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





def  _lowerCamelCase					(   a_				: ndarray):
			return np.dot(a_						,			a_)
class   _lowerCAmelCase		:







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







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    ndarray	,					__snake_case     :    ndarray		):
										return np.dot(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    ndarray	,					__snake_case     :    ndarray		):
										return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)







							def 				snake_case  (				self     :    List[str]	,					__snake_case     :    list[ndarray]	,					__snake_case     :    ndarray		):
										lowerCamelCase						:str						  =     observations
										lowerCamelCase						:Optional[Any]						  =     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

										((lowerCamelCase)  ,    )						:str						  =     np.shape(__snake_case		)

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

										lowerCamelCase						:Tuple						  =     LinearConstraint(__snake_case	,					0	,					0		)
										lowerCamelCase						:Dict						  =     Bounds(0	,					self.regularization		)

										lowerCamelCase						:Union[str, Any]						  =     minimize(
										    __snake_case	,					np.ones(__snake_case		)	,					bounds=__snake_case	,					constraints=[ly_contraint]		).x
										lowerCamelCase						:List[str]						  =     l_star

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







							def 				snake_case  (				self     :    str	,					__snake_case     :    ndarray		):
										lowerCamelCase						:List[Any]						  =     sum(
										    self.optimum[n]
										    * self.classes[n]
										    * self.kernel(self.observations[n]	,					__snake_case		)
										    for n in range(len(self.classes		)		)		)
										return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



