code: 
  
  
  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()
  __A  							=  logging.get_logger()
  
  
  
  @dataclass
  class 							_A :
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   42
      lowerCamelCase  :       Union[str, Any]   =   field(default_factory=__lowerCamelCase							)
      lowerCamelCase  :       Any   =   field(default_factory=__lowerCamelCase							)
      lowerCamelCase  :       List[Any]   =   field(default_factory=__lowerCamelCase							)
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Union[str, Any]:
             __UpperCAmelCase    							=len(list(m.modules()			)			) == 1 or isinstance(_lowerCAmelCase   ,    nn.Convad			) or isinstance(_lowerCAmelCase   ,    nn.BatchNormad			)
             if has_not_submodules:
                    self.traced.append(_lowerCAmelCase			)
                    __UpperCAmelCase    							=m
  
  
  
      def __call__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       Any:
             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 _a       (							self			:    Dict			)			->       List[Any]:
             # check the len of the state_dict keys to see if we have learnable params
             return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()			)			) > 0}
  
  
  
  
  class 							_A (    nn.Module							):
      """simple docstring"""
  
      def __init__(							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			)			->       Dict:
             super().__init__()
  
             __UpperCAmelCase    							=[]
             # - 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}'''
                    __UpperCAmelCase    							=len(_lowerCAmelCase			) + 1
                    feature_blocks.append((f'''res{block_index}''', v)			)
  
             __UpperCAmelCase    							=nn.ModuleDict(_lowerCAmelCase			)
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       List[Any]:
             return get_trunk_forward_outputs(
                 _lowerCAmelCase   ,    out_feat_keys=_lowerCAmelCase   ,    feature_blocks=self._feature_blocks   ,    )
  
  
  
  
  class 							_A (    __lowerCamelCase							):
      """simple docstring"""
  
      def _a       (							self			:    Optional[Any]			)			->       Any:
             return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str		)  ->							Dict[str, str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=RegNetConfig(depths=[2, 7, 17, 1]     ,			hidden_sizes=[8, 8, 8, 8]     ,			groups_width=8		)
         if "in1k" in model_name:
                __UpperCAmelCase    							=RegNetForImageClassification(__lowerCamelCase		)
         else:
                __UpperCAmelCase    							=RegNetModel(__lowerCamelCase		)
         # create from model (with small weights)
         __UpperCAmelCase    							=FakeRegNetVisslWrapper(
             RegNet(FakeRegNetParams(depth=27     ,			group_width=1010     ,			w_a=1744     ,			w_a=6_2_0.8_3     ,			w_m=2.5_2		)		)		)
  
         with torch.no_grad():
                __UpperCAmelCase    							=from_model.eval()
                __UpperCAmelCase    							=our_model.eval()
  
                __UpperCAmelCase    							=torch.randn((1, 3, 32, 32)		)
                # trace both
                __UpperCAmelCase    							=Tracker(__lowerCamelCase		)
                __UpperCAmelCase    							=dest_tracker(__lowerCamelCase		).parametrized
  
                pprint(dest_tracker.nameamodule		)
                __UpperCAmelCase    							=Tracker(__lowerCamelCase		)
                __UpperCAmelCase    							=src_tracker(__lowerCamelCase		).parametrized
  
         # convert the keys -> module dict to keys -> params
         def to_params_dict(A_:	Tuple		):
                __UpperCAmelCase    							=OrderedDict()
                for name, module in dict_with_modules.items():
                       for param_name, param in module.state_dict().items():
                              __UpperCAmelCase    							=param
                return params_dict
  
         __UpperCAmelCase    							={}
  
         __UpperCAmelCase    							=to_params_dict(__lowerCamelCase		)
         __UpperCAmelCase    							=to_params_dict(__lowerCamelCase		)
  
         for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()     ,			dst_state_dict.items()		):
                __UpperCAmelCase    							=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:
                __UpperCAmelCase    							="""classifier.1.weight"""
                __UpperCAmelCase    							="""classifier.1.bias"""
  
         return from_to_ours_keys
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Dict = None     ,			A_:	Union[str, Any] = True		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							="""imagenet-1k-id2label.json"""
         __UpperCAmelCase    							=1000
  
         __UpperCAmelCase    							="""huggingface/label-files"""
         __UpperCAmelCase    							=num_labels
         __UpperCAmelCase    							=json.load(open(cached_download(hf_hub_url(__lowerCamelCase     ,			__lowerCamelCase     ,			repo_type="""dataset"""		)		)     ,			"""r"""		)		)
         __UpperCAmelCase    							={int(__lowerCamelCase		): v for k, v in idalabel.items()}
  
