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: 
  
  
  
  
  
  from dataclasses import dataclass
  
  __lowerCAmelCase     =			"0.1"
  __lowerCAmelCase     =			"Lucia Harcekova"
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   42
  					__A          =   42
  
  
  
  
  					def __repr__(						self       )						->  str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase = 13    ,      __lowerCAmelCase = 6       )						->  None:
  							UpperCamelCase__         =	window_size
  							UpperCamelCase__         =	lookahead_buffer_size
  							UpperCamelCase__         =	self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  list[Token]:
  							UpperCamelCase__         =	[]
  							UpperCamelCase__         =	""""""
  
  							# while there are still characters in text to compress
  							while text:
  									# find the next encoding phrase
  									# - triplet with offset, length, indicator (the next encoding character)
  									UpperCamelCase__         =	self._find_encoding_token(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  									# update the search buffer:
  									# - add new characters from text into it
  									# - check if size exceed the max search buffer size, if so, drop the
  									#   oldest elements
  									search_buffer += text[: token.length + 1]
  									if len(__lowerCAmelCase       ) > self.search_buffer_size:
  											UpperCamelCase__         =	search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									UpperCamelCase__         =	text[token.length + 1 :]
  
  									# append the token to output
  									output.append(__lowerCAmelCase       )
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	""""""
  
  							for token in tokens:
  									for _ in range(token.length       ):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Token:
  
  							if not text:
  									raise ValueError("""We need some text to work with."""       )
  
  							# Initialise result parameters to default values
  							UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  
  							if not search_buffer:
  									return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  							for i, character in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	len(__lowerCAmelCase       ) - i
  									if character == text[0]:
  											UpperCamelCase__         =	self._match_length_from_index(__lowerCAmelCase    ,      __lowerCAmelCase    ,      0    ,      __lowerCAmelCase       )
  											# if the found length is bigger than the current or if it's equal,
  											# which means it's offset is smaller: update offset and length
  											if found_length >= length:
  													UpperCamelCase__  ,			UpperCamelCase__         =	found_offset, found_length
  
  							return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    __lowerCAmelCase    ,      window + text[text_index]    ,      text_index + 1    ,      window_index + 1       )
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				# Initialize compressor class
  				__lowerCAmelCase     =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  				# Example
  				__lowerCAmelCase     =			"cabracadabrarrarrad"
  				__lowerCAmelCase     =			lzaa_compressor.compress(TEXT)
  				print(lzaa_compressor.compress("ababcbababaa"))
  				__lowerCAmelCase     =			lzaa_compressor.decompress(compressed_text)
  				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  