code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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      :				Optional[int]											=					logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 					_UpperCAmelCase							:
  		a     :     Union[str, Any]     						=42
  		a     :     Optional[int]     						=field(default_factory=UpperCamelCase__     )
  		a     :     Optional[int]     						=field(default_factory=UpperCamelCase__     )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       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,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					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 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return list(filter(lambda __SCREAMING_SNAKE_CASE    : len(list(x.state_dict().keys()    )    ) > 0,self.traced    )    )
  
  
  
  
  
  @dataclass
  class 					_UpperCAmelCase							:
  		a     :     List[str]     						=42
  		a     :     Dict     						=42
  		a     :     List[Any]     						=0
  		a     :     Tuple     						=field(default_factory=UpperCamelCase__     )
  		a     :     Union[str, Any]     						=field(default_factory=UpperCamelCase__     )
  		def __call__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       Tracker(self.dest    )(snake_case_    ).parametrized
  					__lowerCAmelCase      		=       Tracker(self.src    )(snake_case_    ).parametrized
  
  					__lowerCAmelCase      		=       list(filter(lambda __SCREAMING_SNAKE_CASE    : type(snake_case_    ) not in self.src_skip,snake_case_    )    )
  					__lowerCAmelCase      		=       list(filter(lambda __SCREAMING_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		(		lowercase      ,			lowercase			)      ->      nn.Module:
  			__lowerCAmelCase      		=       from_model.state_dict()
  			__lowerCAmelCase      		=       our_model.state_dict()
  			__lowerCAmelCase      		=       our_model.config
  			__lowerCAmelCase      		=       []
  			for stage_idx in range(len(config.hidden_sizes			)			):
  						for block_id in range(config.depths[stage_idx]			):
  									__lowerCAmelCase      		=       f'block{stage_idx + 1}.{block_id}.layer_scale_1'
  									__lowerCAmelCase      		=       f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
  									all_keys.append((from_key, to_key)			)
  									__lowerCAmelCase      		=       f'block{stage_idx + 1}.{block_id}.layer_scale_2'
  									__lowerCAmelCase      		=       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:
  						__lowerCAmelCase      		=       from_state_dict.pop(__a			)
  
  			our_model.load_state_dict(__a			)
  			return our_model
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase = True      ,			)      ->      Any:
  			print(f'Downloading weights for {name}...'			)
  			__lowerCAmelCase      		=       cached_download(__a			)
  			print(f'Converting {name}...'			)
  			__lowerCAmelCase      		=       torch.load(__a			)['''state_dict''']
  			from_model.load_state_dict(__a			)
  			from_model.eval()
  			with torch.no_grad():
  						__lowerCAmelCase      		=       VanForImageClassification(__a			).eval()
  						__lowerCAmelCase      		=       ModuleTransfer(src=__a      ,			dest=__a			)
  						__lowerCAmelCase      		=       torch.randn((1, 3, 224, 224)			)
  						module_transfer(__a			)
  						__lowerCAmelCase      		=       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."""			)
  
  			__lowerCAmelCase      		=       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
  						__lowerCAmelCase      		=       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		(		lowercase      ,			lowercase = None      ,			lowercase = True			)      ->      int:
  			__lowerCAmelCase      		=       '''imagenet-1k-id2label.json'''
  			__lowerCAmelCase      		=       1000
  
  			__lowerCAmelCase      		=       '''huggingface/label-files'''
  			__lowerCAmelCase      		=       num_labels
  			__lowerCAmelCase      		=       json.load(open(hf_hub_download(__a      ,			__a      ,			repo_type="""dataset"""			)      ,			"""r"""			)			)
  			__lowerCAmelCase      		=       {int(__a			): v for k, v in idalabel.items()}
  
  			__lowerCAmelCase      		=       idalabel
  			__lowerCAmelCase      		=       {v: k for k, v in idalabel.items()}
  
  			__lowerCAmelCase      		=       partial(__a      ,			num_labels=__a      ,			idalabel=__a      ,			labelaid=__a			)
  
  			__lowerCAmelCase      		=       {
  			    '''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]      ,			),
  			}
  
  			__lowerCAmelCase      		=       {
  			    '''van-tiny''': van_tiny,
  			    '''van-small''': van_small,
  			    '''van-base''': van_base,
  			    '''van-large''': van_large,
  			}
  
  			__lowerCAmelCase      		=       {
  			    '''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      :				int											=					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      :				Optional[int]											=					parser.parse_args()
  	_a      :				str											=					args.pytorch_dump_folder_path
  	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  	_a      :				List[str]											=					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: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
  			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]
  
  			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
  						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue
  
  			def normalize(lowercase			):
  						return min(lowercase      ,			255			)
  
  			for i in range(lowercase			):
  						for j in range(lowercase			):
  									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
  									__lowerCAmelCase      		=       [
  									    normalize(lowercase			),
  									    normalize(greyscale + factor			),
  									    normalize(greyscale + 2 * factor			),
  									]
  
  			return img
  
  
  if __name__ == "__main__":
  	# read original image
  	_a      :				Any											=					{
  	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
  	}
  
  	for percentage, img in images.items():
  		make_sepia(img, percentage)
  
  	for percentage, img in images.items():
  		imshow(f'Original image with sepia (factor: {percentage})', img)
  
  	waitKey(0)
  	destroyAllWindows()
  
  