code: 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()
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class 					UpperCAmelCase_				:
      UpperCAmelCase_			:       nn.Module
      UpperCAmelCase_			:       List[nn.Module]						 =							field(default_factory=_a    )
      UpperCAmelCase_			:       list						 =							field(default_factory=_a    )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Tensor      ,      lowerCAmelCase  :   Tensor   )	-> List[str]:
        a__     		=     len(list(m.modules()   )   ) == 1 or isinstance(lowerCAmelCase      ,      nn.Convad   ) or isinstance(lowerCAmelCase      ,      nn.BatchNormad   )
        if has_not_submodules:
          if not isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
            self.traced.append(lowerCAmelCase   )
  
  
  
  
      def __call__(					self  :   str      ,      lowerCAmelCase  :   Tensor   )	-> Optional[int]:
        for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook   )   )
        self.module(lowerCAmelCase   )
        [x.remove() for x in self.handles]
        return self
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Tuple   )	-> List[Any]:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda lowerCAmelCase   : len(list(x.state_dict().keys()   )   ) > 0      ,      self.traced   )   )
  
  
  
  
  
  
  
  @dataclass
  class 					UpperCAmelCase_				:
      UpperCAmelCase_			:       nn.Module
      UpperCAmelCase_			:       nn.Module
      UpperCAmelCase_			:       int						 =							0
      UpperCAmelCase_			:       List						 =							field(default_factory=_a    )
      UpperCAmelCase_			:       List						 =							field(default_factory=_a    )
  
  
  
  
      def __call__(					self  :   Optional[Any]      ,      lowerCAmelCase  :   Tensor   )	-> Optional[int]:
        a__     		=     Tracker(self.dest   )(lowerCAmelCase   ).parametrized
        a__     		=     Tracker(self.src   )(lowerCAmelCase   ).parametrized
  
        a__     		=     list(filter(lambda lowerCAmelCase   : type(lowerCAmelCase   ) not in self.src_skip      ,      lowerCAmelCase   )   )
        a__     		=     list(filter(lambda lowerCAmelCase   : type(lowerCAmelCase   ) not in self.dest_skip      ,      lowerCAmelCase   )   )
  
        if len(lowerCAmelCase   ) != len(lowerCAmelCase   ):
          raise Exception(
              f"Numbers of operations are different. Source module has {len(lowerCAmelCase   )} operations while"
              f" destination module has {len(lowerCAmelCase   )}."   )
  
        for dest_m, src_m in zip(lowerCAmelCase      ,      lowerCAmelCase   ):
          dest_m.load_state_dict(src_m.state_dict()   )
          if self.verbose == 1:
            print(f"Transfered from={src_m} to={dest_m}"   )
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->nn.Module:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     from_model.state_dict()
    a__     		=     our_model.state_dict()
    a__     		=     our_model.config
    a__     		=     []
    for stage_idx in range(len(config.hidden_sizes					)					):
      for block_id in range(config.depths[stage_idx]					):
        a__     		=     f"block{stage_idx + 1}.{block_id}.layer_scale_1"
        a__     		=     f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"
  
        all_keys.append((from_key, to_key)					)
        a__     		=     f"block{stage_idx + 1}.{block_id}.layer_scale_2"
        a__     		=     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:
      a__     		=     from_state_dict.pop(lowerCamelCase__					)
  
    our_model.load_state_dict(lowerCamelCase__					)
    return our_model
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = True ,				)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    print(f"Downloading weights for {name}..."					)
    a__     		=     cached_download(lowerCamelCase__					)
    print(f"Converting {name}..."					)
    a__     		=     torch.load(lowerCamelCase__					)["state_dict"]
    from_model.load_state_dict(lowerCamelCase__					)
    from_model.eval()
    with torch.no_grad():
      a__     		=     VanForImageClassification(lowerCamelCase__					).eval()
      a__     		=     ModuleTransfer(src=lowerCamelCase__ ,				dest=lowerCamelCase__					)
      a__     		=     torch.randn((1, 3, 2_24, 2_24)					)
      module_transfer(lowerCamelCase__					)
      a__     		=     copy_parameters(lowerCamelCase__ ,				lowerCamelCase__					)
  
    if not torch.allclose(from_model(lowerCamelCase__					) ,				our_model(lowerCamelCase__					).logits					):
      raise ValueError("The model logits don't match the original one."					)
  
    a__     		=     name
    print(lowerCamelCase__					)
  
    if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name ,				commit_message="Add model" ,				use_temp_dir=lowerCamelCase__ ,				)
  
      # we can use the convnext one
      a__     		=     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=lowerCamelCase__ ,				)
  
      print(f"Pushed {checkpoint_name}"					)
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ = None ,				lowerCamelCase__ = True					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "imagenet-1k-id2label.json"
    a__     		=     10_00
  
    a__     		=     "huggingface/label-files"
    a__     		=     num_labels
    a__     		=     json.load(open(hf_hub_download(lowerCamelCase__ ,				lowerCamelCase__ ,				repo_type="dataset"					) ,				"r"					)					)
    a__     		=     {int(lowerCamelCase__					): v for k, v in idalabel.items()}
  
    a__     		=     idalabel
    a__     		=     {v: k for k, v in idalabel.items()}
  
    a__     		=     partial(lowerCamelCase__ ,				num_labels=lowerCamelCase__ ,				idalabel=lowerCamelCase__ ,				labelaid=lowerCamelCase__					)
  
    a__     		=     {
        "van-tiny": ImageNetPreTrainedConfig(
            hidden_sizes=[32, 64, 1_60, 2_56] ,				depths=[3, 3, 5, 2] ,				mlp_ratios=[8, 8, 4, 4] ,				),
        "van-small": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12] ,				depths=[2, 2, 4, 2] ,				mlp_ratios=[8, 8, 4, 4] ,				),
        "van-base": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12] ,				depths=[3, 3, 12, 3] ,				mlp_ratios=[8, 8, 4, 4] ,				),
        "van-large": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12] ,				depths=[3, 5, 27, 3] ,				mlp_ratios=[8, 8, 4, 4] ,				),
    }
  
    a__     		=     {
        "van-tiny": van_tiny,
        "van-small": van_small,
        "van-base": van_base,
        "van-large": van_large,
    }
  
    a__     		=     {
        "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(
          lowerCamelCase__ ,				names_to_config[model_name] ,				checkpoint=names_to_original_checkpoints[model_name] ,				from_model=names_to_original_models[model_name]() ,				save_directory=lowerCamelCase__ ,				push_to_hub=lowerCamelCase__ ,				)
    else:
      for model_name, config in names_to_config.items():
        convert_weight_and_push(
            lowerCamelCase__ ,				lowerCamelCase__ ,				checkpoint=names_to_original_checkpoints[model_name] ,				from_model=names_to_original_models[model_name]() ,				save_directory=lowerCamelCase__ ,				push_to_hub=lowerCamelCase__ ,				)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    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.""",
       )
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
       _SCREAMING_SNAKE_CASE   						=    args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       _SCREAMING_SNAKE_CASE   						=    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)
  
  
  
style_context: _SCREAMING_SNAKE_CASE   						=    [0.485, 0.456, 0.406]
  _SCREAMING_SNAKE_CASE   						=    [0.229, 0.224, 0.225]
  _SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
  _SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
  _SCREAMING_SNAKE_CASE   						=    [0.48145466, 0.4578275, 0.40821073]
  _SCREAMING_SNAKE_CASE   						=    [0.26862954, 0.26130258, 0.27577711]
  
  