





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				ndarray     ):
		return np.dot(UpperCamelCase__       , UpperCamelCase__     )




class 	UpperCamelCase_							:


							def __init__(				self     :int				,					*,
							    __A     :float = np.inf				,					__A     :str = "linear"				,					__A     :float = 0.0				,					) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   regularization
									SCREAMING_SNAKE_CASE__								=   gamma
									if kernel == "linear":
											SCREAMING_SNAKE_CASE__								=   self.__linear
									elif kernel == "rbf":
											if self.gamma == 0:
													raise ValueError("""rbf kernel requires gamma"""		)
											if not isinstance(self.gamma				,					(float, int)		):
													raise ValueError("""gamma must be float or int"""		)
											if not self.gamma > 0:
													raise ValueError("""gamma must be > 0"""		)
											SCREAMING_SNAKE_CASE__								=   self.__rbf
											# in the future, there could be a default value like in sklearn
											# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
											# previously it was 1/(n_features)
									else:
											SCREAMING_SNAKE_CASE__								=   f'''Unknown kernel: {kernel}'''
											raise ValueError(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



									return np.dot(__A				,					__A		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



									return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :list[ndarray]				,					__A     :ndarray		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   observations
									SCREAMING_SNAKE_CASE__								=   classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((SCREAMING_SNAKE_CASE__)						,    )								=   np.shape(__A		)

									def to_minimize(__A     :ndarray		) -> float:
											SCREAMING_SNAKE_CASE__								=   0
											((SCREAMING_SNAKE_CASE__)						,    )								=   np.shape(__A		)
											for i in range(__A		):
													for j in range(__A		):
															s += (
															    candidate[i]
															    * candidate[j]
															    * classes[i]
															    * classes[j]
															    * self.kernel(observations[i]				,					observations[j]		)
															)
											return 1 / 2 * s - sum(__A		)

									SCREAMING_SNAKE_CASE__								=   LinearConstraint(__A				,					0				,					0		)
									SCREAMING_SNAKE_CASE__								=   Bounds(0				,					self.regularization		)

									SCREAMING_SNAKE_CASE__								=   minimize(
									    __A				,					np.ones(__A		)				,					bounds=__A				,					constraints=[ly_contraint]		).x
									SCREAMING_SNAKE_CASE__								=   l_star

									# calculating mean offset of separation plane to points
									SCREAMING_SNAKE_CASE__								=   0
									for i in range(__A		):
											for j in range(__A		):
													s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
													    observations[i]				,					observations[j]		)
									SCREAMING_SNAKE_CASE__								=   s / n


							def   _snake_case	(				self     :Optional[int]				,					__A     :ndarray		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]				,					__A		)
									    for n in range(len(self.classes		)		)		)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()






'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

		from_model.eval()
		snake_case__	       =   LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
		snake_case__	       =   OrderedDict()

		snake_case__	       =   from_model.state_dict()
		snake_case__	       =   list(from_model.state_dict().keys()							)
		snake_case__	       =   list(our_model.state_dict().keys()							)
		print(len(UpperCAmelCase_							)					,				len(UpperCAmelCase_							)							)
		for i in range(len(UpperCAmelCase_							)							):
			snake_case__	       =   weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_							)

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

	assert torch.allclose(UpperCAmelCase_					,				UpperCAmelCase_							), "The model logits don't match the original one."

	snake_case__	       =   name
	print(UpperCAmelCase_							)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name							)
		snake_case__	       =   LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name							)

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

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

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

	snake_case__	       =   partial(UpperCAmelCase_					,				num_labels=UpperCAmelCase_					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_							)

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				num_attention_heads=[6, 9, 12]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0.1					,				),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				names_to_config[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	return config, expected_shape


if __name__ == "__main__":
			a__					:			Optional[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 Levit* architecture,''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''levit-dump-folder/''',
			    type=Path,
			    required=False,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
			parser.add_argument(
			    '''--no-push_to_hub''',
			    dest='''push_to_hub''',
			    action='''store_false''',
			    help='''Do not push model and image processor to the hub''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			Path          =     args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



