"""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). '	,							lowerCAmelCase_	,							)
class        a				(		lowerCAmelCase_   ):
    _snake_case     :  str     							=							RobertaConfig
    _snake_case     :  List[Any]     							=							'roberta'


    def __init__(					self	:  str   ,				__lowerCAmelCase	:  List[str]					):
      super().__init__(__lowerCAmelCase					)

      _UpperCAmelCase						 =      RobertaEmbeddings(__lowerCAmelCase					)
      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. '	,							lowerCAmelCase_	,							)
class        a				(		lowerCAmelCase_   ):
    _snake_case     :  List[str]     							=							RobertaConfig
    _snake_case     :  List[str]     							=							'roberta'


    def __init__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      super().__init__(__lowerCAmelCase					)
      _UpperCAmelCase						 =      config.num_labels
      _UpperCAmelCase						 =      config.num_hidden_layers

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


    @add_start_docstrings_to_model_forward(__lowerCAmelCase					)
    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  List[str]=None   ,				__lowerCAmelCase	:  List[str]=None   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  int=-1   ,				__lowerCAmelCase	:  Optional[int]=False   ,				):
      _UpperCAmelCase						 =      self.num_layers
      try:
        _UpperCAmelCase						 =      self.roberta(
            __lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				position_ids=__lowerCAmelCase   ,				head_mask=__lowerCAmelCase   ,				inputs_embeds=__lowerCAmelCase   ,				)

        _UpperCAmelCase						 =      outputs[1]

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

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

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

        if train_highway:
          _UpperCAmelCase						 =      (sum(highway_losses[:-1]					),) + outputs
          # exclude the final highway, of course
        else:
          _UpperCAmelCase						 =      (loss,) + outputs
      if not self.training:
        _UpperCAmelCase						 =      outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
          _UpperCAmelCase						 =      (
              (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 random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
