




'''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). '''      ,	__snake_case      ,	)
class 				_a							(       __snake_case      ):





       '''simple docstring'''






       A		:							str								=		RobertaConfig
       A		:							Any								=		'roberta'







       def __init__(	self,				A   ):







             '''simple docstring'''







             super().__init__(A_   )

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		RobertaEmbeddings(A_   )
             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. '''      ,	__snake_case      ,	)
class 				_a							(       __snake_case      ):





       '''simple docstring'''






       A		:							Optional[int]								=		RobertaConfig
       A		:							List[Any]								=		'roberta'







       def __init__(	self,				A   ):







             '''simple docstring'''







             super().__init__(A_   )
             SCREAMING_SNAKE_CASE					:	Any   =		config.num_labels
             SCREAMING_SNAKE_CASE					:	int   =		config.num_hidden_layers

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







       @add_start_docstrings_to_model_forward(A_   )
       def     UpperCamelCase_				(	self,				A=None,				A=None,				A=None,				A=None,				A=None,				A=None,				A=None,				A=-1,				A=False,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.num_layers
             try:
                   SCREAMING_SNAKE_CASE					:	Any   =		self.roberta(
                       A_,				attention_mask=A_,				token_type_ids=A_,				position_ids=A_,				head_mask=A_,				inputs_embeds=A_,				)

                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		outputs[1]

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

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

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

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









from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
