



from __future__ import annotations

from collections.abc import MutableSequence



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														if len(__a) != degree + 1:
																					raise ValueError(
																					    "The number of coefficients should be equal to the degree + 1.")

														_lowerCAmelCase     :							list[float]    =    list(__a)
														_lowerCAmelCase     :							Any    =    degree







							def __add__(   self,			__a):

														'''simple docstring'''

														if self.degree > polynomial_a.degree:
																					_lowerCAmelCase     :							Optional[Any]    =    self.coefficients[:]
																					for i in range(polynomial_a.degree + 1):
																												coefficients[i] += polynomial_a.coefficients[i]
																					return Polynomial(self.degree,			__a)
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    polynomial_a.coefficients[:]
																					for i in range(self.degree + 1):
																												coefficients[i] += self.coefficients[i]
																					return Polynomial(polynomial_a.degree,			__a)







							def __sub__(   self,			__a):

														'''simple docstring'''

														return self + polynomial_a * Polynomial(0,			[-1])







							def __neg__(   self):

														'''simple docstring'''

														return Polynomial(self.degree,			[-c for c in self.coefficients])







							def __mul__(   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							list[float]    =    [0] * (self.degree + polynomial_a.degree + 1)
														for i in range(self.degree + 1):
																					for j in range(polynomial_a.degree + 1):
																												coefficients[i + j] += (
																												    self.coefficients[i] * polynomial_a.coefficients[j]
																												)

														return Polynomial(self.degree + polynomial_a.degree,			__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int | float    =    0
														for i in range(self.degree + 1):
																					result += self.coefficients[i] * (substitution**i)
														return result







							def __str__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    ""
														for i in range(self.degree,			-1,			-1):
																					if self.coefficients[i] == 0:
																												continue
																					elif self.coefficients[i] > 0:
																												if polynomial:
																																			polynomial += " + "
																					else:
																												polynomial += " - "

																					if i == 0:
																												polynomial += str(abs(self.coefficients[i]))
																					elif i == 1:
																												polynomial += str(abs(self.coefficients[i])) + "x"
																					else:
																												polynomial += str(abs(self.coefficients[i])) + "x^" + str(__a)

														return polynomial







							def __repr__(   self):

														'''simple docstring'''

														return self.__str__()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							list[float]    =    [0] * self.degree
														for i in range(self.degree):
																					_lowerCAmelCase     :							Optional[Any]    =    self.coefficients[i + 1] * (i + 1)
														return Polynomial(self.degree - 1,			__a)







							def    snake_case__      (   self,			__a = 0):

														'''simple docstring'''

														_lowerCAmelCase     :							list[float]    =    [0] * (self.degree + 2)
														_lowerCAmelCase     :							Dict    =    constant
														for i in range(self.degree + 1):
																					_lowerCAmelCase     :							Tuple    =    self.coefficients[i] / (i + 1)
														return Polynomial(self.degree + 1,			__a)







							def __eq__(   self,			__a):

														'''simple docstring'''

														if not isinstance(__a,			__a):
																					return False

														if self.degree != polynomial_a.degree:
																					return False

														for i in range(self.degree + 1):
																					if self.coefficients[i] != polynomial_a.coefficients[i]:
																												return False

														return True







							def __ne__(   self,			__a):

														'''simple docstring'''

														return not self.__eq__(__a)





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
