
"""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). """				,lowercase__				,)
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		RobertaConfig
  SCREAMING_SNAKE_CASE__			:						int       =		"""roberta"""







  def __init__(		self				,   lowercase_  ):

     """simple docstring"""
     super().__init__(lowercase_  )

     UpperCAmelCase_					:					int			     =    RobertaEmbeddings(lowercase_  )
     self.init_weights()




@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """				,lowercase__				,)
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		RobertaConfig
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		"""roberta"""







  def __init__(		self				,   lowercase_  ):

     """simple docstring"""
     super().__init__(lowercase_  )
     UpperCAmelCase_					:					Any			     =    config.num_labels
     UpperCAmelCase_					:					Optional[Any]			     =    config.num_hidden_layers

     UpperCAmelCase_					:					int			     =    DeeRobertaModel(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    nn.Dropout(config.hidden_dropout_prob  )
     UpperCAmelCase_					:					Dict			     =    nn.Linear(config.hidden_size				,   self.config.num_labels  )







  @add_start_docstrings_to_model_forward(lowercase_  )
  def        UpperCamelCase__					(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=-1				,   lowercase_=False				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.num_layers
     try:
        UpperCAmelCase_					:					str			     =    self.roberta(
            lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   position_ids=lowercase_				,   head_mask=lowercase_				,   inputs_embeds=lowercase_				,   )

        UpperCAmelCase_					:					List[str]			     =    outputs[1]

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

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

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

        if train_highway:
           UpperCAmelCase_					:					Tuple			     =    (sum(highway_losses[:-1]  ),) + outputs
           # exclude the final highway, of course
        else:
           UpperCAmelCase_					:					Any			     =    (loss,) + outputs
     if not self.training:
        UpperCAmelCase_					:					Any			     =    outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
           UpperCAmelCase_					:					List[str]			     =    (
               (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 math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					int			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					List[str]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					int			     =    (1 - _cos) / 2
   UpperCAmelCase_					:					Optional[Any]			     =    1 - _cos

   UpperCAmelCase_					:					int			     =    1 + alpha
   UpperCAmelCase_					:					Dict			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha

   UpperCAmelCase_					:					Dict			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Dict			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Tuple			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Any			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					List[Any]			     =    (1 + _cos) / 2
   UpperCAmelCase_					:					Optional[int]			     =    -1 - _cos

   UpperCAmelCase_					:					Union[str, Any]			     =    1 + alpha
   UpperCAmelCase_					:					Optional[int]			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha

   UpperCAmelCase_					:					List[str]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					str			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					List[Any]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					Any			     =    _sin / 2
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					Tuple			     =    -ba

   UpperCAmelCase_					:					Optional[Any]			     =    1 + alpha
   UpperCAmelCase_					:					Dict			     =    -2 * _cos
   UpperCAmelCase_					:					Optional[int]			     =    1 - alpha

   UpperCAmelCase_					:					List[str]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Any			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Any			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[int]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					List[str]			     =    1 - alpha
   UpperCAmelCase_					:					str			     =    -2 * _cos
   UpperCAmelCase_					:					Any			     =    1 + alpha

   UpperCAmelCase_					:					Tuple			     =    IIRFilter(2  )
   filt.set_coefficients([ba, ba, ba],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					Dict			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Union[str, Any]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[int]			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					List[str]			     =    10 ** (gain_db / 40)

   UpperCAmelCase_					:					List[Any]			     =    1 + alpha * big_a
   UpperCAmelCase_					:					Tuple			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha * big_a
   UpperCAmelCase_					:					str			     =    1 + alpha / big_a
   UpperCAmelCase_					:					List[str]			     =    -2 * _cos
   UpperCAmelCase_					:					List[str]			     =    1 - alpha / big_a

   UpperCAmelCase_					:					Tuple			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					str			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					int			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					List[Any]			     =    10 ** (gain_db / 40)
   UpperCAmelCase_					:					Tuple			     =    (big_a + 1) - (big_a - 1) * _cos
   UpperCAmelCase_					:					int			     =    (big_a + 1) + (big_a - 1) * _cos
   UpperCAmelCase_					:					Optional[Any]			     =    (big_a - 1) - (big_a + 1) * _cos
   UpperCAmelCase_					:					Optional[int]			     =    (big_a - 1) + (big_a + 1) * _cos
   UpperCAmelCase_					:					Dict			     =    2 * sqrt(__lowerCamelCase  ) * alpha

   UpperCAmelCase_					:					List[str]			     =    big_a * (pmc + aaa)
   UpperCAmelCase_					:					int			     =    2 * big_a * mpc
   UpperCAmelCase_					:					int			     =    big_a * (pmc - aaa)
   UpperCAmelCase_					:					Dict			     =    ppmc + aaa
   UpperCAmelCase_					:					Any			     =    -2 * pmpc
   UpperCAmelCase_					:					List[str]			     =    ppmc - aaa

   UpperCAmelCase_					:					List[Any]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					int			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Optional[Any]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					Tuple			     =    10 ** (gain_db / 40)
   UpperCAmelCase_					:					Tuple			     =    (big_a + 1) - (big_a - 1) * _cos
   UpperCAmelCase_					:					Optional[Any]			     =    (big_a + 1) + (big_a - 1) * _cos
   UpperCAmelCase_					:					List[Any]			     =    (big_a - 1) - (big_a + 1) * _cos
   UpperCAmelCase_					:					Any			     =    (big_a - 1) + (big_a + 1) * _cos
   UpperCAmelCase_					:					Dict			     =    2 * sqrt(__lowerCamelCase  ) * alpha

   UpperCAmelCase_					:					Any			     =    big_a * (ppmc + aaa)
   UpperCAmelCase_					:					Union[str, Any]			     =    -2 * big_a * pmpc
   UpperCAmelCase_					:					Dict			     =    big_a * (ppmc - aaa)
   UpperCAmelCase_					:					Optional[int]			     =    pmc + aaa
   UpperCAmelCase_					:					Union[str, Any]			     =    2 * mpc
   UpperCAmelCase_					:					int			     =    pmc - aaa

   UpperCAmelCase_					:					Union[str, Any]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt


