





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'''
  a_				:							Optional[Any]          =RobertaConfig
  a_				:							int          ="roberta"







  def __init__(   self   :       Optional[int]   ,					UpperCamelCase   :       List[str]				):


    '''simple docstring'''





    super().__init__(UpperCamelCase				)

    _snake_case :					Tuple								=						RobertaEmbeddings(UpperCamelCase				)
    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'''
  a_				:							Any          =RobertaConfig
  a_				:							List[Any]          ="roberta"







  def __init__(   self   :       str   ,					UpperCamelCase   :       str				):


    '''simple docstring'''





    super().__init__(UpperCamelCase				)
    _snake_case :					List[Any]								=						config.num_labels
    _snake_case :					str								=						config.num_hidden_layers

    _snake_case :					List[Any]								=						DeeRobertaModel(UpperCamelCase				)
    _snake_case :					Dict								=						nn.Dropout(config.hidden_dropout_prob				)
    _snake_case :					Optional[int]								=						nn.Linear(config.hidden_size   ,					self.config.num_labels				)







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


    '''simple docstring'''





    _snake_case :					List[Any]								=						self.num_layers
    try:
      _snake_case :					str								=						self.roberta(
          UpperCamelCase   ,					attention_mask=UpperCamelCase   ,					token_type_ids=UpperCamelCase   ,					position_ids=UpperCamelCase   ,					head_mask=UpperCamelCase   ,					inputs_embeds=UpperCamelCase   ,					)

      _snake_case :					Any								=						outputs[1]

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

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

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

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









a__   :					Tuple 						=      "Tobias Carryer"

from time import time



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      str      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str=int(time()))						->					List[Any]:  # noqa: B008




         """simple docstring"""

         lowercase__  =	multiplier
         lowercase__  =	increment
         lowercase__  =	modulo
         lowercase__  =	seed



   def       UpperCAmelCase		(    self	:      int)						->					Tuple:




         """simple docstring"""

         lowercase__  =	(self.multiplier * self.seed + self.increment) % self.modulo
         return self.seed


if __name__ == "__main__":
       # Show the LCG in action.
       a__   :					str 						=      LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
       while True:
              print(lcg.next_number())
