code: 
  
  
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      None:
  											SCREAMING_SNAKE_CASE			: Any	     ={literal: None for literal in literals}
  
  
  
  
  				def __str__( self							)    ->      str:
  											return "{" + " , ".join(self.literals							) + "}"
  
  
  
  
  				def __len__( self							)    ->      int:
  											return len(self.literals							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      None:
  
  
  
  
  
  
  											for literal in self.literals:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =literal[:2]
  																		if symbol in model:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =model[symbol]
  																		else:
  																									continue
  																		if value is not None:
  																									# Complement assignment if literal is in complemented form
  																									if literal.endswith('''\''''							):
  																																SCREAMING_SNAKE_CASE			: Optional[int]	     =not value
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =value
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      bool | None:
  
  
  
  
  
  
  											for literal in self.literals:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =literal.rstrip('''\''''							) if literal.endswith('''\''''							) else literal + '''\''''
  																		if symbol in self.literals:
  																									return True
  
  											self.assign(lowerCAmelCase_							)
  											for value in self.literals.values():
  																		if value in (True, None):
  																									return value
  											return any(self.literals.values()							)
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      None:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =list(lowerCAmelCase_							)
  
  
  
  
  
  
  
  				def __str__( self							)    ->      str:
  											return "{" + " , ".join(str(lowerCAmelCase_							) for clause in self.clauses							) + "}"
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	)       ->			Clause:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =random.randint(1	,	5 )
  							SCREAMING_SNAKE_CASE			: Any	     ='''A'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =0
  							while i < no_of_literals:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =random.randint(1	,	5 )
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =base_var + str(__a )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =random.randint(0	,	1 )
  														if var_complement == 1:
  																					var_name += "'"
  														if var_name in literals:
  																					i -= 1
  														else:
  																					literals.append(__a )
  														i += 1
  							return Clause(__a )
  def 						lowerCAmelCase_       (	)       ->			Formula:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =set()
  							SCREAMING_SNAKE_CASE			: Tuple	     =random.randint(1	,	10 )
  							while len(__a ) < no_of_clauses:
  														clauses.add(generate_clause() )
  							return Formula(__a )
  def 						lowerCAmelCase_       (	__a )       ->			tuple[list[Clause], list[str]]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =formula.clauses
  							SCREAMING_SNAKE_CASE			: Tuple	     =[]
  							for clause in formula.clauses:
  														for literal in clause.literals:
  																					SCREAMING_SNAKE_CASE			: str	     =literal[:2]
  																					if symbol not in symbols_set:
  																												symbols_set.append(__a )
  							return clauses, symbols_set
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			tuple[list[str], dict[str, bool | None]]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							SCREAMING_SNAKE_CASE			: List[Any]	     ={}
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
  
  							for clause in clauses:
  														if clause.evaluate(__a ):
  																					continue
  														for literal in clause.literals:
  																					literals.append(__a )
  
  							for s in symbols:
  														SCREAMING_SNAKE_CASE			: int	     =s + '''\''''
  														if (s in literals and sym not in literals) or (
  														    s not in literals and sym in literals
  														):
  																					pure_symbols.append(__a )
  							for p in pure_symbols:
  														SCREAMING_SNAKE_CASE			: Any	     =None
  							for s in pure_symbols:
  														SCREAMING_SNAKE_CASE			: Tuple	     =s + '''\''''
  														if s in literals:
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =True
  														elif sym in literals:
  																					SCREAMING_SNAKE_CASE			: str	     =False
  							return pure_symbols, assignment
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			tuple[list[str], dict[str, bool | None]]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
  							for clause in clauses:
  														if len(__a ) == 1:
  																					unit_symbols.append(list(clause.literals.keys() )[0] )
  														else:
  																					SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[str]	     =0, 0
  																					for literal, value in clause.literals.items():
  																												if value is False:
  																																			f_count += 1
  																												elif value is None:
  																																			SCREAMING_SNAKE_CASE			: int	     =literal
  																																			n_count += 1
  																					if f_count == len(__a ) - 1 and n_count == 1:
  																												unit_symbols.append(__a )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     ={}
  							for i in unit_symbols:
  														SCREAMING_SNAKE_CASE			: str	     =i[:2]
  														SCREAMING_SNAKE_CASE			: int	     =len(__a ) == 2
  							SCREAMING_SNAKE_CASE			: List[str]	     =[i[:2] for i in unit_symbols]
  
  							return unit_symbols, assignment
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			tuple[bool | None, dict[str, bool | None] | None]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =True
  							for clause in clauses:
  														SCREAMING_SNAKE_CASE			: Tuple	     =clause.evaluate(__a )
  														if clause_check is False:
  																					return False, None
  														elif clause_check is None:
  																					SCREAMING_SNAKE_CASE			: str	     =False
  																					continue
  
  							if check_clause_all_true:
  														return True, model
  
  							try:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =find_pure_symbols(__a	,	__a	,	__a )
  							except RecursionError:
  														print('''raises a RecursionError and is''' )
  														return None, {}
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =None
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: str	     =pure_symbols[0], assignment[pure_symbols[0]]
  
  							if p:
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =model
  														SCREAMING_SNAKE_CASE			: int	     =value
  														SCREAMING_SNAKE_CASE			: str	     =list(__a )
  														if p in tmp_symbols:
  																					tmp_symbols.remove(__a )
  														return dpll_algorithm(__a	,	__a	,	__a )
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: int	     =find_unit_clauses(__a	,	__a )
  							SCREAMING_SNAKE_CASE			: Tuple	     =None
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =unit_symbols[0], assignment[unit_symbols[0]]
  							if p:
  														SCREAMING_SNAKE_CASE			: str	     =model
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =value
  														SCREAMING_SNAKE_CASE			: Tuple	     =list(__a )
  														if p in tmp_symbols:
  																					tmp_symbols.remove(__a )
  														return dpll_algorithm(__a	,	__a	,	__a )
  							SCREAMING_SNAKE_CASE			: List[str]	     =symbols[0]
  							SCREAMING_SNAKE_CASE			: int	     =symbols[1:]
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: int	     =model, model
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[str]	     =True, False
  
  							return dpll_algorithm(__a	,	__a	,	__a ) or dpll_algorithm(__a	,	__a	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  							_A							    =	generate_formula()
  							print(F'''The formula {formula} is''', end=""" """)
  
  							_A    , _A							    =	generate_parameters(formula)
  							_A    , _A							    =	dpll_algorithm(clauses, symbols, {})
  
  							if solution:
  														print(F'''satisfiable with the assignment {model}.''')
  							else:
  														print("""not satisfiable.""")
  
  
  
  
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)
  
  
  
  
  
  
  