


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      snake_case_       :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       nn.Module
			SCREAMING_SNAKE_CASE					:       List[nn.Module]						      =    field(default_factory=UpperCamelCase_ )
			SCREAMING_SNAKE_CASE					:       list						      =    field(default_factory=UpperCamelCase_ )







			def     snake_case__(					self			:    Tuple							,       _UpperCamelCase			:    List[Any]							,       _UpperCamelCase			:    Tensor							,       _UpperCamelCase			:    Tensor							)				->Union[str, Any]:
									snake_case_       =     len(list(m.modules()							)							) == 1 or isinstance(lowercase_							,       nn.Convad							) or isinstance(lowercase_							,       nn.BatchNormad							)
									if has_not_submodules:
															if not isinstance(lowercase_							,       lowercase_							):
																					self.traced.append(lowercase_							)







			def __call__(					self			:    Any							,       _UpperCamelCase			:    Tensor							)				->Tuple:




									for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook							)							)
									self.module(lowercase_							)
									[x.remove() for x in self.handles]
									return self







			@property
			def     snake_case__(					self			:    Any							)				->Tuple:
									return list(filter(lambda _UpperCamelCase							: len(list(x.state_dict().keys()							)							) > 0							,       self.traced							)							)






@dataclass
class      snake_case_       :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       nn.Module
			SCREAMING_SNAKE_CASE					:       nn.Module
			SCREAMING_SNAKE_CASE					:       int						      =    0
			SCREAMING_SNAKE_CASE					:       List						      =    field(default_factory=UpperCamelCase_ )
			SCREAMING_SNAKE_CASE					:       List						      =    field(default_factory=UpperCamelCase_ )







			def __call__(					self			:    Optional[int]							,       _UpperCamelCase			:    Tensor							)				->Optional[int]:
									snake_case_       =     Tracker(self.dest							)(lowercase_							).parametrized
									snake_case_       =     Tracker(self.src							)(lowercase_							).parametrized

									snake_case_       =     list(filter(lambda _UpperCamelCase							: type(lowercase_							) not in self.src_skip							,       lowercase_							)							)
									snake_case_       =     list(filter(lambda _UpperCamelCase							: type(lowercase_							) not in self.dest_skip							,       lowercase_							)							)

									if len(lowercase_							) != len(lowercase_							):
															raise Exception(
															    f'''Numbers of operations are different. Source module has {len(lowercase_							)} operations while'''
															    f''' destination module has {len(lowercase_							)}.'''							)

									for dest_m, src_m in zip(lowercase_							,       lowercase_							):
															dest_m.load_state_dict(src_m.state_dict()							)
															if self.verbose == 1:
																					print(f'''Transfered from={src_m} to={dest_m}'''							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     from_model.state_dict()
						snake_case_       =     our_model.state_dict()
						snake_case_       =     our_model.config
						snake_case_       =     []
						for stage_idx in range(len(config.hidden_sizes	)	):
												for block_id in range(config.depths[stage_idx]	):
																		snake_case_       =     F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																		snake_case_       =     F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																		all_keys.append((from_key, to_key)	)
																		snake_case_       =     F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		snake_case_       =     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:
												snake_case_       =     from_state_dict.pop(SCREAMING_SNAKE_CASE__	)

						our_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						return our_model


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = True  ,		):
						print(F'''Downloading weights for {name}...'''	)
						snake_case_       =     cached_download(SCREAMING_SNAKE_CASE__	)
						print(F'''Converting {name}...'''	)
						snake_case_       =     torch.load(SCREAMING_SNAKE_CASE__	)['state_dict']
						from_model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						from_model.eval()
						with torch.no_grad():
												snake_case_       =     VanForImageClassification(SCREAMING_SNAKE_CASE__	).eval()
												snake_case_       =     ModuleTransfer(src=SCREAMING_SNAKE_CASE__  ,		dest=SCREAMING_SNAKE_CASE__	)
												snake_case_       =     torch.randn((1, 3, 224, 224)	)
												module_transfer(SCREAMING_SNAKE_CASE__	)
												snake_case_       =     copy_parameters(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)

						if not torch.allclose(from_model(SCREAMING_SNAKE_CASE__	)  ,		our_model(SCREAMING_SNAKE_CASE__	).logits	):
												raise ValueError('''The model logits don\'t match the original one.'''	)

						snake_case_       =     name
						print(SCREAMING_SNAKE_CASE__	)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name  ,		commit_message='''Add model'''  ,		use_temp_dir=SCREAMING_SNAKE_CASE__  ,		)

