



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        snake_case__    (UpperCamelCase__		):







						"""simple docstring"""




						__lowerCAmelCase					:int      =					RobertaConfig
						__lowerCAmelCase					:Optional[Any]      =					"""roberta"""

						def __init__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									super().__init__(__a  )

									a__       :						Optional[int]							=  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        snake_case__    (UpperCamelCase__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					RobertaConfig
						__lowerCAmelCase					:Optional[int]      =					"""roberta"""

						def __init__(       self ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									super().__init__(__a  )
									a__       :						Tuple							=  config.num_labels
									a__       :						str							=  config.num_hidden_layers

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

						@add_start_docstrings_to_model_forward(__a  )
						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=-1 ,       __lowercase=False ,       ) ->				Tuple:
									"""simple docstring"""




									a__       :						Dict							=  self.num_layers
									try:
												a__       :						Dict							=  self.roberta(
												    __a ,       attention_mask=__a ,       token_type_ids=__a ,       position_ids=__a ,       head_mask=__a ,       inputs_embeds=__a ,       )

												a__       :						str							=  outputs[1]

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

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

												# work with highway exits
												a__       :						Any							=  []
												for highway_exit in outputs[-1]:
															a__       :						str							=  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
																		a__       :						Optional[Any]							=  MSELoss()
																		a__       :						Union[str, Any]							=  loss_fct(highway_logits.view(-1  ) ,       labels.view(-1  )  )
															else:
																		a__       :						List[str]							=  CrossEntropyLoss()
																		a__       :						str							=  loss_fct(highway_logits.view(-1 ,       self.num_labels  ) ,       labels.view(-1  )  )
															highway_losses.append(__a  )

												if train_highway:
															a__       :						str							=  (sum(highway_losses[:-1]  ),) + outputs
															# exclude the final highway, of course
												else:
															a__       :						List[Any]							=  (loss,) + outputs
									if not self.training:
												a__       :						Tuple							=  outputs + ((original_entropy, highway_entropy), exit_layer)
												if output_layer >= 0:
															a__       :						Optional[int]							=  (
															    (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

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       float						,       _lowercase    :       float						,       )							->	tuple[str, float]:







			"""simple docstring"""
			if (stress, tangential_force, area).count(0) != 1:
						raise ValueError("""You cannot supply more or less than 2 values""")
			elif stress < 0:
						raise ValueError("""Stress cannot be negative""")
			elif tangential_force < 0:
						raise ValueError("""Tangential Force cannot be negative""")
			elif area < 0:
						raise ValueError("""Area cannot be negative""")
			elif stress == 0:
						return (
						    "stress",
						    tangential_force / area,
						)
			elif tangential_force == 0:
						return (
						    "tangential_force",
						    stress * area,
						)
			else:
						return (
						    "area",
						    tangential_force / stress,
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




