


'''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)



'''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}