												# we can use the convnext one
												snake_case_       =     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=SCREAMING_SNAKE_CASE__  ,		)

												print(F'''Pushed {checkpoint_name}'''	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = True	):
						snake_case_       =     'imagenet-1k-id2label.json'
						snake_case_       =     1000

						snake_case_       =     'huggingface/label-files'
						snake_case_       =     num_labels
						snake_case_       =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		repo_type='''dataset'''	)  ,		'''r'''	)	)
						snake_case_       =     {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}

						snake_case_       =     idalabel
						snake_case_       =     {v: k for k, v in idalabel.items()}

						snake_case_       =     partial(SCREAMING_SNAKE_CASE__  ,		num_labels=SCREAMING_SNAKE_CASE__  ,		idalabel=SCREAMING_SNAKE_CASE__  ,		labelaid=SCREAMING_SNAKE_CASE__	)

						snake_case_       =     {
						    '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]  ,		),
						}

						snake_case_       =     {
						    'van-tiny': van_tiny,
						    'van-small': van_small,
						    'van-base': van_base,
						    'van-large': van_large,
						}

						snake_case_       =     {
						    '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(
												    SCREAMING_SNAKE_CASE__  ,		names_to_config[model_name]  ,		checkpoint=names_to_original_checkpoints[model_name]  ,		from_model=names_to_original_models[model_name]()  ,		save_directory=SCREAMING_SNAKE_CASE__  ,		push_to_hub=SCREAMING_SNAKE_CASE__  ,		)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		checkpoint=names_to_original_checkpoints[model_name]  ,		from_model=names_to_original_models[model_name]()  ,		save_directory=SCREAMING_SNAKE_CASE__  ,		push_to_hub=SCREAMING_SNAKE_CASE__  ,		)


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)


from sklearn.metrics import roc_auc_score

import datasets


lowerCAmelCase_	     =     '''
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
'''

lowerCAmelCase_	     =     '''
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:
    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.
    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric("roc_auc")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results[\'roc_auc\'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')
        >>> print(round(results[\'roc_auc\'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results[\'roc_auc\']])
        [0.83, 0.38, 0.94]
'''

lowerCAmelCase_	     =     '''\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@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}
}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,	_KWARGS_DESCRIPTION )
class      snake_case_       (		datasets.Metric ):







			'''simple docstring'''

			def     snake_case__(					self			:    List[str]							)				->Tuple:
									return datasets.MetricInfo(
									    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
									        {
									            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''							)							),
									            '''references''': datasets.Value('''int32'''							),
									        }
									        if self.config_name == '''multiclass'''
									        else {
									            '''references''': datasets.Sequence(datasets.Value('''int32'''							)							),
									            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''							)							),
									        }
									        if self.config_name == '''multilabel'''
									        else {
									            '''references''': datasets.Value('''int32'''							),
									            '''prediction_scores''': datasets.Value('''float'''							),
									        }							)							,       reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html''']							,       )







			def     snake_case__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    int="macro"							,       _UpperCamelCase			:    Any=None							,       _UpperCamelCase			:    str=None							,       _UpperCamelCase			:    List[str]="raise"							,       _UpperCamelCase			:    Union[str, Any]=None							,       )				->int:
									return {
									    "roc_auc": roc_auc_score(
									        _UpperCamelCase							,       _UpperCamelCase							,       average=_UpperCamelCase							,       sample_weight=_UpperCamelCase							,       max_fpr=_UpperCamelCase							,       multi_class=_UpperCamelCase							,       labels=_UpperCamelCase							,       )
									}