o
    
ji                     @   s<  d dl Z d dlZd dlmZmZmZ d dlZd dlZd dl	m
Z
 d dlm
  mZ d dl	mZ d dlmZmZmZmZmZmZmZmZmZ e eZ			 		 ddeeef d	eej d
ededededede dedej!fddZ"G dd dZ#G dd de
j$Z%G dd de
j$Z&G dd de
j$Z'G dd de
j$Z(dS )    N)ListOptionalTuple)	LayerNorm)	Fp32GroupNormFp32LayerNorm
GLU_LinearGradMultiplyMultiheadAttentionSamePadTransposeLastget_activation_fninit_bert_paramsstatic        Fshapepadding_mask	mask_probmask_length	mask_type
mask_other	min_masks
no_overlap	min_spacereturnc	              	      s  | \}	}
t |	|
fd}t||
 t| t j  }t||}g }t|	D ]}|durN|
||  	 
  }t|| t| t j  }t||}n|
}|}|dkr]t ||nA|dkrot jj||d d |dn/|dkrt jj|||dd	d
 D n|dkrt jj||ddd
 D ntd| t	dkrt||d d< |rg fdd}d|fg}t}tddD ]> t  fdd|D t j}t 	|}|dkr n#|t 	| }t jjt||d}||\}}|||| | qt n-t}|| |kr|| d }t jj|| |ddt fdd
ttD |t |k   q&tdd
 |D }t|D ]\}t|krht jj|ddd||f< qT|S )a  
    Computes random mask spans for a given shape

    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_type: how to compute mask lengths
            static = fixed size
            uniform = sample from uniform distribution [mask_other, mask_length*2]
            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
            poisson = sample from possion distribution with lambda = mask length
        min_masks: minimum number of masked spans
        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
    FNr   uniform      )sizenormalc                 S   s   g | ]}t d tt|qS )r   )maxintround.0x r&   T/home/kuhnn/.local/lib/python3.10/site-packages/TTS/vc/modules/freevc/wavlm/wavlm.py
<listcomp>a   s    z(compute_mask_indices.<locals>.<listcomp>poissonc                 S   s   g | ]}t t|qS r&   )r!   r"   r#   r&   r&   r'   r(   d   s    zunknown mask selection r   c                    s   t j| ||   fddt|D  g } |   |kr,||   d f |  |  |krA| |  |f |S )Nc                 3   s    | ]} | V  qd S Nr&   r$   i
span_startr&   r'   	<genexpr>p   s    z8compute_mask_indices.<locals>.arrange.<locals>.<genexpr>r   )nprandomrandintextendrangeappend)selengthkeep_length	new_parts)mask_idcr   r-   r'   arrangen   s   z%compute_mask_indices.<locals>.arrangeT)reversec                 3   s0    | ]\}}||   kr|| nd V  qdS )r   Nr&   )r$   r6   r7   )r8   r   r&   r'   r/   }   s   . z'compute_mask_indices.<locals>.<genexpr>p)replacec                    s*   g | ]}t  | D ]}| | q
qS r&   )r4   )r$   joffset)lengthsr;   r&   r'   r(      s   * c                 S   s   g | ]}t |qS r&   )len)r$   mr&   r&   r'   r(      s    )r0   fullr!   floatr1   randr    r4   longsumitemr2   r   r)   	ExceptionminsortedfromiterchoicerD   popr3   asarrayr5   unique	enumerate)r   r   r   r   r   r   r   r   r   bszall_szmaskall_num_mask	mask_idcsr,   sznum_maskr<   parts
min_lengthlensl_sumprobscr6   r7   min_lenr&   )r8   rC   r;   r   r'   compute_mask_indices#   s~   


"rc   c                   @   s$   e Zd ZdddZdefddZdS )WavLMConfigNc                 C   s   d| _ d| _d| _d| _d| _d| _d| _d| _d| _d| _	d| _
d	| _d	| _d
| _d
| _d
| _d
| _d| _d| _d| _d| _d| _d| _d| _d
| _d| _d| _d| _d| _d| _d| _d| _d| _ d| _!d| _"|d urt| #| d S d S )Ndefault         geluFz0[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2      ?皙?r   
   g?r   r   r         i@  i   )$extractor_modeencoder_layersencoder_embed_dimencoder_ffn_embed_dimencoder_attention_headsactivation_fnlayer_norm_firstconv_feature_layers	conv_biasfeature_grad_mult	normalizedropoutattention_dropoutactivation_dropoutencoder_layerdropdropout_inputdropout_featuresr   r   mask_selectionr   no_mask_overlapmask_min_spacemask_channel_lengthmask_channel_probmask_channel_selectionmask_channel_otherno_mask_channel_overlapmask_channel_min_spaceconv_posconv_pos_groupsrelative_position_embeddingnum_bucketsmax_distancegru_rel_posupdateselfcfgr&   r&   r'   __init__   sP   zWavLMConfig.__init__r   c                 C   s   | j | d S r*   )__dict__r   r   r&   r&   r'   r      s   zWavLMConfig.updater*   )__name__
__module____qualname__r   dictr   r&   r&   r&   r'   rd      s    
9rd   c                       s   e Zd Zdeddf fddZdd Zdejd	ejdejfd
dZ					ddejd	e	ej de
de
de	e de
fddZ  ZS )WavLMr   r   Nc                    s(  t    td|j  || _t|j}|d d | _t	|d|j
|jd| _| j|jkr6t| j|jnd | _|j| _|j| _|j| _|j| _|j| _|j| _|j| _|j| _|j| _|j| _|j| _|j| _t|j| _t|j| _|j | _ t!t"#|j$ | _%t&|| _'t(| j| _)d S )NzWavLM Config: r   r   )conv_layersrz   moderw   )*superr   loggerinfor   r   evalrv   embedConvFeatureExtractionModelro   rw   feature_extractorrq   nnLinearpost_extract_projr   r   r   r   r   r   r   r   r   r   r   r   Dropoutr~   r   rx   	ParametertorchFloatTensoruniform_mask_embTransformerEncoderencoderr   
layer_norm)r   r   feature_enc_layers	__class__r&   r'   r      s>   


