code: 
  
  
  
  
  import numpy as np
  def 						lowerCAmelCase_       (	__a	,	__a="utf-8"	,	__a="surrogatepass" )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =bin(int.from_bytes(text.encode(__a	,	__a )	,	'''big''' ) )[2:]
  							return bits.zfill(8 * ((len(__a ) + 7) // 8) )
  def 						lowerCAmelCase_       (	__a	,	__a="utf-8"	,	__a="surrogatepass" )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =int(__a	,	2 )
  							return n.to_bytes((n.bit_length() + 7) // 8	,	'''big''' ).decode(__a	,	__a ) or "\0"
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if size_par + len(__a ) <= 2**size_par - (len(__a ) - 1):
  														raise ValueError('''size of parity don\'t match with size of data''' )
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  							SCREAMING_SNAKE_CASE			: int	     =[]
  							SCREAMING_SNAKE_CASE			: int	     =[bin(__a )[2:] for x in range(1	,	size_par + len(__a ) + 1 )]
  
  							# sorted information data for the size of the output data
  							SCREAMING_SNAKE_CASE			: int	     =[]
  							# data position template + parity
  							SCREAMING_SNAKE_CASE			: List[Any]	     =[]
  							# parity bit counter
  							SCREAMING_SNAKE_CASE			: Tuple	     =0
  							# counter position of data bits
  							SCREAMING_SNAKE_CASE			: List[str]	     =0
  
  							for x in range(1	,	size_par + len(__a ) + 1 ):
  														# Performs a template of bit positions - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par:
  																					if (np.log(__a ) / np.log(2 )).is_integer():
  																												data_out_gab.append('''P''' )
  																												SCREAMING_SNAKE_CASE			: Dict	     =qtd_bp + 1
  																					else:
  																												data_out_gab.append('''D''' )
  														else:
  																					data_out_gab.append('''D''' )
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data[cont_data] )
  																					cont_data += 1
  														else:
  																					data_ord.append(__a )
  
      # Calculates parity
  							SCREAMING_SNAKE_CASE			: List[Any]	     =0  # parity bit counter
  							for bp in range(1	,	size_par + 1 ):
  														# Bit counter one for a given parity
  														SCREAMING_SNAKE_CASE			: Tuple	     =0
  														# counter to control the loop reading
  														SCREAMING_SNAKE_CASE			: List[str]	     =0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			SCREAMING_SNAKE_CASE			: Any	     =(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			SCREAMING_SNAKE_CASE			: int	     ='''0'''
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(cont_bo % 2 )
  
  														qtd_bp += 1
  
  							# Mount the message
  							SCREAMING_SNAKE_CASE			: Any	     =0  # parity bit counter
  							for x in range(0	,	size_par + len(__a ) ):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp] ) )
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x] )
  
  							return data_out
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							# Parity bit counter
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =0
  							# Counter p data bit reading
  							SCREAMING_SNAKE_CASE			: List[str]	     =0
  							# list of parity received
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							SCREAMING_SNAKE_CASE			: List[Any]	     =[]
  
  							for x in range(1	,	len(__a ) + 1 ):
  														# Performs a template of bit positions - who should be given,
  														#  and who should be parity
  														if qtd_bp < size_par and (np.log(__a ) / np.log(2 )).is_integer():
  																					data_out_gab.append('''P''' )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =qtd_bp + 1
  														else:
  																					data_out_gab.append('''D''' )
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_output.append(data[cont_data] )
  														else:
  																					parity_received.append(data[cont_data] )
  														cont_data += 1
  
  							# -----------calculates the parity with the data
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[bin(__a )[2:] for x in range(1	,	size_par + len(__a ) + 1 )]
  
  							#  sorted information data for the size of the output data
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							# Data position feedback + parity
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							# Parity bit counter
  							SCREAMING_SNAKE_CASE			: List[str]	     =0
  							# Counter p data bit reading
  							SCREAMING_SNAKE_CASE			: Any	     =0
  
  							for x in range(1	,	size_par + len(__a ) + 1 ):
  														# Performs a template position of bits - who should be given,
  														# and who should be parity
  														if qtd_bp < size_par and (np.log(__a ) / np.log(2 )).is_integer():
  																					data_out_gab.append('''P''' )
  																					SCREAMING_SNAKE_CASE			: Any	     =qtd_bp + 1
  														else:
  																					data_out_gab.append('''D''' )
  
