code: 
  
  
  
  
  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_       (	__a )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =fname.split(os.path.sep )[-1]
  							return re.search(r'''^(.*)_\d+\.jpg$'''	,	__a ).groups()[0]
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[str]	     =file_names
  											SCREAMING_SNAKE_CASE			: Any	     =image_transform
  											SCREAMING_SNAKE_CASE			: str	     =label_to_id
  
  
  
  
  				def __len__( self							)    ->      Dict:
  											return len(self.file_names							)
  
  
  
  
  
  
  
  				def __getitem__( self		,		snake_case_							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.file_names[idx]
  											SCREAMING_SNAKE_CASE			: int	     =PIL.Image.open(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =raw_image.convert('''RGB'''							)
  											if self.image_transform is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.image_transform(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =extract_label(snake_case_							)
  											if self.label_to_id is not None:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =self.label_to_id[label]
  											return {"image": image, "label": label}
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision )
  
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							SCREAMING_SNAKE_CASE			: Tuple	     =config['''lr''']
  							SCREAMING_SNAKE_CASE			: List[Any]	     =int(config['''num_epochs'''] )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =int(config['''seed'''] )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =int(config['''batch_size'''] )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =config['''image_size''']
  							if not isinstance(__a	,	(list, tuple) ):
  														SCREAMING_SNAKE_CASE			: Dict	     =(image_size, image_size)
  
  							# Grab all the image filenames
  							SCREAMING_SNAKE_CASE			: int	     =[os.path.join(args.data_dir	,	__a ) for fname in os.listdir(args.data_dir ) if fname.endswith('''.jpg''' )]
  
  							# Build the label correspondences
  							SCREAMING_SNAKE_CASE			: List[str]	     =[extract_label(__a ) for fname in file_names]
  							SCREAMING_SNAKE_CASE			: int	     =list(set(__a ) )
  							id_to_label.sort()
  							SCREAMING_SNAKE_CASE			: Optional[int]	     ={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
  							SCREAMING_SNAKE_CASE			: int	     =np.random.permutation(len(__a ) )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =int(0.8 * len(__a ) )
  							SCREAMING_SNAKE_CASE			: Tuple	     =random_perm[:cut]
  							SCREAMING_SNAKE_CASE			: Tuple	     =random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							SCREAMING_SNAKE_CASE			: str	     =Compose([RandomResizedCrop(__a	,	scale=(0.5, 1.0) ), ToTensor()] )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =PetsDataset(
  							    [file_names[i] for i in train_split]	,	image_transform=__a	,	label_to_id=__a )
  
  							# For evaluation, we use a deterministic Resize
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Compose([Resize(__a ), ToTensor()] )
  							SCREAMING_SNAKE_CASE			: int	     =PetsDataset([file_names[i] for i in eval_split]	,	image_transform=__a	,	label_to_id=__a )
  
  							# Instantiate dataloaders.
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a	,	num_workers=4 )
  							SCREAMING_SNAKE_CASE			: List[str]	     =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)
  							SCREAMING_SNAKE_CASE			: List[str]	     =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).
  							SCREAMING_SNAKE_CASE			: List[str]	     =model.to(accelerator.device )
  
  							# Freezing the base model
  							for param in model.parameters():
  														SCREAMING_SNAKE_CASE			: Dict	     =False
  							for param in model.get_classifier().parameters():
  														SCREAMING_SNAKE_CASE			: str	     =True
  
  							# We normalize the batches of images to be a bit faster.
  							SCREAMING_SNAKE_CASE			: List[str]	     =torch.tensor(model.default_cfg['''mean'''] )[None, :, None, None].to(accelerator.device )
  							SCREAMING_SNAKE_CASE			: List[str]	     =torch.tensor(model.default_cfg['''std'''] )[None, :, None, None].to(accelerator.device )
  
  							# Instantiate optimizer
  							SCREAMING_SNAKE_CASE			: Any	     =torch.optim.Adam(params=model.parameters()	,	lr=lr / 25 )
  
  							# Instantiate learning rate scheduler
  							SCREAMING_SNAKE_CASE			: Tuple	     =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.
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: int	     =accelerator.prepare(
  							    __a	,	__a	,	__a	,	__a	,	__a )
  
