.jpg$'		,      A__				).groups()[0]





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=None					,  lowercase=None)			->						List[str]:







          '''simple docstring'''

          a__						:	List[Any] =				file_names
          a__						:	List[Any] =				image_transform
          a__						:	Optional[int] =				label_to_id

   def __len__(					self)			->						List[Any]:







          '''simple docstring'''

          return len(self.file_names)

   def __getitem__(					self					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Any =				self.file_names[idx]
          a__						:	Optional[Any] =				PIL.Image.open(lowercase)
          a__						:	List[str] =				raw_image.convert('RGB')
          if self.image_transform is not None:
                 a__						:	List[str] =				self.image_transform(lowercase)
          a__						:	str =				extract_label(lowercase)
          if self.label_to_id is not None:
                 a__						:	int =				self.label_to_id[label]
          return {"image": image, "label": label}



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

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

       # Grab all the image filenames
       a__						:	Any =				[os.path.join(args.data_dir		,      A__				) for fname in os.listdir(args.data_dir				) if fname.endswith('.jpg'				)]

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

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

       # Split our filenames between train and validation
       a__						:	Tuple =				np.random.permutation(len(A__				)				)
       a__						:	List[str] =				int(0.8 * len(A__				)				)
       a__						:	Optional[int] =				random_perm[:cut]
       a__						:	Tuple =				random_perm[cut:]

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

       # For evaluation, we use a deterministic Resize
       a__						:	List[Any] =				Compose([Resize(A__				), ToTensor()]				)
       a__						:	Dict =				PetsDataset([file_names[i] for i in eval_split]		,      image_transform=A__		,      label_to_id=A__				)

       # Instantiate dataloaders.
       a__						:	List[str] =				DataLoader(A__		,      shuffle=A__		,      batch_size=A__		,      num_workers=4				)
       a__						:	Tuple =				DataLoader(A__		,      shuffle=A__		,      batch_size=A__		,      num_workers=4				)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__						:	Any =				create_model('resnet50d'		,      pretrained=A__		,      num_classes=len(A__				)				)

       # 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).
       a__						:	List[Any] =				model.to(accelerator.device				)

       # Freezing the base model
       for param in model.parameters():
              a__						:	List[str] =				False
       for param in model.get_classifier().parameters():
              a__						:	List[str] =				True

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

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

       # Instantiate learning rate scheduler
       a__						:	Any =				OneCycleLR(optimizer=A__		,      max_lr=A__		,      epochs=A__		,      steps_per_epoch=len(A__				)				)

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

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

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

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

       if args.with_tracking:
              accelerator.end_training()



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


if __name__ == "__main__":
    main()









import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