  														# Sorts the data to the new output size
  														if data_out_gab[-1] == "D":
  																					data_ord.append(data_output[cont_data] )
  																					cont_data += 1
  														else:
  																					data_ord.append(__a )
  
      # Calculates parity
  							SCREAMING_SNAKE_CASE			: Tuple	     =0  # parity bit counter
  							for bp in range(1	,	size_par + 1 ):
  														# Bit counter one for a certain parity
  														SCREAMING_SNAKE_CASE			: List[Any]	     =0
  														# Counter to control loop reading
  														SCREAMING_SNAKE_CASE			: Any	     =0
  														for x in data_ord:
  																					if x is not None:
  																												try:
  																																			SCREAMING_SNAKE_CASE			: Optional[int]	     =(bin_pos[cont_loop])[-1 * (bp)]
  																												except IndexError:
  																																			SCREAMING_SNAKE_CASE			: Optional[int]	     ='''0'''
  																												if aux == "1" and x == "1":
  																																			cont_bo += 1
  																					cont_loop += 1
  														parity.append(str(cont_bo % 2 ) )
  
  														qtd_bp += 1
  
  							# Mount the message
  							SCREAMING_SNAKE_CASE			: List[str]	     =0  # Parity bit counter
  							for x in range(0	,	size_par + len(__a ) ):
  														if data_ord[x] is None:
  																					data_out.append(str(parity[cont_bp] ) )
  																					cont_bp += 1
  														else:
  																					data_out.append(data_ord[x] )
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =parity_received == parity
  							return data_output, ack
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import argparse
  import json
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from typing import List
  
  import torch
  import torch.nn as nn
  from huggingface_hub import cached_download, hf_hub_download
  from torch import Tensor
  
  from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
  from transformers.models.deprecated.van.modeling_van import VanLayerScaling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _A							    =	logging.get_logger(__name__)
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Tuple	     =len(list(m.modules()							)							) == 1 or isinstance(snake_case_		,		nn.Convad							) or isinstance(snake_case_		,		nn.BatchNormad							)
  											if has_not_submodules:
  																		if not isinstance(snake_case_		,		snake_case_							):
  																									self.traced.append(snake_case_							)
  
  
  
  
  				def __call__( self		,		snake_case_							)    ->      List[Any]:
  											for m in self.module.modules():
  																		self.handles.append(m.register_forward_hook(self._forward_hook							)							)
  											self.module(snake_case_							)
  											[x.remove() for x in self.handles]
  											return self
  
  
  
  
  
  
  
  				@property
  				def     __a		( self							)    ->      int:
  											# check the len of the state_dict keys to see if we have learnable params
  											return list(filter(lambda snake_case_							: len(list(x.state_dict().keys()							)							) > 0		,		self.traced							)							)
  
  
  
  
  
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    42
  				lowerCamelCase__									=    0
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
  
  
  
  
  				def __call__( self		,		snake_case_							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =Tracker(self.dest							)(snake_case_							).parametrized
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =Tracker(self.src							)(snake_case_							).parametrized
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.src_skip		,		snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: Dict	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.dest_skip		,		snake_case_							)							)
  
  											if len(snake_case_							) != len(snake_case_							):
  																		raise Exception(
  																		    F'Numbers of operations are different. Source module has {len(snake_case_							)} operations while'
  																		    F' destination module has {len(snake_case_							)}.'							)
  
  											for dest_m, src_m in zip(snake_case_		,		snake_case_							):
  																		dest_m.load_state_dict(src_m.state_dict()							)
  																		if self.verbose == 1:
  																									print(F'Transfered from={src_m} to={dest_m}'							)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			nn.Module:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =from_model.state_dict()
  							SCREAMING_SNAKE_CASE			: List[str]	     =our_model.state_dict()
  							SCREAMING_SNAKE_CASE			: int	     =our_model.config
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							for stage_idx in range(len(config.hidden_sizes ) ):
  														for block_id in range(config.depths[stage_idx] ):
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =f'block{stage_idx + 1}.{block_id}.layer_scale_1'
  																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
  																					all_keys.append((from_key, to_key) )
  																					SCREAMING_SNAKE_CASE			: int	     =f'block{stage_idx + 1}.{block_id}.layer_scale_2'
  																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'
  
