code: 
  
  
  
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  @require_tokenizers
  class 							_lowerCAmelCase		(     UpperCAmelCase__	):
  				lowerCamelCase__									=    SqueezeBertTokenizer
  				lowerCamelCase__									=    SqueezeBertTokenizerFast
  				lowerCamelCase__									=    True
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      int:
  											return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname		,		**lowerCamelCase__							)
  
  
  
  
  
  
  
  				@slow
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =tokenizer.encode('''sequence builders'''		,		add_special_tokens=lowerCamelCase__							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer.encode('''multi-sequence build'''		,		add_special_tokens=lowerCamelCase__							)
  
  											SCREAMING_SNAKE_CASE			: str	     =tokenizer.build_inputs_with_special_tokens(lowerCamelCase__							)
  											SCREAMING_SNAKE_CASE			: Any	     =tokenizer.build_inputs_with_special_tokens(lowerCamelCase__		,		lowerCamelCase__							)
  
  											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
  											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
  											    tokenizer.sep_token_id
  											]
  
  
  
  
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)
  
  
  
  
  
  
  