code: 
  
  
  
  
  
  '''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
  
  
  UpperCAmelCase			:			Union[str, Any]	  = logging.get_logger(__name__)
  
  UpperCAmelCase			:			Optional[Any]	  = {
      """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
      """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
      """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
      """apple/deeplabv3-mobilevit-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
      ),
      """apple/deeplabv3-mobilevit-x-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
      ),
      """apple/deeplabv3-mobilevit-xx-small""": (
          """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Tuple        =    """mobilevit"""
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=3	,   lowerCAmelCase__=2_5_6	,   lowerCAmelCase__=2	,   lowerCAmelCase__=[1_4_4, 1_9_2, 2_4_0]	,   lowerCAmelCase__=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]	,   lowerCAmelCase__=4	,   lowerCAmelCase__=2.0	,   lowerCAmelCase__=4.0	,   lowerCAmelCase__="silu"	,   lowerCAmelCase__=3	,   lowerCAmelCase__=3_2	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.02	,   lowerCAmelCase__=1E-5	,   lowerCAmelCase__=True	,   lowerCAmelCase__=2_5_6	,   lowerCAmelCase__=[6, 1_2, 1_8]	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=2_5_5	,   **lowerCAmelCase__	,   )     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__(**lowerCAmelCase__							)
  
          a__			:      Any            =num_channels
          a__			:      Any            =image_size
          a__			:      Dict            =patch_size
          a__			:      Tuple            =hidden_sizes
          a__			:      List[Any]            =neck_hidden_sizes
          a__			:      str            =num_attention_heads
          a__			:      Dict            =mlp_ratio
          a__			:      Tuple            =expand_ratio
          a__			:      List[Any]            =hidden_act
          a__			:      str            =conv_kernel_size
          a__			:      int            =output_stride
          a__			:      int            =hidden_dropout_prob
          a__			:      Tuple            =attention_probs_dropout_prob
          a__			:      Tuple            =classifier_dropout_prob
          a__			:      List[str]            =initializer_range
          a__			:      Optional[int]            =layer_norm_eps
          a__			:      Any            =qkv_bias
  
          # decode head attributes for semantic segmentation
          a__			:      Dict            =aspp_out_channels
          a__			:      Optional[int]            =atrous_rates
          a__			:      Dict            =aspp_dropout_prob
          a__			:      str            =semantic_loss_ignore_index
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Optional[int]        =    version.parse("""1.11""")
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]							)
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.task == "image-classification":
            return OrderedDict([("logits", {0: "batch"})]							)
          else:
            return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]							)
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		float:
  
  
  
          '''simple docstring'''
  
  
  
  
          return 1E-4
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from pprint import pprint
  from typing import Dict, List, Tuple
  
  import torch
  import torch.nn as nn
  from classy_vision.models.regnet import RegNet, RegNetParams
  from huggingface_hub import cached_download, hf_hub_url
  from torch import Tensor
  from vissl.models.model_helpers import get_trunk_forward_outputs
  
  from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  UpperCAmelCase			:			Optional[Any]	  = logging.get_logger()
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      nn.Module
        _lowercase       :      List[nn.Module]        =    field(default_factory=UpperCamelCase__)
        _lowercase       :      list        =    field(default_factory=UpperCamelCase__)
        _lowercase       :      Dict[str, nn.Module]        =    field(default_factory=UpperCamelCase__)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =len(list(m.modules()							)							) == 1 or isinstance(lowerCAmelCase__	,   nn.Convad							) or isinstance(lowerCAmelCase__	,   nn.BatchNormad							)
          if has_not_submodules:
            self.traced.append(lowerCAmelCase__							)
            a__			:      List[Any]            =m
  
  
  
  
  
