
    Z j<                      t    S r SSKJr  SSKJr  SSKJr  / SQr/ SQr\" SS	9\ " S
 S\5      5       5       r	S/r
g)zWhisper model configuration    )strict   )PreTrainedConfig)auto_docstring)X            	   
                        :   ;   <   =   >   ?   Z   [   \   ]   ie  in  i  i  i  i  i  i  i"  i  i  i  i  i?  ia  io  ic  i  iS  ir  i9	  i	  i  i  is  i  i  i  i  i  i#  i%  i&  iC)  i"*  i,  i-  i.  ik3  i5  i5  i9  i;  i@  iA  iHF  iK  i6L  iP  i!W  iY  ii  iu  iv  i  i  i[  i-  ie  i  i  Q  i      )Vr   r   r	   r
   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   ig  i  i
  i  ii  i}  i  i  i  i  iF  i=  i  i	  iC
  i  i  i  i  i  iy  iW  i;  i  i  ii  ie#  i$  i(  i*  i.  i/  i+0  i1  i5  iM7  i+9  i;  i=  i@  i@  iG  iJ  ikN  iT  iW  if  i1f  iCg  iwn  is  i{  i.~  i~  i  io  iA  i  iN  iR  r   r    i  zopenai/whisper-tiny)
checkpointc                      \ rS rSr% SrSrS/rSSSSS.rS	r\	\
S
'   Sr\	\
S'   Sr\	\
S'   Sr\	\
S'   Sr\	\
S'   Sr\	\
S'   Sr\	\
S'   Sr\	\
S'   Sr\\	-  \
S'   Sr\\	-  \
S'   Sr\	\
S'   Sr\\
S'   Sr\\
S'   Sr\\
S'   Sr\	\
S'   Sr\\	-  \
S'   Sr\\	-  \
S '   Sr\\	-  \
S!'   S"r\\
S#'   S$r \\
S%'   S&r!\	\
S''   S(r"\	\
S)'   S*r#\	S+-  \
S,'   S*r$\	S+-  \
S-'   S*r%\	\&\	   -  S+-  \
S.'   S+r'\&S+-  \
S/'   S0r(\&\	   \)\	S14   -  S+-  \
S2'   S$r*\\
S3'   S4r+\	\
S5'   S$r,\\
S6'   S7r-\\	-  \
S8'   S9r.\	\
S:'   S;r/\	\
S<'   Sr0\\	-  \
S='   S9r1\	\
S>'   S?r2\	\
S@'   SAr3\	\
SB'   Sr4\\
SC'   SDr5g+)EWhisperConfig0   a  
max_source_positions (`int`, *optional*, defaults to 1500):
    The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
max_target_positions (`int`, *optional*, defaults to 448):
    The maximum sequence length that this model might ever be used with. Typically set this to something large
    just in case (e.g., 512 or 1024 or 2048).
suppress_tokens (`list[int]`, *optional*):
    A list containing the non-speech tokens that will be used by the logit processor in the `generate`
    function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
    `multilingual` model.
begin_suppress_tokens (`list[int]`, *optional*, defaults to `[220,50256]`):
    A list containing tokens that will be suppressed at the beginning of the sampling process. Initialized as
    the token for `" "` (`blank_token_id`) and the `eos_token_id`
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
    Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
    instance of [`WhisperForAudioClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
    Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
    instance of [`WhisperForAudioClassification`].
apply_spec_augment (`bool`, *optional*, defaults to `False`):
    Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
    [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
    Recognition](https://huggingface.co/papers/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
    Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
    procedure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
    reasoning from the probability of each feature vector to be chosen as the start of the vector span to be
    masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
    actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`.
mask_time_length (`int`, *optional*, defaults to 10):
    Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
    The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
    irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
    mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
    Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
    masking procedure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
    the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector
    span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
    may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
    True`.
mask_feature_length (`int`, *optional*, defaults to 10):
    Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0):
    The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
    step, irrespectively of `mask_feature_prob`. Only relevant if
    `mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`.
median_filter_width (`int`, *optional*, defaults to 7):
    Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
    Should be an odd number.

Example:

```python
>>> from transformers import WhisperConfig, WhisperModel

>>> # Initializing a Whisper tiny style configuration
>>> configuration = WhisperConfig()

>>> # Initializing a model (with random weights) from the tiny style configuration
>>> model = WhisperModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```whisperpast_key_valuesencoder_attention_headsd_modelencoder_layers)num_key_value_headsnum_attention_headshidden_sizenum_hidden_layersi  
vocab_sizeP   num_mel_bins      decoder_layersdecoder_attention_headsi   decoder_ffn_dimencoder_ffn_dimg        encoder_layerdropdecoder_layerdropr   decoder_start_token_idT	use_cacheis_encoder_decodergeluactivation_functioni  dropoutattention_dropoutactivation_dropoutg{Gz?init_stdFscale_embeddingi  max_source_positionsi  max_target_positionsP  Npad_token_idbos_token_ideos_token_idsuppress_tokens)   rE   .begin_suppress_tokensuse_weighted_layer_sum   classifier_proj_sizeapply_spec_augmentg?mask_time_probr   mask_time_lengthr   mask_time_min_masksmask_feature_probmask_feature_lengthr   mask_feature_min_masksr	   median_filter_widthtie_word_embeddings )6__name__
__module____qualname____firstlineno____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr.   int__annotations__r0   r)   r'   r3   r4   r5   r6   r7   floatr8   r9   r:   boolr;   r=   strr(   r>   r?   r@   rA   rB   rC   rD   rF   rG   rH   listrI   rK   tuplerL   rN   rO   rP   rQ   rR   rS   rT   rU   rV   rW   __static_attributes__rX       ڂ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/whisper/configuration_whisper.pyr#   r#   0   s   AF J#4"588 -	M JL#NC#$S$NC#$S$OSOS%(us{(%(us{("'C'It##%%GSGUS[%(us{(&))He!OT! $#$ ###$L#*$$L#*$+0L#S	/D(0#'OTD['@L49uS#X6=L#(D( ###$$"&NECK&c  %(us{(!!"#C#   $$ri   r#   N)r]   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   NON_SPEECH_TOKENSNON_SPEECH_TOKENS_MULTIr#   __all__rX   ri   rj   <module>rq      s[    " . 3 #
 
  01r%$ r%  2r%j 
ri   