  							# Now we train the model
  							for epoch in range(__a ):
  														model.train()
  														for step, batch in enumerate(__a ):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					SCREAMING_SNAKE_CASE			: str	     ={k: v.to(accelerator.device ) for k, v in batch.items()}
  																					SCREAMING_SNAKE_CASE			: str	     =(batch['''image'''] - mean) / std
  																					SCREAMING_SNAKE_CASE			: Any	     =model(__a )
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =torch.nn.functional.cross_entropy(__a	,	batch['''label'''] )
  																					accelerator.backward(__a )
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														SCREAMING_SNAKE_CASE			: int	     =0
  														SCREAMING_SNAKE_CASE			: int	     =0
  														for _, batch in enumerate(__a ):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					SCREAMING_SNAKE_CASE			: str	     ={k: v.to(accelerator.device ) for k, v in batch.items()}
  																					SCREAMING_SNAKE_CASE			: List[str]	     =(batch['''image'''] - mean) / std
  																					with torch.no_grad():
  																												SCREAMING_SNAKE_CASE			: str	     =model(__a )
  																					SCREAMING_SNAKE_CASE			: str	     =outputs.argmax(dim=-1 )
  																					SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[str]	     =accelerator.gather_for_metrics((predictions, batch['''label''']) )
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														SCREAMING_SNAKE_CASE			: Any	     =accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
  def 						lowerCAmelCase_       (	)       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =argparse.ArgumentParser(description='''Simple example of training script.''' )
  							parser.add_argument('''--data_dir'''	,	required=__a	,	help='''The data folder on disk.''' )
  							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(
  							    '''--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('''--cpu'''	,	action='''store_true'''	,	help='''If passed, will train on the CPU.''' )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =parser.parse_args()
  							SCREAMING_SNAKE_CASE			: int	     ={'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
  							training_function(__a	,	__a )
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import argparse
  import json
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from typing import List
  
  import torch
  import torch.nn as nn
  from huggingface_hub import cached_download, hf_hub_download
  from torch import Tensor
  
  from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
  from transformers.models.deprecated.van.modeling_van import VanLayerScaling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _A							    =	logging.get_logger(__name__)
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Tuple	     =len(list(m.modules()							)							) == 1 or isinstance(snake_case_		,		nn.Convad							) or isinstance(snake_case_		,		nn.BatchNormad							)
  											if has_not_submodules:
  																		if not isinstance(snake_case_		,		snake_case_							):
  																									self.traced.append(snake_case_							)
  
  
  
  
  				def __call__( self		,		snake_case_							)    ->      List[Any]:
  											for m in self.module.modules():
  																		self.handles.append(m.register_forward_hook(self._forward_hook							)							)
  											self.module(snake_case_							)
  											[x.remove() for x in self.handles]
  											return self
  
  
  
  
  
  
  
  				@property
  				def     __a		( self							)    ->      int:
  											# check the len of the state_dict keys to see if we have learnable params
  											return list(filter(lambda snake_case_							: len(list(x.state_dict().keys()							)							) > 0		,		self.traced							)							)
  
  
  
  
  
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    0
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  
  
  
  
  				def __call__( self		,		snake_case_							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =Tracker(self.dest							)(snake_case_							).parametrized
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =Tracker(self.src							)(snake_case_							).parametrized
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.src_skip		,		snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: Dict	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.dest_skip		,		snake_case_							)							)
  
  											if len(snake_case_							) != len(snake_case_							):
  																		raise Exception(
  																		    F'Numbers of operations are different. Source module has {len(snake_case_							)} operations while'
  																		    F' destination module has {len(snake_case_							)}.'							)
  
  											for dest_m, src_m in zip(snake_case_		,		snake_case_							):
  																		dest_m.load_state_dict(src_m.state_dict()							)
  																		if self.verbose == 1:
  																									print(F'Transfered from={src_m} to={dest_m}'							)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			nn.Module:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =from_model.state_dict()
  							SCREAMING_SNAKE_CASE			: List[str]	     =our_model.state_dict()
  							SCREAMING_SNAKE_CASE			: int	     =our_model.config
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							for stage_idx in range(len(config.hidden_sizes ) ):
  														for block_id in range(config.depths[stage_idx] ):
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =f'block{stage_idx + 1}.{block_id}.layer_scale_1'
  																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
  																					all_keys.append((from_key, to_key) )
  																					SCREAMING_SNAKE_CASE			: int	     =f'block{stage_idx + 1}.{block_id}.layer_scale_2'
  																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'
  
