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()
  lowercase_								=    logging.get_logger()
  
  
  
  
  @dataclass
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  42
  							_A  				=  field(default_factory=lowerCamelCase__	)
  							_A  				=  field(default_factory=lowerCamelCase__	)
  							_A  				=  field(default_factory=lowerCamelCase__	)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Tensor		,				SCREAMING_SNAKE_CASE_  :				Tensor		,				SCREAMING_SNAKE_CASE_  :				str		):
  													_a     =			len(list(m.modules()		)		) == 1 or isinstance(SCREAMING_SNAKE_CASE_		,				nn.Convad		) or isinstance(SCREAMING_SNAKE_CASE_		,				nn.BatchNormad		)
  													if has_not_submodules:
  																			self.traced.append(SCREAMING_SNAKE_CASE_		)
  																			_a     =			m
  
  
  
  
  
  
  
  							def __call__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Tensor		):
  													for name, m in self.module.named_modules():
  																			self.handles.append(m.register_forward_hook(partial(self._forward_hook		,				name=SCREAMING_SNAKE_CASE_		)		)		)
  													self.module(SCREAMING_SNAKE_CASE_		)
  													[x.remove() for x in self.handles]
  													return self
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				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 					_UpperCamelCase (      nn.Module	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				nn.Module		):
  													super().__init__()
  
  													_a     =			[]
  													# - 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     =			len(SCREAMING_SNAKE_CASE_		) + 1
  																			feature_blocks.append((f"""res{block_index}""", v)		)
  
  													_a     =			nn.ModuleDict(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Tensor		):
  													return get_trunk_forward_outputs(
  													    SCREAMING_SNAKE_CASE_		,				out_feat_keys=SCREAMING_SNAKE_CASE_		,				feature_blocks=self._feature_blocks		,				)
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		):
  													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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Dict[str, str]:
  						_a     =			RegNetConfig(depths=[2, 7, 17, 1]							,     hidden_sizes=[8, 8, 8, 8]							,     groups_width=8				)
  						if "in1k" in model_name:
  												_a     =			RegNetForImageClassification(_UpperCAmelCase				)
  						else:
  												_a     =			RegNetModel(_UpperCAmelCase				)
  						# create from model (with small weights)
  						_a     =			FakeRegNetVisslWrapper(
  						    RegNet(FakeRegNetParams(depth=27							,     group_width=1010							,     w_a=1744							,     w_a=620.83							,     w_m=2.52				)				)				)
  
  						with torch.no_grad():
  												_a     =			from_model.eval()
  												_a     =			our_model.eval()
  
  												_a     =			torch.randn((1, 3, 32, 32)				)
  												# trace both
  												_a     =			Tracker(_UpperCAmelCase				)
  												_a     =			dest_tracker(_UpperCAmelCase				).parametrized
  
  												pprint(dest_tracker.nameamodule				)
  												_a     =			Tracker(_UpperCAmelCase				)
  												_a     =			src_tracker(_UpperCAmelCase				).parametrized
  
  						# convert the keys -> module dict to keys -> params
  						def to_params_dict(_UpperCAmelCase				):
  												_a     =			OrderedDict()
  												for name, module in dict_with_modules.items():
  																		for param_name, param in module.state_dict().items():
  																								_a     =			param
  												return params_dict
  
  						_a     =			{}
  
  						_a     =			to_params_dict(_UpperCAmelCase				)
  						_a     =			to_params_dict(_UpperCAmelCase				)
  
  						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,     dst_state_dict.items()				):
  												_a     =			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     =			'classifier.1.weight'
  												_a     =			'classifier.1.bias'
  
  						return from_to_ours_keys
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase = None							,     _UpperCAmelCase = True				)    ->  Union[str, Any]:
  						_a     =			'imagenet-1k-id2label.json'
  						_a     =			1000
  
  						_a     =			'huggingface/label-files'
  						_a     =			num_labels
  						_a     =			json.load(open(cached_download(hf_hub_url(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)				)							,     'r'				)				)
  						_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  
  						_a     =			idalabel
  						_a     =			{v: k for k, v in idalabel.items()}
  
  						_a     =			partial(_UpperCAmelCase							,     num_labels=_UpperCAmelCase							,     idalabel=_UpperCAmelCase							,     labelaid=_UpperCAmelCase				)
  
  						_a     =			{
  						    '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(_UpperCAmelCase				) -> Tuple[Dict, Dict]:
  												_a     =			torch.hub.load_state_dict_from_url(_UpperCAmelCase							,     model_dir=str(_UpperCAmelCase				)							,     map_location='cpu'				)
  												# check if we have a head, if yes add it
  												_a     =			files['classy_state_dict']['base_model']['model']
  												return model_state_dict["trunk"], model_state_dict["heads"]
  
  						_a     =			{
  						    'regnet-y-10b-seer': partial(
  						        _UpperCAmelCase							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'							,     ),
  						    'regnet-y-10b-seer-in1k': partial(
  						        _UpperCAmelCase							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'							,     ),
  						}
  
  						_a     =			get_from_to_our_keys(_UpperCAmelCase				)
  
  						if not (save_directory / f"""{model_name}.pth""").exists():
  												logger.info('Loading original state_dict.'				)
  												_a ,	_a     =			names_to_from_model[model_name]()
  												_a     =			from_state_dict_trunk
  												if "in1k" in model_name:
  																		# add the head
  																		_a     =			{**from_state_dict_trunk, **from_state_dict_head}
  												logger.info('Done!'				)
  
  												_a     =			{}
  
  												_a     =			list(from_state_dict.keys()				)
  												_a     =			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     =			re.sub(_UpperCAmelCase							,     ''							,     _UpperCAmelCase				)
  																		# 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     =			from_to_ours_keys[src_key]
  																		# store the parameter with our key
  																		_a     =			from_state_dict[key]
  																		not_used_keys.remove(_UpperCAmelCase				)
  												# check that all keys have been updated
  												assert len(_UpperCAmelCase				) == 0, f"""Some keys where not used {','.join(_UpperCAmelCase				)}"""
  
  												logger.info(f"""The following keys were not used: {','.join(_UpperCAmelCase				)}"""				)
  
  												# save our state dict to disk
  												torch.save(_UpperCAmelCase							,     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     =			names_to_config[model_name]
  												_a     =			RegNetModel
  												if "in1k" in model_name:
  																		_a     =			RegNetForImageClassification
  												_a     =			our_model_func(_UpperCAmelCase				)
  												# 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     =			our_model.state_dict().keys()
  												PreTrainedModel._load_pretrained_model_low_mem(
  												    _UpperCAmelCase							,     _UpperCAmelCase							,     [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     =			384
  												# we can use the convnext one
  												_a     =			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							,     size=_UpperCAmelCase				)
  												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__":
  		lowercase_								=    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.',
  		)
  
  		lowercase_								=    parser.parse_args()
  
  		lowercase_								=    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: 
  
  
  import math
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						_a     =			math.log(2							,     10				) * number
  						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)
  
  						return difference
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
  						_a     =			90
  						_a     =			0
  
  						_a     =			math.log(1.23							,     10				)
  						_a     =			math.log(1.24							,     10				)
  						_a     =			0
  
  						while position < number:
  												_a     =			log_difference(_UpperCAmelCase				)
  
  												if difference >= upper_limit:
  																		power_iterator += 93
  
  												elif difference < lower_limit:
  																		power_iterator += 196
  
  												else:
  																		_a     =			power_iterator
  																		power_iterator += 196
  																		position += 1
  
  						return previous_power
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		print(f"""{solution() = }""")
  
  
  