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()
  a__						:			Optional[Any]										=			logging.get_logger()
  @dataclass
  class        UpperCAmelCase__  :
  	__SCREAMING_SNAKE_CASE					      =				42
  	__SCREAMING_SNAKE_CASE					      =				field(default_factory=UpperCamelCase__)
  	__SCREAMING_SNAKE_CASE					      =				field(default_factory=UpperCamelCase__)
  	__SCREAMING_SNAKE_CASE					      =				field(default_factory=UpperCamelCase__)
  
  
  
  	def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							str:
  		__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		,	lowercase			)  ->							Optional[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    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  		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		,	lowercase			)  ->							List[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    __lowerCamelCase				(  self		,	lowercase			)  ->							Union[str, Any]:
  		return get_trunk_forward_outputs(
  		    lowerCAmelCase__		,	out_feat_keys=lowerCAmelCase__		,	feature_blocks=self._feature_blocks		,	)
  
  
  class        UpperCAmelCase__  (    UpperCamelCase__):
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Tuple:
  		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 _lowercase   (  __A       ):
  
  
  
  	'''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(__A       )
  	else:
  		__UpperCamelCase				      =  RegNetModel(__A       )
  	# create from model (with small weights)
  	__UpperCamelCase				      =  FakeRegNetVisslWrapper(
  	    RegNet(FakeRegNetParams(depth=27							,group_width=1_010							,w_a=1_744							,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, 32, 32)       )
  		# trace both
  		__UpperCamelCase				      =  Tracker(__A       )
  		__UpperCamelCase				      =  dest_tracker(__A       ).parametrized
  
  		pprint(dest_tracker.nameamodule       )
  		__UpperCamelCase				      =  Tracker(__A       )
  		__UpperCamelCase				      =  src_tracker(__A       ).parametrized
  
  	# convert the keys -> module dict to keys -> params
  	def to_params_dict(__A       ):
  		__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(__A       )
  	__UpperCamelCase				      =  to_params_dict(__A       )
  
  	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							,__A = None							,__A = True       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  "imagenet-1k-id2label.json"
  	__UpperCamelCase				      =  1_000
  
  	__UpperCamelCase				      =  "huggingface/label-files"
  	__UpperCamelCase				      =  num_labels
  	__UpperCamelCase				      =  json.load(open(cached_download(hf_hub_url(__A							,__A							,repo_type="""dataset"""       )       )							,"""r"""       )       )
  	__UpperCamelCase				      =  {int(__A       ): v for k, v in idalabel.items()}
  
  	__UpperCamelCase				      =  idalabel
  	__UpperCamelCase				      =  {v: k for k, v in idalabel.items()}
  
  	__UpperCamelCase				      =  partial(__A							,num_labels=__A							,idalabel=__A							,labelaid=__A       )
  
  	__UpperCamelCase				      =  {
  	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
  	        depths=[2, 7, 17, 1]							,hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,groups_width=1_010       ),
  	    # finetuned on imagenet
  	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
  	        depths=[2, 7, 17, 1]							,hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,groups_width=1_010       ),
  	}
  
  	# add seer weights logic
  	def load_using_classy_vision(__A       ) -> Tuple[Dict, Dict]:
  		__UpperCamelCase				      =  torch.hub.load_state_dict_from_url(__A							,model_dir=str(__A       )							,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(
  	        __A							,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,),
  	    "regnet-y-10b-seer-in1k": partial(
  	        __A							,"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,),
  	}
  
  	__UpperCamelCase				      =  get_from_to_our_keys(__A       )
  
  	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(__A							,""""""							,__A       )
  			# 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(__A       )
  		# check that all keys have been updated
  		assert len(__A       ) == 0, f"Some keys where not used {','.join(__A       )}"
  
  		logger.info(f"The following keys were not used: {','.join(__A       )}"       )
  
  		# save our state dict to disk
  		torch.save(__A							,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(__A       )
  		# 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(
  		    __A							,__A							,[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=__A       )
  		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__						:			Tuple										=			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__						:			int										=			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)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from .stack import Stack
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  Stack()
   __UpperCamelCase				      =  {"""(""": """)""", """[""": """]""", """{""": """}"""}
   for bracket in parentheses:
    if bracket in bracket_pairs:
     stack.push(__A       )
    elif bracket in (")", "]", "}"):
     if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
      return False
   return stack.is_empty()
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
   a__						:			List[Any]										=			['((()))', '((())', '(()))']
   print('Balanced parentheses demonstration:\n')
   for example in examples:
    a__						:			int										=			'' if balanced_parentheses(example) else 'not '
    print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  