zWavLM.__init__c                 C   s   |j \}}}| jdkr/t||f|| j| j| j| jd| j| jd	}t	|
|j}| j||< nd }| jdkr_t||fd | j| j| j| j| j| jd}t	|
|jdd|d}d||< ||fS )Nr   r   )r   r   r   )r   r   r   r   )r   r   rc   r   r   r   r   r   r   
from_numpytodevicer   r   r   r   r   r   r   	unsqueezeexpand)r   r%   r   BTCmask_indicesmask_channel_indicesr&   r&   r'   
apply_mask	  s<   

"
zWavLM.apply_maskfeaturesr   c                 C   sZ   | d| d }|dkr|d d d | f }|| d| dd}|d}|S )Nr   r   r   )r   viewany)r   r   r   extrar&   r&   r'   forward_padding_mask,  s   
zWavLM.forward_padding_maskFsourcerW   ret_convoutput_layerret_layer_resultsc                 C   s$  | j dkr| |}| j dkrt|| j }nt  | |}W d    n1 s+w   Y  |dd}| |}|d urE| ||}| j	d urO| 	|}| 
|}|r_| ||\}}	n|}| j|||d u rkd n|d d\}}
||||
d}|r|d n|d }|r||d	 f}||d
 fS )Nr   rj   r   r   )r   layer)r%   r   r   layer_resultsr   r%   r   r   )rx   r   r	   applyr   no_grad	transposer   r   r   r~   r   r   )r   r   r   rW   r   r   r   r   r%   r   r   resfeaturer&   r&   r'   extract_features9  s4   
	







zWavLM.extract_features)NFFNF)r   r   r   rd   r   r   r   Tensorr   r   boolr!   r   __classcell__r&   r&   r   r'   r      s@    .#
r   c                       sT   e Zd Z				ddeeeeef  dedededef
 fd	d
Z	dddZ
  ZS )r   r   re   Fr   rz   r   rw   	conv_typec                    s  t    |dv sJ 			d fdd	}|| _| jdkr\d}t | _t|D ]0\}}	t|	dks;J dt|	 |	\ }
}| j	|| |
||d	k|dkoR|d
k|d  }q)d S | jdkrd}t | _t|D ])\}}	t|	dksxJ |	\ }
}| j	t
j| |
| | j	t
j   }qld S | jdkrd}d}t | _t|D ]V\}}	t|	dksJ |	\ }
}| j	t
jj| |
|dd | j	t
j |g | j	t
j   }|d d d
kr| j	t
jjdddd tt|d }qd S 	 d S )N>   re   r   Fc                    s    fdd}|o|dksJ d|r0t | t jdt t tddt t  S |rFt | t jdtddt  S t | t jdt  S )	Nc                     s&   t j d} t j| j | S )N)stridebias)r   Conv1dinitkaiming_normal_weight)convrw   kn_inn_outr   r&   r'   	make_conv  s   zEConvFeatureExtractionModel.__init__.<locals>.block.<locals>.make_convFz'layer norm and group norm are exclusiver>   T)elementwise_affine)affine)r   
Sequentialr   r   r   GELUr   )r   r   r   r   is_layer_normis_group_normrw   r   dimrz   r   r'   blockx  s*   	



z2ConvFeatureExtractionModel.__init__.<locals>.blockre   r      zinvalid conv definition: r   r   )r   r   rw   conv2dcustomP   )paddingr   T)r   	ceil_mode)FFF)r   r   r   r   
ModuleListr   rT   rD   strr5   r   Conv2dReLUr   	MaxPool2dr!   mathceil)r   r   rz   r   rw   r   r   in_dr,   clr   r   idimr   r   r'   r   l  sj   
%







z#ConvFeatureExtractionModel.__init__Nc                 C   s   | d}| jdkr@| jD ]}t|tjr$|dd}||dd}q||}q|dd }||	dd|	d}|S | jD ]}||}qC| jdkrf|	 \}}}}|dd ||| |}|S )Nr   r   r   r   r   r   r   )
r   r   r   
isinstancer   r   r   
contiguousr   r   )r   r%   rW   r   bra   tfr&   r&   r'   forward  s    






