



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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							return np.dot(_lowerCamelCase				, _lowerCamelCase  )



class   UpperCAmelCase_    :







							def __init__(   self,			*,
							    __a = np.inf,			__a = "linear",			__a = 0.0,			):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    regularization
														_lowerCAmelCase     :							Union[str, Any]    =    gamma
														if kernel == "linear":
																					_lowerCAmelCase     :							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")
																					_lowerCAmelCase     :							Optional[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:
																					_lowerCAmelCase     :							Union[str, Any]    =    f"Unknown kernel: {kernel}"
																					raise ValueError(__a)







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														return np.dot(__a,			__a)







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    observations
														_lowerCAmelCase     :							Optional[int]    =    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(__a)

														def to_minimize(__a) -> float:
																					_lowerCAmelCase     :							Tuple    =    0
																					((_lowerCAmelCase)   ,    )     :							Tuple    =    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)

														_lowerCAmelCase     :							str    =    LinearConstraint(__a,			0,			0)
														_lowerCAmelCase     :							int    =    Bounds(0,			self.regularization)

														_lowerCAmelCase     :							int    =    minimize(
														    __a,			np.ones(__a),			bounds=__a,			constraints=[ly_contraint]).x
														_lowerCAmelCase     :							Any    =    l_star

														# calculating mean offset of separation plane to points
														_lowerCAmelCase     :							int    =    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])
														_lowerCAmelCase     :							Any    =    s / n







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    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()





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

														if not self.training:
																					_lowerCAmelCase     :							int    =    entropy(__a)
																					_lowerCAmelCase     :							List[Any]    =    []
																					_lowerCAmelCase     :							str    =    []
														if labels is not None:
																					if self.num_labels == 1:
																												#  We are doing regression
																												_lowerCAmelCase     :							Optional[Any]    =    MSELoss()
																												_lowerCAmelCase     :							int    =    loss_fct(logits.view(-1),			labels.view(-1))
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    CrossEntropyLoss()
																												_lowerCAmelCase     :							Optional[Any]    =    loss_fct(logits.view(-1,			self.num_labels),			labels.view(-1))

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    highway_exit[0]
																												if not self.training:
																																			highway_logits_all.append(__a)
																																			highway_entropy.append(highway_exit[2])
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																												)  # use the highway of the last layer

														return outputs  # (loss), logits, (hidden_states), (attentions), entropy
