






"""simple docstring"""






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). "       ,	UpperCAmelCase__       ,	)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						RobertaConfig
      __UpperCamelCase							=						"roberta"
      def __init__(		self  :						int		,			lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Dict							=	RobertaEmbeddings(lowercase_)
          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. "       ,	UpperCAmelCase__       ,	)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						RobertaConfig
      __UpperCamelCase							=						"roberta"
      def __init__(		self  :						Union[str, Any]		,			lowercase_  :						List[str]):




          '''simple docstring'''


          super().__init__(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	config.num_labels
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	config.num_hidden_layers

          SCREAMING_SNAKE_CASE_   :						Dict							=	DeeRobertaModel(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	nn.Dropout(config.hidden_dropout_prob)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	nn.Linear(config.hidden_size		,			self.config.num_labels)



      @add_start_docstrings_to_model_forward(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Optional[Any]=None		,			lowercase_  :						int=None		,			lowercase_  :						Optional[Any]=None		,			lowercase_  :						Union[str, Any]=None		,			lowercase_  :						Tuple=None		,			lowercase_  :						Dict=None		,			lowercase_  :						Tuple=None		,			lowercase_  :						List[str]=-1		,			lowercase_  :						Optional[Any]=False		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.num_layers
          try:
              SCREAMING_SNAKE_CASE_   :						int							=	self.roberta(
                  lowercase_		,			attention_mask=lowercase_		,			token_type_ids=lowercase_		,			position_ids=lowercase_		,			head_mask=lowercase_		,			inputs_embeds=lowercase_		,			)

              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	outputs[1]

              SCREAMING_SNAKE_CASE_   :						int							=	self.dropout(lowercase_)
              SCREAMING_SNAKE_CASE_   :						List[str]							=	self.classifier(lowercase_)
              SCREAMING_SNAKE_CASE_   :						str							=	(logits,) + outputs[2:]  # add hidden states and attention if they are here
          except HighwayException as e:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	e.message
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	e.exit_layer
              SCREAMING_SNAKE_CASE_   :						List[str]							=	outputs[0]

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

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

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

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










"""simple docstring"""






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray



def       _A    (__a					,       __a					,       __a = False							)				-> list[float]:
    """simple docstring"""






    if radian_mode:
        return [magnitude * cos(__a							), magnitude * sin(__a							)]
    return [magnitude * cos(radians(__a							)							), magnitude * sin(radians(__a							)							)]



def       _A    (__a					,       __a					,       __a = 10**-1							)				-> bool:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						NDArray[floataa]							=	cross(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						float							=	sum(__a							)
    return abs(__a							) < eps


if __name__ == "__main__":
  # Test to check if it works
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(7_1_8.4, 180 - 30),
          polar_force(8_7_9.5_4, 45),
          polar_force(100, -90),
      ]
  )

  UpperCAmelCase_     :				NDArray[floataa]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  UpperCAmelCase_     :				Union[str, Any]         =				array(
      [
          polar_force(30 * 9.8_1, 15),
          polar_force(215, 180 - 45),
          polar_force(264, 90 - 30),
      ]
  )

  UpperCAmelCase_     :				Optional[int]         =				array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  UpperCAmelCase_     :				List[str]         =				array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

  UpperCAmelCase_     :				Any         =				array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()


