






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}







class       __lowercase					(snake_case__    ):
	_lowerCamelCase              =  '''data2vec-vision'''

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						List[str]=768  ,					UpperCAmelCase_   :						List[str]=12  ,					UpperCAmelCase_   :						List[Any]=12  ,					UpperCAmelCase_   :						List[str]=3_072  ,					UpperCAmelCase_   :						List[str]="gelu"  ,					UpperCAmelCase_   :						int=0.0  ,					UpperCAmelCase_   :						Optional[Any]=0.0  ,					UpperCAmelCase_   :						Tuple=0.02  ,					UpperCAmelCase_   :						List[str]=1e-12  ,					UpperCAmelCase_   :						List[str]=224  ,					UpperCAmelCase_   :						Tuple=16  ,					UpperCAmelCase_   :						Tuple=3  ,					UpperCAmelCase_   :						str=False  ,					UpperCAmelCase_   :						List[Any]=False  ,					UpperCAmelCase_   :						Tuple=False  ,					UpperCAmelCase_   :						Optional[Any]=False  ,					UpperCAmelCase_   :						Optional[Any]=0.1  ,					UpperCAmelCase_   :						str=0.1  ,					UpperCAmelCase_   :						int=True  ,					UpperCAmelCase_   :						Optional[Any]=[3, 5, 7, 11]  ,					UpperCAmelCase_   :						List[Any]=[1, 2, 3, 6]  ,					UpperCAmelCase_   :						Optional[int]=True  ,					UpperCAmelCase_   :						List[str]=0.4  ,					UpperCAmelCase_   :						str=256  ,					UpperCAmelCase_   :						Dict=1  ,					UpperCAmelCase_   :						List[Any]=False  ,					UpperCAmelCase_   :						Optional[Any]=255  ,					**UpperCAmelCase_   :						List[Any]  ,					):
			super().__init__(**lowercase_)

			UpperCamelCase__				:					int					=					hidden_size
			UpperCamelCase__				:					Dict					=					num_hidden_layers
			UpperCamelCase__				:					List[Any]					=					num_attention_heads
			UpperCamelCase__				:					int					=					intermediate_size
			UpperCamelCase__				:					str					=					hidden_act
			UpperCamelCase__				:					Tuple					=					hidden_dropout_prob
			UpperCamelCase__				:					Any					=					attention_probs_dropout_prob
			UpperCamelCase__				:					List[str]					=					initializer_range
			UpperCamelCase__				:					Any					=					layer_norm_eps

			UpperCamelCase__				:					Optional[int]					=					image_size
			UpperCamelCase__				:					str					=					patch_size
			UpperCamelCase__				:					Dict					=					num_channels
			UpperCamelCase__				:					Any					=					use_mask_token
			UpperCamelCase__				:					Dict					=					use_absolute_position_embeddings
			UpperCamelCase__				:					Union[str, Any]					=					use_relative_position_bias
			UpperCamelCase__				:					Any					=					use_shared_relative_position_bias
			UpperCamelCase__				:					Tuple					=					layer_scale_init_value
			UpperCamelCase__				:					Optional[Any]					=					drop_path_rate
			UpperCamelCase__				:					str					=					use_mean_pooling
			# decode head attributes (semantic segmentation)
			UpperCamelCase__				:					Dict					=					out_indices
			UpperCamelCase__				:					Dict					=					pool_scales
			# auxiliary head attributes (semantic segmentation)
			UpperCamelCase__				:					Any					=					use_auxiliary_head
			UpperCamelCase__				:					Union[str, Any]					=					auxiliary_loss_weight
			UpperCamelCase__				:					Any					=					auxiliary_channels
			UpperCamelCase__				:					int					=					auxiliary_num_convs
			UpperCamelCase__				:					List[str]					=					auxiliary_concat_input
			UpperCamelCase__				:					Any					=					semantic_loss_ignore_index







class       __lowercase					(snake_case__    ):
	_lowerCamelCase              =  version.parse('''1.11'''    )

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ])





	@property
	def      __UpperCamelCase							(      self   :						int):
			return 1e-4












'''simple docstring'''




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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					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')
					UpperCamelCase__				:					Union[str, Any]					=					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:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					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

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

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

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

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





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