  																					all_keys.append((from_key, to_key) )
  
  							for from_key, to_key in all_keys:
  														SCREAMING_SNAKE_CASE			: List[Any]	     =from_state_dict.pop(__a )
  
  							our_model.load_state_dict(__a )
  							return our_model
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a = True	,	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							print(f'Downloading weights for {name}...' )
  							SCREAMING_SNAKE_CASE			: Dict	     =cached_download(__a )
  							print(f'Converting {name}...' )
  							SCREAMING_SNAKE_CASE			: Dict	     =torch.load(__a )['''state_dict''']
  							from_model.load_state_dict(__a )
  							from_model.eval()
  							with torch.no_grad():
  														SCREAMING_SNAKE_CASE			: int	     =VanForImageClassification(__a ).eval()
  														SCREAMING_SNAKE_CASE			: Tuple	     =ModuleTransfer(src=__a	,	dest=__a )
  														SCREAMING_SNAKE_CASE			: Any	     =torch.randn((1, 3, 224, 224) )
  														module_transfer(__a )
  														SCREAMING_SNAKE_CASE			: str	     =copy_parameters(__a	,	__a )
  
  							if not torch.allclose(from_model(__a )	,	our_model(__a ).logits ):
  														raise ValueError('''The model logits don\'t match the original one.''' )
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =name
  							print(__a )
  
  							if push_to_hub:
  														our_model.push_to_hub(
  														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=__a	,	)
  
  														# we can use the convnext one
  														SCREAMING_SNAKE_CASE			: Tuple	     =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
  														image_processor.push_to_hub(
  														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=__a	,	)
  
  														print(f'Pushed {checkpoint_name}' )
  def 						lowerCAmelCase_       (	__a	,	__a = None	,	__a = True )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''imagenet-1k-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =1000
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =num_labels
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =json.load(open(hf_hub_download(__a	,	__a	,	repo_type='''dataset''' )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={int(__a ): v for k, v in idalabel.items()}
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Tuple	     ={v: k for k, v in idalabel.items()}
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =partial(__a	,	num_labels=__a	,	idalabel=__a	,	labelaid=__a )
  
  							SCREAMING_SNAKE_CASE			: int	     ={
  							    '''van-tiny''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[32, 64, 160, 256]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-small''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-base''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-large''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							}
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={
  							    '''van-tiny''': van_tiny,
  							    '''van-small''': van_small,
  							    '''van-base''': van_base,
  							    '''van-large''': van_large,
  							}
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     ={
  							    '''van-tiny''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
  							    ),
  							    '''van-small''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
  							    ),
  							    '''van-base''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
  							    ),
  							    '''van-large''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
  							    ),
  							}
  
  							if model_name:
  														convert_weight_and_push(
  														    __a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
  							else:
  														for model_name, config in names_to_config.items():
  																					convert_weight_and_push(
  																					    __a	,	__a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
  
  
  if __name__ == "__main__":
  							_A							    =	argparse.ArgumentParser()
  							# Required parameters
  							parser.add_argument(
  							    """--model-name""",
  							    default=None,
  							    type=str,
  							    help=(
  							        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
  							        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
  							    ),
  							)
  							parser.add_argument(
  							    """--pytorch_dump_folder_path""",
  							    default=None,
  							    type=Path,
  							    required=True,
  							    help="""Path to the output PyTorch model directory.""",
  							)
  							parser.add_argument(
  							    """--van_dir""",
  							    required=True,
  							    type=Path,
  							    help=(
  							        """A path to VAN's original implementation directory. You can download from here:"""
  							        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
  							    ),
  							)
  							parser.add_argument(
  							    """--push_to_hub""",
  							    default=True,
  							    type=bool,
  							    required=False,
  							    help="""If True, push model and image processor to the hub.""",
  							)
  
  							_A							    =	parser.parse_args()
  							_A							    =	args.pytorch_dump_folder_path
  							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  							_A							    =	args.van_dir
  							# append the path to the parents to maskformer dir
  							sys.path.append(str(van_dir.parent))
  							from van.models.van import van_base, van_large, van_small, van_tiny
  
  							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
  
  
  
  
  
  
  