  
  
        def __call__(					self	,   lowerCAmelCase__							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          for name, m in self.module.named_modules():
            self.handles.append(m.register_forward_hook(partial(self._forward_hook	,   name=lowerCAmelCase__							)							)							)
          self.module(lowerCAmelCase__							)
          [x.remove() for x in self.handles]
          return self
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					nn.Module):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__()
  
          a__			:      List[Tuple[str, nn.Module]]            =[]
          # - get the stem
          feature_blocks.append(("conv1", model.stem)							)
          # - get all the feature blocks
          for k, v in model.trunk_output.named_children():
            assert k.startswith("block"							), F'''Unexpected layer name {k}'''
            a__			:      int            =len(lowerCAmelCase__							) + 1
            feature_blocks.append((F'''res{block_index}''', v)							)
  
          a__			:      List[str]            =nn.ModuleDict(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return get_trunk_forward_outputs(
              lowerCAmelCase__	,   out_feat_keys=lowerCAmelCase__	,   feature_blocks=self._feature_blocks	,   )
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]
  def   _A (	SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Union[str, Any]            =RegNetConfig(depths=[2, 7, 17, 1] , hidden_sizes=[8, 8, 8, 8] , groups_width=8		)
    if "in1k" in model_name:
      a__			:      Dict            =RegNetForImageClassification(SCREAMING_SNAKE_CASE		)
    else:
      a__			:      List[Any]            =RegNetModel(SCREAMING_SNAKE_CASE		)
    # create from model (with small weights)
    a__			:      Tuple            =FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=6_2_0.8_3 , w_m=2.5_2		)		)		)
  
    with torch.no_grad():
      a__			:      int            =from_model.eval()
      a__			:      int            =our_model.eval()
  
      a__			:      Dict            =torch.randn((1, 3, 32, 32)		)
      # trace both
      a__			:      Tuple            =Tracker(SCREAMING_SNAKE_CASE		)
      a__			:      List[Any]            =dest_tracker(SCREAMING_SNAKE_CASE		).parametrized
  
      pprint(dest_tracker.nameamodule		)
      a__			:      Optional[Any]            =Tracker(SCREAMING_SNAKE_CASE		)
      a__			:      Tuple            =src_tracker(SCREAMING_SNAKE_CASE		).parametrized
  
    # convert the keys -> module dict to keys -> params
    def to_params_dict(SCREAMING_SNAKE_CASE  :     Any		):
      a__			:      List[Any]            =OrderedDict()
      for name, module in dict_with_modules.items():
        for param_name, param in module.state_dict().items():
          a__			:      Optional[Any]            =param
      return params_dict
  
    a__			:      Union[str, Any]            ={}
  
    a__			:      Optional[int]            =to_params_dict(SCREAMING_SNAKE_CASE		)
    a__			:      List[Any]            =to_params_dict(SCREAMING_SNAKE_CASE		)
  
    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() , dst_state_dict.items()		):
      a__			:      List[str]            =dest_key
      logger.info(f'''{src_key} -> {dest_key}'''		)
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
      a__			:      str            ="classifier.1.weight"
      a__			:      Optional[Any]            ="classifier.1.bias"
  
    return from_to_ours_keys
  def   _A (	SCREAMING_SNAKE_CASE  :     Path , SCREAMING_SNAKE_CASE  :     str = None , SCREAMING_SNAKE_CASE  :     bool = True		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      int            ="imagenet-1k-id2label.json"
    a__			:      int            =1_000
  
    a__			:      Optional[int]            ="huggingface/label-files"
    a__			:      Union[str, Any]            =num_labels
    a__			:      Optional[int]            =json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type="dataset"		)		) , "r"		)		)
    a__			:      Tuple            ={int(SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
  
    a__			:      Any            =idalabel
    a__			:      Optional[int]            ={v: k for k, v in idalabel.items()}
  
    a__			:      str            =partial(SCREAMING_SNAKE_CASE , num_labels=SCREAMING_SNAKE_CASE , idalabel=SCREAMING_SNAKE_CASE , labelaid=SCREAMING_SNAKE_CASE		)
  
    a__			:      Tuple            ={
        "regnet-y-10b-seer": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010		),
        # finetuned on imagenet
        "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010		),
    }
  