z"ConvFeatureExtractionModel.forward)r   re   Fre   r*   )r   r   r   r   r   r!   rG   r   r   r   r   r   r&   r&   r   r'   r   k  s"    `r   c                       s0   e Zd Z fddZdddZdddZ  ZS )	r   c                    s>  t     j_ j_tjjj j jd  jd_	d}t
dd|   jj  }tjjj	jd|d tjj	jd tjjjj	ddd_	tj	t jt _	t d	rp j_ j_ j_n	d
_d_d_t fddt jD _ j_t j_! j"_#$t% d S )Nr   )kernel_sizer   groupsr      rj   )meanstdr   )namer   r   Fc                    sL   g | ]"}t j j jj j j j jj	o|d kj
j jdqS )r   )embedding_dimffn_embedding_dimnum_attention_headsrz   r{   r|   rt   ru   has_relative_attention_biasr   r   r   )TransformerSentenceEncoderLayerr  rr   rs   rz   r{   r|   rt   ru   r   r   r   r   r+   argsr   r&   r'   r(     s"    z/TransformerEncoder.__init__.<locals>.<listcomp>)&r   r   rz   rq   r  r   r   r   r   pos_convr   sqrtr   normal_r   	constant_r   utilsparametrizationsweight_normr   r   r   hasattrr   r   r   r   r4   rp   layersru   r   r   r}   	layerdropr   r   )r   r
  rz   r  r   r	  r'   r     s@   


zTransformerEncoder.__init__Nc                 C   s4   |  ||||\}}| jr|d u r| |}||fS r*   )r   ru   r   )r   r%   r   streaming_maskr   r   r&   r&   r'   r     s   
zTransformerEncoder.forwardc                 C   s  |d urd||< |  |dd}|dd}||7 }| js#| |}tj|| j| jd}|dd}g }d }|d urB|||f d }d }	t| j	D ]0\}
}t
j }| jr\|| jkrh|||d||	d\}}}	|d urs|||f |
|kr{|} nqK|d ur|}|dd}||fS )Nr   r   r   )r?   trainingF)self_attn_padding_maskneed_weightsself_attn_maskpos_bias)r  r   ru   r   Frz   r  r5   rT   r  r0   r1   r  )r   r%   r   r  	tgt_layerx_convr   zrr  r,   r   dropout_probabilityr&   r&   r'   r   "  sF   

z#TransformerEncoder.extract_features)NNN)r   r   r   r   r   r   r   r&   r&   r   r'   r     s    
8r   c                       s   e Zd ZdZ													d d	ed
ededededededededededededdf fddZ				d!de	j
de	j
de	j
defddZ  ZS )"r  z_
    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
    models.
    rg   rh      rk   reluFr   r  r  r  rz   r{   r|   rt   ru   r  r   r   rescale_initr   r   Nc                    s   t    || _|| _|| _|| _t|| _t| j||d|	|
|||d	| _	t
|| _t
| j| _t
|| _|| _t| j| _| jdkrOt| j|d| _nt
| j|| _t
|| j| _t| j| _d S )NT)rz   self_attentionr  r   r   r#  r   gluswish)r   r   r  rz   r|   activation_namer   rt   r
   	self_attnr   r   dropout1dropout2dropout3ru   r   self_attn_layer_normr   fc1r   fc2final_layer_norm)r   r  r  r  rz   r{   r|   rt   ru   r  r   r   r#  r   r   r&   r'   r   W  s6   


z(TransformerSentenceEncoderLayer.__init__r%   r  r  r  c              	   C   s>  |}| j rP| |}| j||||d||d\}}}| |}|| }|}| |}| jdkr4| |}n| | |}| |}| 	|}| 
|}|| }nJ| j|||||||d\}}}| |}|| }| |}|}| jdkrz| |}n| | |}| |}| 	|}| 
|}|| }| |}|||fS )z
        LayerNorm is applied either before or after the self-attention/ffn
        modules similar to the original Transformer imlementation.
        F)querykeyvaluekey_padding_maskr  	attn_maskposition_biasr%  )ru   r,  r(  r)  r/  r'  r-  rt   r*  r.  r+  )r   r%   r  r  r  r  residualattnr&   r&   r'   r     sZ   

	














z'TransformerSentenceEncoderLayer.forward)rg   rh   r!  rk   rk   rk   r"  FFr   r   FF)NNFN)r   r   r   __doc__rG   r   r   r!   r   r   r   r   r   r&   r&   r   r'   r  Q  sr    	
:r  )r   r   r   Fr   ))loggingr   typingr   r   r   numpyr0   r   torch.nnr   torch.nn.functional
functionalr  r   #TTS.vc.modules.freevc.wavlm.modulesr   r   r   r	   r
   r   r   r   r   	getLoggerr   r   r!   r   rG   r   r   ndarrayrc   rd   Moduler   r   r   r  r&   r&   r&   r'   <module>   sT   	,

	

y> vp