






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 							_UpperCamelCase			(UpperCamelCase__			):
      snake_case_  =							RobertaConfig
      snake_case_  =							"""roberta"""


      def __init__(	self					,						__UpperCamelCase	)->	int:
           super().__init__(_a	)

           __lowerCAmelCase								=					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. """							,		UpperCamelCase__							,		)
class 							_UpperCamelCase			(UpperCamelCase__			):
      snake_case_  =							RobertaConfig
      snake_case_  =							"""roberta"""


      def __init__(	self					,						__UpperCamelCase	)->	Any:
           super().__init__(_a	)
           __lowerCAmelCase								=					config.num_labels
           __lowerCAmelCase								=					config.num_hidden_layers

           __lowerCAmelCase								=					DeeRobertaModel(_a	)
           __lowerCAmelCase								=					nn.Dropout(config.hidden_dropout_prob	)
           __lowerCAmelCase								=					nn.Linear(config.hidden_size					,						self.config.num_labels	)


      @add_start_docstrings_to_model_forward(_a	)
      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=-1					,						__UpperCamelCase=False					,						)->	List[str]:
           __lowerCAmelCase								=					self.num_layers
           try:
                __lowerCAmelCase								=					self.roberta(
                    _a					,						attention_mask=_a					,						token_type_ids=_a					,						position_ids=_a					,						head_mask=_a					,						inputs_embeds=_a					,						)

                __lowerCAmelCase								=					outputs[1]

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

           if not self.training:
                __lowerCAmelCase								=					entropy(_a	)
                __lowerCAmelCase								=					[]
                __lowerCAmelCase								=					[]
           if labels is not None:
                if self.num_labels == 1:
                     #  We are doing regression
                     __lowerCAmelCase								=					MSELoss()
                     __lowerCAmelCase								=					loss_fct(logits.view(-1	)					,						labels.view(-1	)	)
                else:
                     __lowerCAmelCase								=					CrossEntropyLoss()
                     __lowerCAmelCase								=					loss_fct(logits.view(-1					,						self.num_labels	)					,						labels.view(-1	)	)

                # work with highway exits
                __lowerCAmelCase								=					[]
                for highway_exit in outputs[-1]:
                     __lowerCAmelCase								=					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								=					MSELoss()
                          __lowerCAmelCase								=					loss_fct(highway_logits.view(-1	)					,						labels.view(-1	)	)
                     else:
                          __lowerCAmelCase								=					CrossEntropyLoss()
                          __lowerCAmelCase								=					loss_fct(highway_logits.view(-1					,						self.num_labels	)					,						labels.view(-1	)	)
                     highway_losses.append(_a	)

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

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












from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase			:					int         =	1.0_5457_1817E-34  # unit of ℏ : J * s

lowerCamelCase			:					Union[str, Any]         =	3E8  # unit of c : m * s^-1





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

     if (force, area, distance).count(0       ) != 1:
          raise ValueError("One and only one argument must be 0"       )
     if force < 0:
          raise ValueError("Magnitude of force can not be negative"       )
     if distance < 0:
          raise ValueError("Distance can not be negative"       )
     if area < 0:
          raise ValueError("Area can not be negative"       )
     if force == 0:
          __lowerCAmelCase								=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
              240 * (distance) ** 4
          )
          return {"force": force}
     elif area == 0:
          __lowerCAmelCase								=					(240 * force * (distance) ** 4) / (
              REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
          )
          return {"area": area}
     elif distance == 0:
          __lowerCAmelCase								=					(
              (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
          ) ** (1 / 4)
          return {"distance": distance}
     raise ValueError("One and only one argument must be 0"       )


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()






