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()
  __lowerCAmelCase     =			logging.get_logger()
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   field(default_factory=_lowercase					)
  					__A          =   field(default_factory=_lowercase					)
  					__A          =   field(default_factory=_lowercase					)
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  								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    ,      __lowerCAmelCase       )						->  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 	snake_case_		(						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					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase       )						->  Optional[Any]:
  								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 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  								return get_trunk_forward_outputs(
  								    __lowerCAmelCase    ,      out_feat_keys=__lowerCAmelCase    ,      feature_blocks=self._feature_blocks    ,      )
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase					):
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  								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     a						(      _lowercase   :     str       )     ->Dict[str, str]:
  			UpperCamelCase__         =	RegNetConfig(depths=[2, 7, 1_7, 1] ,						hidden_sizes=[8, 8, 8, 8] ,						groups_width=8       )
  			if "in1k" in model_name:
  						UpperCamelCase__         =	RegNetForImageClassification(_lowercase       )
  			else:
  						UpperCamelCase__         =	RegNetModel(_lowercase       )
  			# create from model (with small weights)
  			UpperCamelCase__         =	FakeRegNetVisslWrapper(
  			    RegNet(FakeRegNetParams(depth=2_7 ,						group_width=1_0_1_0 ,						w_a=1_7_4_4 ,						w_a=620.83 ,						w_m=2.52       )       )       )
  
  			with torch.no_grad():
  						UpperCamelCase__         =	from_model.eval()
  						UpperCamelCase__         =	our_model.eval()
  
  						UpperCamelCase__         =	torch.randn((1, 3, 3_2, 3_2)       )
  						# trace both
  						UpperCamelCase__         =	Tracker(_lowercase       )
  						UpperCamelCase__         =	dest_tracker(_lowercase       ).parametrized
  
  						pprint(dest_tracker.nameamodule       )
  						UpperCamelCase__         =	Tracker(_lowercase       )
  						UpperCamelCase__         =	src_tracker(_lowercase       ).parametrized
  
  			# convert the keys -> module dict to keys -> params
  			def to_params_dict(_lowercase   :     Union[str, Any]       ):
  						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(_lowercase       )
  			UpperCamelCase__         =	to_params_dict(_lowercase       )
  
  			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     a						(      _lowercase   :     Path ,						_lowercase   :     str = None ,						_lowercase   :     bool = True       )     ->Tuple:
  			UpperCamelCase__         =	"""imagenet-1k-id2label.json"""
  			UpperCamelCase__         =	1_0_0_0
  
  			UpperCamelCase__         =	"""huggingface/label-files"""
  			UpperCamelCase__         =	num_labels
  			UpperCamelCase__         =	json.load(open(cached_download(hf_hub_url(_lowercase ,						_lowercase ,						repo_type="""dataset"""       )       ) ,						"""r"""       )       )
  			UpperCamelCase__         =	{int(_lowercase       ): v for k, v in idalabel.items()}
  
  			UpperCamelCase__         =	idalabel
  			UpperCamelCase__         =	{v: k for k, v in idalabel.items()}
  
  			UpperCamelCase__         =	partial(_lowercase ,						num_labels=_lowercase ,						idalabel=_lowercase ,						labelaid=_lowercase       )
  
  			UpperCamelCase__         =	{
  			    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
  			        depths=[2, 7, 1_7, 1] ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,						groups_width=1_0_1_0       ),
  			    # finetuned on imagenet
  			    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
  			        depths=[2, 7, 1_7, 1] ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,						groups_width=1_0_1_0       ),
  			}
  
  			# add seer weights logic
  			def load_using_classy_vision(_lowercase   :     str       ) -> Tuple[Dict, Dict]:
  						UpperCamelCase__         =	torch.hub.load_state_dict_from_url(_lowercase ,						model_dir=str(_lowercase       ) ,						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(
  			        _lowercase ,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,						),
  			    """regnet-y-10b-seer-in1k""": partial(
  			        _lowercase ,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" ,						),
  			}
  
  			UpperCamelCase__         =	get_from_to_our_keys(_lowercase       )
  
  			if not (save_directory / F"""{model_name}.pth""").exists():
  						logger.info("""Loading original state_dict."""       )
  						UpperCamelCase__  ,			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(_lowercase ,						"""""" ,						_lowercase       )
  									# 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(_lowercase       )
  						# check that all keys have been updated
  						assert len(_lowercase       ) == 0, F"""Some keys where not used {','.join(_lowercase       )}"""
  
  						logger.info(F"""The following keys were not used: {','.join(_lowercase       )}"""       )
  
  						# save our state dict to disk
  						torch.save(_lowercase ,						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(_lowercase       )
  						# 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(
  						    _lowercase ,						_lowercase ,						[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__         =	3_8_4
  						# we can use the convnext one
  						UpperCamelCase__         =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" ,						size=_lowercase       )
  						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__":
  				__lowerCAmelCase     =			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.",
  				)
  
  				__lowerCAmelCase     =			parser.parse_args()
  
  				__lowerCAmelCase     =			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: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
  				if not swapped:
  						break  # Stop iteration if the collection is sorted.
  		return collection
  
  
  if __name__ == "__main__":
  				import doctest
  				import time
  
  				doctest.testmod()
  
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  