code: 
  
  
  '''simple docstring'''
  
  
  
  import math
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int							)  ->	int:
       return sum(int(UpperCAmelCase_							) for digit in str(UpperCAmelCase_							)							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str = 30							)  ->	int:
       __lowerCamelCase     :			Union[str, Any]              =	[]
       for digit in range(2   ,					1_00							):
            for power in range(2   ,					1_00							):
                 __lowerCamelCase     :			Dict              =	int(math.pow(UpperCAmelCase_   ,					UpperCAmelCase_							)							)
                 if digit == digit_sum(UpperCAmelCase_							):
                      digit_to_powers.append(UpperCAmelCase_							)
  
       digit_to_powers.sort()
       return digit_to_powers[n - 1]
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  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__	:      Optional[Any]				=						logging.get_logger()
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      nn.Module
      lowerCamelCase						:      List[nn.Module]							  =    field(default_factory=_UpperCAmelCase						)
      lowerCamelCase						:      list							  =    field(default_factory=_UpperCAmelCase						)
      lowerCamelCase						:      Dict[str, nn.Module]							  =    field(default_factory=_UpperCAmelCase						)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           __lowerCamelCase     :			List[Any]              =	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_			)
                __lowerCamelCase     :			int              =	m
  
  
  
  
  
  
  
      def __call__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
           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    lowercase_       (							self			)       ->  Dict:
           # 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    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
           super().__init__()
  
           __lowerCamelCase     :			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}'
                __lowerCamelCase     :			Union[str, Any]              =	len(SCREAMING_SNAKE_CASE_			) + 1
                feature_blocks.append((f'res{block_index}', v)			)
  
           __lowerCamelCase     :			Tuple              =	nn.ModuleDict(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           return get_trunk_forward_outputs(
               SCREAMING_SNAKE_CASE_    ,  out_feat_keys=SCREAMING_SNAKE_CASE_    ,  feature_blocks=self._feature_blocks    ,  )
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           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     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	Dict[str, str]:
       __lowerCamelCase     :			List[str]              =	RegNetConfig(depths=[2, 7, 17, 1]   ,					hidden_sizes=[8, 8, 8, 8]   ,					groups_width=8							)
       if "in1k" in model_name:
            __lowerCamelCase     :			Dict              =	RegNetForImageClassification(UpperCAmelCase_							)
       else:
            __lowerCamelCase     :			List[str]              =	RegNetModel(UpperCAmelCase_							)
       # create from model (with small weights)
       __lowerCamelCase     :			Dict              =	FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27   ,					group_width=10_10   ,					w_a=17_44   ,					w_a=620.83   ,					w_m=2.52							)							)							)
  
       with torch.no_grad():
            __lowerCamelCase     :			Union[str, Any]              =	from_model.eval()
            __lowerCamelCase     :			Dict              =	our_model.eval()
  
            __lowerCamelCase     :			str              =	torch.randn((1, 3, 32, 32)							)
            # trace both
            __lowerCamelCase     :			str              =	Tracker(UpperCAmelCase_							)
            __lowerCamelCase     :			str              =	dest_tracker(UpperCAmelCase_							).parametrized
  
            pprint(dest_tracker.nameamodule							)
            __lowerCamelCase     :			int              =	Tracker(UpperCAmelCase_							)
            __lowerCamelCase     :			int              =	src_tracker(UpperCAmelCase_							).parametrized
  
       # convert the keys -> module dict to keys -> params
       def to_params_dict(UpperCAmelCase_					:       Optional[Any]							):
            __lowerCamelCase     :			int              =	OrderedDict()
            for name, module in dict_with_modules.items():
                 for param_name, param in module.state_dict().items():
                      __lowerCamelCase     :			Tuple              =	param
            return params_dict
  
       __lowerCamelCase     :			int              =	{}
  
       __lowerCamelCase     :			int              =	to_params_dict(UpperCAmelCase_							)
       __lowerCamelCase     :			Dict              =	to_params_dict(UpperCAmelCase_							)
  
