
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). "   ,__lowercase   ,)
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				RobertaConfig
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				"roberta"
   def __init__(self			: str ,    snake_case_			: Optional[int]       ):
    super().__init__(snake_case_       )

    __a			:  Any		    =  RobertaEmbeddings(snake_case_       )
    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. "   ,__lowercase   ,)
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				RobertaConfig
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"roberta"
   def __init__(self			: Dict ,    snake_case_			: List[Any]       ):
    super().__init__(snake_case_       )
    __a			:  Optional[int]		    =  config.num_labels
    __a			:  Tuple		    =  config.num_hidden_layers

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







   @add_start_docstrings_to_model_forward(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[str]=None ,    snake_case_			: Optional[int]=None ,    snake_case_			: int=None ,    snake_case_			: Any=None ,    snake_case_			: Optional[int]=None ,    snake_case_			: int=None ,    snake_case_			: List[Any]=None ,    snake_case_			: Dict=-1 ,    snake_case_			: Any=False ,    ):
    __a			:  int		    =  self.num_layers
    try:
     __a			:  Optional[int]		    =  self.roberta(
         snake_case_ ,    attention_mask=snake_case_ ,    token_type_ids=snake_case_ ,    position_ids=snake_case_ ,    head_mask=snake_case_ ,    inputs_embeds=snake_case_ ,    )

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

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

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

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

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


def        __UpperCamelCase					(    lowerCAmelCase__							:    list[list[int | float]]  ):
 __a			:  int		    =  len(lowerCAmelCase__  )
 __a			:  Dict		    =  len(matrix[0]  )
 __a			:  Union[str, Any]		    =  min(lowerCAmelCase__  ,       lowerCAmelCase__  )

 for row in range(lowerCAmelCase__  ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  matrix[col][row] / matrix[row][row]
    for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __a			:  Optional[int]		    =  True
   for i in range(row + 1  ,       lowerCAmelCase__  ):
    if matrix[i][row] != 0:
     __a    ,	__a			:  Any		    =  matrix[i], matrix[row]
     __a			:  Union[str, Any]		    =  False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCAmelCase__  ):
     __a			:  Optional[Any]		    =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()
