code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  UpperCAmelCase			:			int	  = logging.get_logger(__name__)
  
  
  if is_vision_available():
       import PIL
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase_):
        _lowercase       :      Dict        =    ["""pixel_values"""]
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = PILImageResampling.BICUBIC	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = 1 / 2_5_5	,   lowerCAmelCase__ = True	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = True	,   **lowerCAmelCase__	,   )     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__(**__SCREAMING_SNAKE_CASE							)
          a__			:      List[str]            =size if size is not None else {"shortest_edge": 2_2_4}
          a__			:      Optional[Any]            =get_size_dict(__SCREAMING_SNAKE_CASE	,   default_to_square=__SCREAMING_SNAKE_CASE							)
          a__			:      int            =crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
          a__			:      Optional[Any]            =get_size_dict(__SCREAMING_SNAKE_CASE							)
  
          a__			:      Any            =do_resize
          a__			:      Tuple            =size
          a__			:      List[Any]            =resample
          a__			:      List[Any]            =do_center_crop
          a__			:      Optional[Any]            =crop_size
          a__			:      List[str]            =do_rescale
          a__			:      Dict            =rescale_factor
          a__			:      int            =do_normalize
          a__			:      List[str]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          a__			:      Optional[int]            =image_std if image_std is not None else OPENAI_CLIP_STD
          a__			:      Union[str, Any]            =do_convert_rgb
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = PILImageResampling.BICUBIC	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =get_size_dict(__SCREAMING_SNAKE_CASE	,   default_to_square=__SCREAMING_SNAKE_CASE							)
          a__			:      str            =get_resize_output_image_size(
              __SCREAMING_SNAKE_CASE	,   size=(size["height"], size["width"])	,   default_to_square=__SCREAMING_SNAKE_CASE							)
          return resize(__SCREAMING_SNAKE_CASE	,   size=__SCREAMING_SNAKE_CASE	,   resample=__SCREAMING_SNAKE_CASE	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =get_size_dict(__SCREAMING_SNAKE_CASE							)
          return center_crop(__SCREAMING_SNAKE_CASE	,   size=(size["height"], size["width"])	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return rescale(__SCREAMING_SNAKE_CASE	,   scale=__SCREAMING_SNAKE_CASE	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		np.ndarray:
  
  
  
          '''simple docstring'''
  
  
  
  
          return normalize(__SCREAMING_SNAKE_CASE	,   mean=__SCREAMING_SNAKE_CASE	,   std=__SCREAMING_SNAKE_CASE	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = ChannelDimension.FIRST	,   **lowerCAmelCase__	,   )     ->		PIL.Image.Image:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =do_resize if do_resize is not None else self.do_resize
          a__			:      Any            =size if size is not None else self.size
          a__			:      str            =get_size_dict(__SCREAMING_SNAKE_CASE	,   default_to_square=__SCREAMING_SNAKE_CASE							)
          a__			:      str            =resample if resample is not None else self.resample
          a__			:      Optional[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
          a__			:      Tuple            =crop_size if crop_size is not None else self.crop_size
          a__			:      Any            =get_size_dict(__SCREAMING_SNAKE_CASE							)
          a__			:      List[Any]            =do_rescale if do_rescale is not None else self.do_rescale
          a__			:      Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
          a__			:      Any            =do_normalize if do_normalize is not None else self.do_normalize
          a__			:      Any            =image_mean if image_mean is not None else self.image_mean
          a__			:      Dict            =image_std if image_std is not None else self.image_std
          a__			:      Optional[Any]            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
          a__			:      str            =make_list_of_images(__SCREAMING_SNAKE_CASE							)
  
          if not valid_images(__SCREAMING_SNAKE_CASE							):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."							)
  
          if do_resize and size is None:
            raise ValueError("Size must be specified if do_resize is True."							)
  
          if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True."							)
  
          if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True."							)
  
          if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True."							)
  
          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
            a__			:      Optional[Any]            =[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]
  
          # All transformations expect numpy arrays.
          a__			:      List[str]            =[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]
  
          if do_resize:
            a__			:      List[Any]            =[self.resize(image=__SCREAMING_SNAKE_CASE	,   size=__SCREAMING_SNAKE_CASE	,   resample=__SCREAMING_SNAKE_CASE							) for image in images]
  
          if do_center_crop:
            a__			:      Any            =[self.center_crop(image=__SCREAMING_SNAKE_CASE	,   size=__SCREAMING_SNAKE_CASE							) for image in images]
  
          if do_rescale:
            a__			:      int            =[self.rescale(image=__SCREAMING_SNAKE_CASE	,   scale=__SCREAMING_SNAKE_CASE							) for image in images]
  
          if do_normalize:
            a__			:      Tuple            =[self.normalize(image=__SCREAMING_SNAKE_CASE	,   mean=__SCREAMING_SNAKE_CASE	,   std=__SCREAMING_SNAKE_CASE							) for image in images]
  
          a__			:      List[str]            =[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE							) for image in images]
  
          a__			:      List[Any]            ={"pixel_values": images}
          return BatchFeature(data=__SCREAMING_SNAKE_CASE	,   tensor_type=__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  