       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,					dst_state_dict.items()							):
            __lowerCamelCase     :			Optional[Any]              =	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:
            __lowerCamelCase     :			Any              =	'classifier.1.weight'
            __lowerCamelCase     :			Optional[int]              =	'classifier.1.bias'
  
       return from_to_ours_keys
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Path   ,					UpperCAmelCase_					:       str = None   ,					UpperCAmelCase_					:       bool = True							)  ->	List[Any]:
       __lowerCamelCase     :			Union[str, Any]              =	'imagenet-1k-id2label.json'
       __lowerCamelCase     :			List[Any]              =	10_00
  
       __lowerCamelCase     :			Dict              =	'huggingface/label-files'
       __lowerCamelCase     :			Optional[Any]              =	num_labels
       __lowerCamelCase     :			int              =	json.load(open(cached_download(hf_hub_url(UpperCAmelCase_   ,					UpperCAmelCase_   ,					repo_type='dataset'							)							)   ,					'r'							)							)
       __lowerCamelCase     :			Any              =	{int(UpperCAmelCase_							): v for k, v in idalabel.items()}
  
       __lowerCamelCase     :			Tuple              =	idalabel
       __lowerCamelCase     :			int              =	{v: k for k, v in idalabel.items()}
  
       __lowerCamelCase     :			Optional[Any]              =	partial(UpperCAmelCase_   ,					num_labels=UpperCAmelCase_   ,					idalabel=UpperCAmelCase_   ,					labelaid=UpperCAmelCase_							)
  
       __lowerCamelCase     :			Any              =	{
           'regnet-y-10b-seer': ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]   ,					hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]   ,					groups_width=10_10							),
           # finetuned on imagenet
           'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]   ,					hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]   ,					groups_width=10_10							),
       }
  
       # add seer weights logic
       def load_using_classy_vision(UpperCAmelCase_					:       str							) -> Tuple[Dict, Dict]:
            __lowerCamelCase     :			int              =	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
            __lowerCamelCase     :			Tuple              =	files['classy_state_dict']['base_model']['model']
            return model_state_dict["trunk"], model_state_dict["heads"]
  
       __lowerCamelCase     :			Any              =	{
           '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'   ,					),
       }
  
       __lowerCamelCase     :			Tuple              =	get_from_to_our_keys(UpperCAmelCase_							)
  
       if not (save_directory / F'{model_name}.pth').exists():
            logger.info('Loading original state_dict.'							)
            __lowerCamelCase       , __lowerCamelCase     :			List[Any]              =	names_to_from_model[model_name]()
            __lowerCamelCase     :			int              =	from_state_dict_trunk
            if "in1k" in model_name:
                 # add the head
                 __lowerCamelCase     :			int              =	{**from_state_dict_trunk, **from_state_dict_head}
            logger.info('Done!'							)
  
            __lowerCamelCase     :			Optional[Any]              =	{}
  
            __lowerCamelCase     :			List[str]              =	list(from_state_dict.keys()							)
            __lowerCamelCase     :			str              =	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
                 __lowerCamelCase     :			Optional[int]              =	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
                 __lowerCamelCase     :			List[Any]              =	from_to_ours_keys[src_key]
                 # store the parameter with our key
                 __lowerCamelCase     :			int              =	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
            __lowerCamelCase     :			List[str]              =	names_to_config[model_name]
            __lowerCamelCase     :			List[Any]              =	RegNetModel
            if "in1k" in model_name:
                 __lowerCamelCase     :			str              =	RegNetForImageClassification
            __lowerCamelCase     :			int              =	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
            __lowerCamelCase     :			Dict              =	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   ,					)
            __lowerCamelCase     :			Union[str, Any]              =	3_84
            # we can use the convnext one
            __lowerCamelCase     :			Tuple              =	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__":
        A__	:      Dict				=						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__	:      Tuple				=						parser.parse_args()
  
        A__	:      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)
  
  
  
  
  
  
  