         __UpperCAmelCase    							=idalabel
         __UpperCAmelCase    							={v: k for k, v in idalabel.items()}
  
         __UpperCAmelCase    							=partial(__lowerCamelCase     ,			num_labels=__lowerCamelCase     ,			idalabel=__lowerCamelCase     ,			labelaid=__lowerCamelCase		)
  
         __UpperCAmelCase    							={
             """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
                 depths=[2, 7, 17, 1]     ,			hidden_sizes=[2020, 4040, 11110, 28280]     ,			groups_width=1010		),
             # finetuned on imagenet
             """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
                 depths=[2, 7, 17, 1]     ,			hidden_sizes=[2020, 4040, 11110, 28280]     ,			groups_width=1010		),
         }
  
         # add seer weights logic
         def load_using_classy_vision(A_:	Optional[int]		) -> Tuple[Dict, Dict]:
                __UpperCAmelCase    							=torch.hub.load_state_dict_from_url(__lowerCamelCase     ,			model_dir=str(__lowerCamelCase		)     ,			map_location="""cpu"""		)
                # check if we have a head, if yes add it
                __UpperCAmelCase    							=files["""classy_state_dict"""]["""base_model"""]["""model"""]
                return model_state_dict["trunk"], model_state_dict["heads"]
  
         __UpperCAmelCase    							={
             """regnet-y-10b-seer""": partial(
                 __lowerCamelCase     ,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""     ,			),
             """regnet-y-10b-seer-in1k""": partial(
                 __lowerCamelCase     ,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""     ,			),
         }
  
         __UpperCAmelCase    							=get_from_to_our_keys(__lowerCamelCase		)
  
         if not (save_directory / F'''{model_name}.pth''').exists():
                logger.info("""Loading original state_dict."""		)
                __UpperCAmelCase    							=names_to_from_model[model_name]()
                __UpperCAmelCase    							=from_state_dict_trunk
                if "in1k" in model_name:
                       # add the head
                       __UpperCAmelCase    							={**from_state_dict_trunk, **from_state_dict_head}
                logger.info("""Done!"""		)
  
                __UpperCAmelCase    							={}
  
                __UpperCAmelCase    							=list(from_state_dict.keys()		)
                __UpperCAmelCase    							=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
                       __UpperCAmelCase    							=re.sub(__lowerCamelCase     ,			""""""     ,			__lowerCamelCase		)
                       # 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
                       __UpperCAmelCase    							=from_to_ours_keys[src_key]
                       # store the parameter with our key
                       __UpperCAmelCase    							=from_state_dict[key]
                       not_used_keys.remove(__lowerCamelCase		)
                # check that all keys have been updated
                assert len(__lowerCamelCase		) == 0, F'''Some keys where not used {",".join(__lowerCamelCase		)}'''
  
                logger.info(F'''The following keys were not used: {",".join(__lowerCamelCase		)}'''		)
  
                # save our state dict to disk
                torch.save(__lowerCamelCase     ,			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
                __UpperCAmelCase    							=names_to_config[model_name]
                __UpperCAmelCase    							=RegNetModel
                if "in1k" in model_name:
                       __UpperCAmelCase    							=RegNetForImageClassification
                __UpperCAmelCase    							=our_model_func(__lowerCamelCase		)
                # 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
                __UpperCAmelCase    							=our_model.state_dict().keys()
                PreTrainedModel._load_pretrained_model_low_mem(
                    __lowerCamelCase     ,			__lowerCamelCase     ,			[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     ,			)
                __UpperCAmelCase    							=384
                # we can use the convnext one
                __UpperCAmelCase    							=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""     ,			size=__lowerCamelCase		)
                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__":
     __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 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.",
     )
  
     __A  							=  parser.parse_args()
  
     __A  							=  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)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections import defaultdict
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int = 5		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=defaultdict(A_		)
         __UpperCAmelCase    							=0
  
         while True:
                __UpperCAmelCase    							=get_digits(A_		)
                freqs[digits].append(A_		)
  
                if len(freqs[digits]		) == max_base:
                       __UpperCAmelCase    							=freqs[digits][0] ** 3
                       return base
  
                num += 1
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return "".join(sorted(str(num**3		)		)		)
  
  
  if __name__ == "__main__":
     print(F"""{solution() = }""")
  
  
  
  
  