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()
  lowerCAmelCase								=    logging.get_logger(__name__)
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    nn.Module
  		A_			:    List[nn.Module]   					=       field(default_factory=lowercase__							)
  		A_			:    list   					=       field(default_factory=lowercase__							)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[str]						,  _A	:		Tensor						,  _A	:		Tensor    )						->				Union[str, Any]:
  			__magic_name__ :					Optional[int]  			=  len(list(m.modules()    )    ) == 1 or isinstance(_A						,  nn.Convad    ) or isinstance(_A						,  nn.BatchNormad    )
  			if has_not_submodules:
  				if not isinstance(_A						,  _A    ):
  					self.traced.append(_A    )
  
  
  
  
  
  
  
  		def __call__(      self	:		Union[str, Any]						,  _A	:		Tensor    )						->				Union[str, Any]:
  			for m in self.module.modules():
  				self.handles.append(m.register_forward_hook(self._forward_hook    )    )
  			self.module(_A    )
  			[x.remove() for x in self.handles]
  			return self
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Union[str, Any]:
  			# check the len of the state_dict keys to see if we have learnable params
  			return list(filter(lambda _A    : len(list(x.state_dict().keys()    )    ) > 0						,  self.traced    )    )
  
  
  
  
  
  
  
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    nn.Module
  		A_			:    nn.Module
  		A_			:    int   					=       0
  		A_			:    List   					=       field(default_factory=lowercase__							)
  		A_			:    List   					=       field(default_factory=lowercase__							)
  
  
  
  
  
  
  
  		def __call__(      self	:		Tuple						,  _A	:		Tensor    )						->				List[Any]:
  			__magic_name__ :					Dict  			=  Tracker(self.dest    )(_A    ).parametrized
  			__magic_name__ :					List[Any]  			=  Tracker(self.src    )(_A    ).parametrized
  
  			__magic_name__ :					List[Any]  			=  list(filter(lambda _A    : type(_A    ) not in self.src_skip						,  _A    )    )
  			__magic_name__ :					List[Any]  			=  list(filter(lambda _A    : type(_A    ) not in self.dest_skip						,  _A    )    )
  
  			if len(_A    ) != len(_A    ):
  				raise Exception(
  				    F'Numbers of operations are different. Source module has {len(_A    )} operations while'
  				    F' destination module has {len(_A    )}.'    )
  
  			for dest_m, src_m in zip(_A						,  _A    ):
  				dest_m.load_state_dict(src_m.state_dict()    )
  				if self.verbose == 1:
  					print(F'Transfered from={src_m} to={dest_m}'    )
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			nn.Module	,	lowerCAmelCase       :			nn.Module	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  from_model.state_dict()
  	__magic_name__ :					List[str]  			=  our_model.state_dict()
  	__magic_name__ :					Optional[int]  			=  our_model.config
  	__magic_name__ :					List[str]  			=  []
  	for stage_idx in range(len(config.hidden_sizes	)	):
  		for block_id in range(config.depths[stage_idx]	):
  			__magic_name__ :					List[str]  			=  f'block{stage_idx + 1}.{block_id}.layer_scale_1'
  			__magic_name__ :					Optional[int]  			=  f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
  			all_keys.append((from_key, to_key)	)
  			__magic_name__ :					List[str]  			=  f'block{stage_idx + 1}.{block_id}.layer_scale_2'
  			__magic_name__ :					Optional[Any]  			=  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:
  		__magic_name__ :					Optional[Any]  			=  from_state_dict.pop(lowerCAmelCase	)
  
  	our_model.load_state_dict(lowerCAmelCase	)
  	return our_model
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	,	lowerCAmelCase       :			VanConfig	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			nn.Module	,	lowerCAmelCase       :			Path	,	lowerCAmelCase       :			bool = True	,	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	print(f'Downloading weights for {name}...'	)
  	__magic_name__ :					str  			=  cached_download(lowerCAmelCase	)
  	print(f'Converting {name}...'	)
  	__magic_name__ :					List[Any]  			=  torch.load(lowerCAmelCase	)['state_dict']
  	from_model.load_state_dict(lowerCAmelCase	)
  	from_model.eval()
  	with torch.no_grad():
  		__magic_name__ :					int  			=  VanForImageClassification(lowerCAmelCase	).eval()
  		__magic_name__ :					int  			=  ModuleTransfer(src=lowerCAmelCase	,	dest=lowerCAmelCase	)
  		__magic_name__ :					List[str]  			=  torch.randn((1, 3, 224, 224)	)
  		module_transfer(lowerCAmelCase	)
  		__magic_name__ :					Tuple  			=  copy_parameters(lowerCAmelCase	,	lowerCAmelCase	)
  
  	if not torch.allclose(from_model(lowerCAmelCase	)	,	our_model(lowerCAmelCase	).logits	):
  		raise ValueError('The model logits don\'t match the original one.'	)
  