  																					all_keys.append((from_key, to_key) )
  
  							for from_key, to_key in all_keys:
  														SCREAMING_SNAKE_CASE			: List[Any]	     =from_state_dict.pop(__a )
  
  							our_model.load_state_dict(__a )
  							return our_model
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a = True	,	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							print(f'Downloading weights for {name}...' )
  							SCREAMING_SNAKE_CASE			: Dict	     =cached_download(__a )
  							print(f'Converting {name}...' )
  							SCREAMING_SNAKE_CASE			: Dict	     =torch.load(__a )['''state_dict''']
  							from_model.load_state_dict(__a )
  							from_model.eval()
  							with torch.no_grad():
  														SCREAMING_SNAKE_CASE			: int	     =VanForImageClassification(__a ).eval()
  														SCREAMING_SNAKE_CASE			: Tuple	     =ModuleTransfer(src=__a	,	dest=__a )
  														SCREAMING_SNAKE_CASE			: Any	     =torch.randn((1, 3, 224, 224) )
  														module_transfer(__a )
  														SCREAMING_SNAKE_CASE			: str	     =copy_parameters(__a	,	__a )
  
  							if not torch.allclose(from_model(__a )	,	our_model(__a ).logits ):
  														raise ValueError('''The model logits don\'t match the original one.''' )
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =name
  							print(__a )
  
  							if push_to_hub:
  														our_model.push_to_hub(
  														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=__a	,	)
  
  														# we can use the convnext one
  														SCREAMING_SNAKE_CASE			: Tuple	     =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
  														image_processor.push_to_hub(
  														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=__a	,	)
  
  														print(f'Pushed {checkpoint_name}' )
  def 						lowerCAmelCase_       (	__a	,	__a = None	,	__a = True )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''imagenet-1k-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =1000
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =num_labels
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =json.load(open(hf_hub_download(__a	,	__a	,	repo_type='''dataset''' )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={int(__a ): v for k, v in idalabel.items()}
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Tuple	     ={v: k for k, v in idalabel.items()}
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =partial(__a	,	num_labels=__a	,	idalabel=__a	,	labelaid=__a )
  
  							SCREAMING_SNAKE_CASE			: int	     ={
  							    '''van-tiny''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[32, 64, 160, 256]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-small''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-base''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							    '''van-large''': ImageNetPreTrainedConfig(
  							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  							}
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={
  							    '''van-tiny''': van_tiny,
  							    '''van-small''': van_small,
  							    '''van-base''': van_base,
  							    '''van-large''': van_large,
  							}
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     ={
  							    '''van-tiny''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
  							    ),
  							    '''van-small''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
  							    ),
  							    '''van-base''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
  							    ),
  							    '''van-large''': (
  							        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
  							    ),
  							}
  
  							if model_name:
  														convert_weight_and_push(
  														    __a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
  							else:
  														for model_name, config in names_to_config.items():
  																					convert_weight_and_push(
  																					    __a	,	__a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
  
  
  if __name__ == "__main__":
  							_A							    =	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 resnet* architecture,"""
  							        """ currently: van-tiny/small/base/large. 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(
  							    """--van_dir""",
  							    required=True,
  							    type=Path,
  							    help=(
  							        """A path to VAN's original implementation directory. You can download from here:"""
  							        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
  							    ),
  							)
  							parser.add_argument(
  							    """--push_to_hub""",
  							    default=True,
  							    type=bool,
  							    required=False,
  							    help="""If True, push model and image processor to the hub.""",
  							)
  
  							_A							    =	parser.parse_args()
  							_A							    =	args.pytorch_dump_folder_path
  							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  							_A							    =	args.van_dir
  							# append the path to the parents to maskformer dir
  							sys.path.append(str(van_dir.parent))
  							from van.models.van import van_base, van_large, van_small, van_tiny
  
  							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
  
  
  
  
  
  
  