






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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  RobertaConfig
  _lowerCamelCase    =  '''roberta'''


  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]							)						->	Dict:


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

         _UpperCAmelCase							=    RobertaEmbeddings(lowerCamelCase							)
         self.init_weights()



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

  '''simple docstring'''







  _lowerCamelCase    =  RobertaConfig
  _lowerCamelCase    =  '''roberta'''


  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         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		:			Tuple       ,				lowerCamelCase		:			Dict=None       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Any=None       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			List[Any]=None       ,				lowerCamelCase		:			Optional[int]=-1       ,				lowerCamelCase		:			Union[str, Any]=False       ,				)						->	int:


         """simple docstring"""
         _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"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
							__lowerCamelCase   :   Any       =output_stream[0]
							__lowerCamelCase   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

							__lowerCamelCase   :   Any       =[
							    '''ffmpeg''',
							    '''-f''',
							    format_,
							    '''-i''',
							    input_,
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-fflags''',
							    '''nobuffer''',
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]
							__lowerCamelCase   :   Tuple       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							__lowerCamelCase   :   int       =_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							for item in iterator:
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

							__lowerCamelCase   :   Union[str, Any]       =ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    format_for_conversion=SCREAMING_SNAKE_CASE )
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   int       =np.intaa
														__lowerCamelCase   :   Optional[int]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   Dict       =np.floataa
														__lowerCamelCase   :   Tuple       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

							__lowerCamelCase   :   List[str]       =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
							__lowerCamelCase   :   int       =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
							__lowerCamelCase   :   Optional[int]       =datetime.datetime.now()
							__lowerCamelCase   :   List[Any]       =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    stride=(stride_left, stride_right)     ,    stream=SCREAMING_SNAKE_CASE ):
														# Put everything back in numpy scale
														__lowerCamelCase   :   Optional[int]       =np.frombuffer(item['''raw''']     ,    dtype=SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Dict       =(
														    item['''stride'''][0] // size_of_sample,
														    item['''stride'''][1] // size_of_sample,
														)
														__lowerCamelCase   :   Optional[Any]       =sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =B''''''
							__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
							__lowerCamelCase   :   List[str]       =0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
																					__lowerCamelCase   :   List[str]       =(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
																												# We are flushing the accumulator
																												__lowerCamelCase   :   Optional[Any]       =(_stride_left, stride_right)
																												__lowerCamelCase   :   str       ={'''raw''': acc[:chunk_len], '''stride''': stride}
																												if stream:
																																			__lowerCamelCase   :   Optional[Any]       =False
																												yield item
																												__lowerCamelCase   :   Dict       =stride_left
																												__lowerCamelCase   :   str       =acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE ) > stride_left:
														__lowerCamelCase   :   List[str]       ={'''raw''': acc, '''stride''': (_stride_left, 0)}
														if stream:
																					__lowerCamelCase   :   Tuple       =False
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error