    # add seer weights logic
    def load_using_classy_vision(SCREAMING_SNAKE_CASE  :     str		) -> Tuple[Dict, Dict]:
      a__			:      Optional[int]            =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , model_dir=str(SCREAMING_SNAKE_CASE		) , map_location="cpu"		)
      # check if we have a head, if yes add it
      a__			:      Optional[Any]            =files["classy_state_dict"]["base_model"]["model"]
      return model_state_dict["trunk"], model_state_dict["heads"]
  
    a__			:      int            ={
        "regnet-y-10b-seer": partial(
            SCREAMING_SNAKE_CASE , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch" , ),
        "regnet-y-10b-seer-in1k": partial(
            SCREAMING_SNAKE_CASE , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch" , ),
    }
  
    a__			:      List[Any]            =get_from_to_our_keys(SCREAMING_SNAKE_CASE		)
  
    if not (save_directory / f'''{model_name}.pth''').exists():
      logger.info("Loading original state_dict."		)
      a__      ,							a__			:      Optional[int]            =names_to_from_model[model_name]()
      a__			:      Dict            =from_state_dict_trunk
      if "in1k" in model_name:
        # add the head
        a__			:      Optional[int]            ={**from_state_dict_trunk, **from_state_dict_head}
      logger.info("Done!"		)
  
      a__			:      List[str]            ={}
  
      a__			:      Any            =list(from_state_dict.keys()		)
      a__			:      Union[str, Any]            =r"\.block.-part."
      # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
      for key in from_state_dict.keys():
        # remove the weird "block[0,1]-part" from the key
        a__			:      int            =re.sub(SCREAMING_SNAKE_CASE , "" , SCREAMING_SNAKE_CASE		)
        # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
        a__			:      Optional[int]            =from_to_ours_keys[src_key]
        # store the parameter with our key
        a__			:      Tuple            =from_state_dict[key]
        not_used_keys.remove(SCREAMING_SNAKE_CASE		)
      # check that all keys have been updated
      assert len(SCREAMING_SNAKE_CASE		) == 0, f'''Some keys where not used {",".join(SCREAMING_SNAKE_CASE		)}'''
  
      logger.info(f'''The following keys were not used: {",".join(SCREAMING_SNAKE_CASE		)}'''		)
  
      # save our state dict to disk
      torch.save(SCREAMING_SNAKE_CASE , save_directory / f'''{model_name}.pth'''		)
  
      del converted_state_dict
    else:
      logger.info("The state_dict was already stored on disk."		)
    if push_to_hub:
      logger.info(f'''Token is {os.environ["HF_TOKEN"]}'''		)
      logger.info("Loading our model."		)
      # create our model
      a__			:      Tuple            =names_to_config[model_name]
      a__			:      Tuple            =RegNetModel
      if "in1k" in model_name:
        a__			:      Any            =RegNetForImageClassification
      a__			:      Tuple            =our_model_func(SCREAMING_SNAKE_CASE		)
      # place our model to the meta device (so remove all the weights)
      our_model.to(torch.device("meta"		)		)
      logger.info("Loading state_dict in our model."		)
      # load state dict
      a__			:      int            =our_model.state_dict().keys()
      PreTrainedModel._load_pretrained_model_low_mem(
          SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , [save_directory / f'''{model_name}.pth''']		)
      logger.info("Finally, pushing!"		)
      # push it to hub
      our_model.push_to_hub(
          repo_path_or_name=save_directory / model_name , commit_message="Add model" , output_dir=save_directory / model_name , )
      a__			:      Dict            =384
      # we can use the convnext one
      a__			:      Any            =AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" , size=SCREAMING_SNAKE_CASE		)
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / model_name , commit_message="Add image processor" , output_dir=save_directory / model_name , )
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			Tuple	  = 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 regnet* architecture,"""
               """ currently: regnetx-*, regnety-*. 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(
           """--push_to_hub""",
           default=True,
           type=bool,
           required=False,
           help="""If True, push model and image processor to the hub.""",
       )
  
       UpperCAmelCase			:			int	  = parser.parse_args()
  
       UpperCAmelCase			:			Path	  = args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
  
  
  
  
  
  
  