code: 
  
  
  
  
  '''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()
  _lowerCamelCase							:		Any										=						logging.get_logger()
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = len(list(m.modules()				)				) == 1 or isinstance(__UpperCamelCase,						nn.Convad				) or isinstance(__UpperCamelCase,						nn.BatchNormad				)
  							if has_not_submodules:
  									self.traced.append(__UpperCamelCase				)
  									_snake_case			 = m
  
  
  
  
  
  
  					def __call__(					self,						__UpperCamelCase				):
  							for name, m in self.module.named_modules():
  									self.handles.append(m.register_forward_hook(partial(self._forward_hook,						name=__UpperCamelCase				)				)				)
  							self.module(__UpperCamelCase				)
  							[x.remove() for x in self.handles]
  							return self
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# 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   lowerCamelCase_							( nn.Module							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							super().__init__()
  
  							_snake_case			 = []
  							# - 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}'''
  									_snake_case			 = len(__UpperCamelCase				) + 1
  									feature_blocks.append((f'''res{block_index}''', v)				)
  
  							_snake_case			 = nn.ModuleDict(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return get_trunk_forward_outputs(
  							    __UpperCamelCase,						out_feat_keys=__UpperCamelCase,						feature_blocks=self._feature_blocks,						)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							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	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = RegNetConfig(depths=[2, 7, 17, 1]		,							hidden_sizes=[8, 8, 8, 8]		,							groups_width=8						)
  		if "in1k" in model_name:
  				_snake_case			 = RegNetForImageClassification(lowercase_						)
  		else:
  				_snake_case			 = RegNetModel(lowercase_						)
  		# create from model (with small weights)
  		_snake_case			 = FakeRegNetVisslWrapper(
  		    RegNet(FakeRegNetParams(depth=27		,							group_width=1010		,							w_a=1744		,							w_a=620.83		,							w_m=2.52						)						)						)
  
  		with torch.no_grad():
  				_snake_case			 = from_model.eval()
  				_snake_case			 = our_model.eval()
  
  				_snake_case			 = torch.randn((1, 3, 32, 32)						)
  				# trace both
  				_snake_case			 = Tracker(lowercase_						)
  				_snake_case			 = dest_tracker(lowercase_						).parametrized
  
  				pprint(dest_tracker.nameamodule						)
  				_snake_case			 = Tracker(lowercase_						)
  				_snake_case			 = src_tracker(lowercase_						).parametrized
  
  		# convert the keys -> module dict to keys -> params
  		def to_params_dict(lowercase_						):
  				_snake_case			 = OrderedDict()
  				for name, module in dict_with_modules.items():
  						for param_name, param in module.state_dict().items():
  								_snake_case			 = param
  				return params_dict
  
  		_snake_case			 = {}
  
  		_snake_case			 = to_params_dict(lowercase_						)
  		_snake_case			 = to_params_dict(lowercase_						)
  
  		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,							dst_state_dict.items()						):
  				_snake_case			 = 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:
  				_snake_case			 = "classifier.1.weight"
  				_snake_case			 = "classifier.1.bias"
  
  		return from_to_ours_keys
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_ = None		,							lowercase_ = True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = "imagenet-1k-id2label.json"
  		_snake_case			 = 1000
  
  		_snake_case			 = "huggingface/label-files"
  		_snake_case			 = num_labels
  		_snake_case			 = json.load(open(cached_download(hf_hub_url(lowercase_		,							lowercase_		,							repo_type="dataset"						)						)		,							"r"						)						)
  		_snake_case			 = {int(lowercase_						): v for k, v in idalabel.items()}
  
  		_snake_case			 = idalabel
  		_snake_case			 = {v: k for k, v in idalabel.items()}
  
  		_snake_case			 = partial(lowercase_		,							num_labels=lowercase_		,							idalabel=lowercase_		,							labelaid=lowercase_						)
  
  		_snake_case			 = {
  		    "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(lowercase_						) -> Tuple[Dict, Dict]:
  				_snake_case			 = 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
  				_snake_case			 = files["classy_state_dict"]["base_model"]["model"]
  				return model_state_dict["trunk"], model_state_dict["heads"]
  
  		_snake_case			 = {
  		    "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"		,							),
  		}
  
  		_snake_case			 = get_from_to_our_keys(lowercase_						)
  
  		if not (save_directory / f'''{model_name}.pth''').exists():
  				logger.info("Loading original state_dict."						)
  				_snake_case ,					_snake_case			 = names_to_from_model[model_name]()
  				_snake_case			 = from_state_dict_trunk
  				if "in1k" in model_name:
  						# add the head
  						_snake_case			 = {**from_state_dict_trunk, **from_state_dict_head}
  				logger.info("Done!"						)
  
  				_snake_case			 = {}
  
  				_snake_case			 = list(from_state_dict.keys()						)
  				_snake_case			 = 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
  						_snake_case			 = 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
  						_snake_case			 = from_to_ours_keys[src_key]
  						# store the parameter with our key
  						_snake_case			 = 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
  				_snake_case			 = names_to_config[model_name]
  				_snake_case			 = RegNetModel
  				if "in1k" in model_name:
  						_snake_case			 = RegNetForImageClassification
  				_snake_case			 = 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
  				_snake_case			 = 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		,							)
  				_snake_case			 = 384
  				# we can use the convnext one
  				_snake_case			 = 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							:		str										=						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							:		Tuple										=						parser.parse_args()
  
  						_lowerCamelCase							:		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)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  