code: 
  
  
  
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
  							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
  							_A							    =	False
  
  if is_vision_available():
  							from PIL import Image
  
  							from transformers import (
  							    AutoProcessor,
  							    PixaStructImageProcessor,
  							    PixaStructProcessor,
  							    PreTrainedTokenizerFast,
  							    TaTokenizer,
  							)
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11  ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'  ,   )
  @require_vision
  @require_torch
  class 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =tempfile.mkdtemp()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructImageProcessor()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =TaTokenizer.from_pretrained('''t5-small'''							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =PixaStructProcessor(snake_case_		,		snake_case_							)
  
  											processor.save_pretrained(self.tmpdirname							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Optional[int]:
  											return AutoProcessor.from_pretrained(self.tmpdirname		,		**snake_case_							).tokenizer
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Any:
  											return AutoProcessor.from_pretrained(self.tmpdirname		,		**snake_case_							).image_processor
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											shutil.rmtree(self.tmpdirname							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta							)]
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =[Image.fromarray(np.moveaxis(snake_case_		,		0		,		-1							)							) for x in image_inputs]
  
  											return image_inputs
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: int	     =PixaStructProcessor(tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor()							)
  											processor.save_pretrained(self.tmpdirname							)
  
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer(bos_token='''(BOS)'''		,		eos_token='''(EOS)'''							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.get_image_processor(do_normalize=snake_case_		,		padding_value=1.0							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =PixaStructProcessor.from_pretrained(
  											    self.tmpdirname		,		bos_token='''(BOS)'''		,		eos_token='''(EOS)'''		,		do_normalize=snake_case_		,		padding_value=1.0							)
  
  											self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab()							)
  											self.assertIsInstance(processor.tokenizer		,		snake_case_							)
  
  											self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string()							)
  											self.assertIsInstance(processor.image_processor		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =image_processor(snake_case_		,		return_tensors='''np'''							)
  											SCREAMING_SNAKE_CASE			: str	     =processor(images=snake_case_		,		return_tensors='''np'''							)
  
  											for key in input_feat_extract.keys():
  																		self.assertAlmostEqual(input_feat_extract[key].sum()		,		input_processor[key].sum()		,		delta=1E-2							)
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     ='''lower newer'''
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =tokenizer(snake_case_		,		return_token_type_ids=snake_case_		,		add_special_tokens=snake_case_							)
  
  											for key in encoded_tok.keys():
  																		self.assertListEqual(encoded_tok[key]		,		encoded_processor[key]							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: Dict	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											self.assertListEqual(
  											    list(inputs.keys()							)		,		['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']							)
  
  											# test if it raises when no input is passed
  											with pytest.raises(snake_case_							):
  																		processor()
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: str	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: int	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     =[512, 1_024, 2_048, 4_096]
  											SCREAMING_SNAKE_CASE			: List[Any]	     =[770, 770, 770, 770]
  											# with text
  											for i, max_patch in enumerate(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =processor(text=snake_case_		,		images=snake_case_		,		max_patches=snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[0]		,		snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[1]		,		expected_hidden_size[i]							)
  
  											# without text input
  											for i, max_patch in enumerate(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =processor(images=snake_case_		,		max_patches=snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[0]		,		snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[1]		,		expected_hidden_size[i]							)
  
  
  
  
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =processor.batch_decode(snake_case_							)
  											SCREAMING_SNAKE_CASE			: str	     =tokenizer.batch_decode(snake_case_							)
  
  											self.assertListEqual(snake_case_		,		snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Any	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Any	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  											self.assertListEqual(
  											    list(inputs.keys()							)		,		['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =processor(text=snake_case_							)
  
  											# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
  											self.assertListEqual(list(inputs.keys()							)		,		['''input_ids''', '''attention_mask''']							)
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  