






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''openai-gpt'''
	__magic_name__			   =       {
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}





	def __init__(   self      :				Any			,			lowerCAmelCase_      :				Dict=40_478			,			lowerCAmelCase_      :				List[Any]=512			,			lowerCAmelCase_      :				List[str]=768			,			lowerCAmelCase_      :				Optional[Any]=12			,			lowerCAmelCase_      :				int=12			,			lowerCAmelCase_      :				Union[str, Any]="gelu"			,			lowerCAmelCase_      :				Tuple=0.1			,			lowerCAmelCase_      :				Union[str, Any]=0.1			,			lowerCAmelCase_      :				List[Any]=0.1			,			lowerCAmelCase_      :				str=1e-5			,			lowerCAmelCase_      :				Union[str, Any]=0.0_2			,			lowerCAmelCase_      :				Dict="cls_index"			,			lowerCAmelCase_      :				List[Any]=True			,			lowerCAmelCase_      :				Dict=None			,			lowerCAmelCase_      :				int=True			,			lowerCAmelCase_      :				Any=0.1			,			**lowerCAmelCase_      :				int			,			)		->    int:
				UpperCAmelCase_  :				Tuple      =      vocab_size
				UpperCAmelCase_  :				Tuple      =      n_positions
				UpperCAmelCase_  :				Any      =      n_embd
				UpperCAmelCase_  :				Optional[Any]      =      n_layer
				UpperCAmelCase_  :				List[str]      =      n_head
				UpperCAmelCase_  :				Optional[int]      =      afn
				UpperCAmelCase_  :				List[Any]      =      resid_pdrop
				UpperCAmelCase_  :				Dict      =      embd_pdrop
				UpperCAmelCase_  :				Dict      =      attn_pdrop
				UpperCAmelCase_  :				Union[str, Any]      =      layer_norm_epsilon
				UpperCAmelCase_  :				str      =      initializer_range
				UpperCAmelCase_  :				Optional[int]      =      summary_type
				UpperCAmelCase_  :				Any      =      summary_use_proj
				UpperCAmelCase_  :				int      =      summary_activation
				UpperCAmelCase_  :				Dict      =      summary_first_dropout
				UpperCAmelCase_  :				List[str]      =      summary_proj_to_labels
				super().__init__(**lowerCAmelCase_		)











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

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      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_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				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

				((UpperCAmelCase_)  ,			)  :				Optional[int]      =      np.shape(lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

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

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





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


if __name__ == "__main__":
			import doctest

			doctest.testmod()