  	__magic_name__ :					Any  			=  name
  	print(lowerCAmelCase	)
  
  	if push_to_hub:
  		our_model.push_to_hub(
  		    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='Add model'	,	use_temp_dir=lowerCAmelCase	,	)
  
  		# we can use the convnext one
  		__magic_name__ :					Optional[int]  			=  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=lowerCAmelCase	,	)
  
  		print(f'Pushed {checkpoint_name}'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Path	,	lowerCAmelCase       :			str = None	,	lowerCAmelCase       :			bool = True	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[str]  			=  'imagenet-1k-id2label.json'
  	__magic_name__ :					List[Any]  			=  1000
  
  	__magic_name__ :					Any  			=  'huggingface/label-files'
  	__magic_name__ :					List[Any]  			=  num_labels
  	__magic_name__ :					int  			=  json.load(open(hf_hub_download(lowerCAmelCase	,	lowerCAmelCase	,	repo_type='dataset'	)	,	'r'	)	)
  	__magic_name__ :					List[str]  			=  {int(lowerCAmelCase	): v for k, v in idalabel.items()}
  
  	__magic_name__ :					str  			=  idalabel
  	__magic_name__ :					Union[str, Any]  			=  {v: k for k, v in idalabel.items()}
  
  	__magic_name__ :					int  			=  partial(lowerCAmelCase	,	num_labels=lowerCAmelCase	,	idalabel=lowerCAmelCase	,	labelaid=lowerCAmelCase	)
  
  	__magic_name__ :					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]	,	),
  	}
  
  	__magic_name__ :					Optional[int]  			=  {
  	    'van-tiny': van_tiny,
  	    'van-small': van_small,
  	    'van-base': van_base,
  	    'van-large': van_large,
  	}
  
  	__magic_name__ :					Tuple  			=  {
  	    '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(
  		    lowerCAmelCase	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=lowerCAmelCase	,	push_to_hub=lowerCAmelCase	,	)
  	else:
  		for model_name, config in names_to_config.items():
  			convert_weight_and_push(
  			    lowerCAmelCase	,	lowerCAmelCase	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=lowerCAmelCase	,	push_to_hub=lowerCAmelCase	,	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase								=    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.''',
  							)
  
  							lowerCAmelCase								=    parser.parse_args()
  							lowerCAmelCase								=    args.pytorch_dump_folder_path
  							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  							lowerCAmelCase								=    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 sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  lowerCAmelCase   :Union[str, Any]								=    '''\
  @article{scikit-learn,
   title={Scikit-learn: Machine Learning in {P}ython},
   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
   year={2011}
  }
  '''
  
  lowerCAmelCase   :int								=    '''\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  '''
  
  
  lowerCAmelCase   :Optional[int]								=    '''
  Args:
      predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Estimated target values.
      references: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Ground truth (correct) target values.
      sample_weight: array-like of shape (n_samples,), default=None
          Sample weights.
      multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
          Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
  
                   "raw_values" : Returns a full set of errors in case of multioutput input.
  
                   "uniform_average" : Errors of all outputs are averaged with uniform weight.
  
  Returns:
      mae : mean absolute error.
          If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
          MAE output is non-negative floating point. The best value is 0.0.
  Examples:
  
      >>> mae_metric = datasets.load_metric("mae")
      >>> predictions = [2.5, 0.0, 2, 8]
      >>> references = [3, -0.5, 2, 7]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.5}
  
      If you\'re using multi-dimensional lists, then set the config as follows :
  
      >>> mae_metric = datasets.load_metric("mae", "multilist")
      >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
      >>> references = [[0, 2], [-1, 2], [8, -5]]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.75}
      >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
      >>> print(results)
      {\'mae\': array([0.5, 1. ])}
  '''
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION							)
  class _lowerCamelCase (		datasets.Metric							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				int:
  			return datasets.MetricInfo(
  			    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(self._get_feature_types()    )						,  reference_urls=[
  			        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
  			    ]						,  )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				List[str]:
  			if self.config_name == "multilist":
  				return {
  				    "predictions": datasets.Sequence(datasets.Value('float'    )    ),
  				    "references": datasets.Sequence(datasets.Value('float'    )    ),
  				}
  			else:
  				return {
  				    "predictions": datasets.Value('float'    ),
  				    "references": datasets.Value('float'    ),
  				}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]=None						,  _A	:		Tuple="uniform_average"    )						->				Any:
  			__magic_name__ :					Optional[Any]  			=  mean_absolute_error(_A						,  _A						,  sample_weight=_A						,  multioutput=_A    )
  
  			return {"mae": mae_score}