code: 
  
  
  
  
  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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)
  
  											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1
  
  											# Number of slack variables added to make inequalities into equalities
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages
  
  											# Objectives for each stage
  											SCREAMING_SNAKE_CASE			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: int	     =[]
  											for i in range(3							):
  																		for j in range(args[i]							):
  																									titles.append(string_starts[i] + str(j + 1							)							)
  											titles.append('''RHS'''							)
  											return titles
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.argmax(sign * tableau[0, : self.n_vars]							)
  
  											# Choice is only valid if below 0 for maximise, and above for minimise
  											if sign * self.tableau[0, col_idx] <= 0:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =True
  																		return 0, 0
  
  											# Pivot row is chosen as having the lowest quotient when elements of
  											# the pivot column divide the right-hand side
  
  											# Slice excluding the objective rows
  											SCREAMING_SNAKE_CASE			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.full(self.n_rows - self.n_stages		,		np.nan							)
  
  											# If element in pivot column is greater than zeron_stages, return
  											# quotient or nan otherwise
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=snake_case_		,		where=divisor > 0							)
  
  											# Arg of minimum quotient excluding the nan values. n_stages is added
  											# to compensate for earlier exclusion of objective columns
  											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =piv_row[col_idx]
  
  											# Entry becomes 1
  											piv_row *= 1 / piv_val
  
  											# Variable in pivot column becomes basic, ie the only non-zero entry
  											for idx, coeff in enumerate(tableau[:, col_idx]							):
  																		tableau[idx] += -coeff * piv_row
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( self							)    ->      dict[Any, Any]:
  											# Stop simplex algorithm from cycling.
  											for _ in range(100							):
  																		# Completion of each stage removes an objective. If both stages
  																		# are complete, then no objectives are left
  																		if not self.objectives:
  																									SCREAMING_SNAKE_CASE			: int	     =self.generate_col_titles(
  																									    self.n_vars		,		self.n_slack		,		self.n_art_vars							)
  
  																									# Find the values of each variable at optimal solution
  																									return self.interpret_tableau(self.tableau		,		self.col_titles							)
  
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =self.find_pivot(self.tableau							)
  
  																		# If there are no more negative values in objective row
  																		if self.stop_iter:
  																									# Delete artificial variable columns and rows. Update attributes
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: Any	     =tableau[nonzero_rowidx, i]
  
  																		# If there is only one nonzero value in column, which is one
  																		if n_nonzero == nonzero_val == 1:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =rhs_val
  
          # Check for basic variables
  											for title in col_titles:
  																		# Don't add RHS or slack variables to output dict
  																		if title[0] not in "R-s-a":
  																									output_dict.setdefault(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  