
    sg_B                         d Z ddlmZ ddlmZmZmZmZmZ ddl	m
Z
 ddlmZmZ ddlmZ erddlmZ dd	lmZ dd
lmZ  ej*                  e      Zg dZg dZ G d de
      Z G d de      Zy)zWhisper model configuration    )OrderedDict)TYPE_CHECKINGAnyMappingOptionalUnion   )PretrainedConfig)
OnnxConfigOnnxSeq2SeqConfigWithPast)logging)FeatureExtractionMixin)PreTrainedTokenizerBase)
TensorType)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  c            %            e Zd ZdZdZdgZddddZ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gddddddddddf% fd	Z xZS )WhisperConfiga#  
    This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
    Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the Whisper
    [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 51865):
            Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
            `decoder_input_ids` passed when calling [`WhisperModel`]
        num_mel_bins (`int`, *optional*, defaults to 80):
            Number of mel features used per input features. Should correspond to the value used in the
            `WhisperProcessor` class.
        encoder_layers (`int`, *optional*, defaults to 4):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 4):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 6):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 6):
            Number of attention heads for each attention layer in the Transformer decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 1536):
            Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 1536):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_start_token_id (`int`, *optional*, defaults to 50257):
            Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
            are provided to the `generate` function. It is used to guide the model`s generation process depending on
            the task.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
            Whether the model is used as an encoder/decoder or not.
        activation_function (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        d_model (`int`, *optional*, defaults to 384):
            Dimensionality of the layers.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        scale_embedding (`bool`, *optional*, defaults to False):
            Scale embeddings by diving by sqrt(d_model).
        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).
        pad_token_id (`int`, *optional*, defaults to 50256):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 50256):
            Begin of stream token id.
        eos_token_id (`int`, *optional*, defaults to 50256):
            End of stream token id.
        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 supressed 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://arxiv.org/abs/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
            procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
            reasoning from the propability 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 procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
            the axis. If reasoning from the propability 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)num_key_value_headsnum_attention_headshidden_sizei  P         i   g        r(   Tgelui  g{Gz?Fi  i  iP  N      g?r   r   r   r   c&                    || _         || _        || _        || _        || _        || _        || _        || _        || _        || _	        || _
        || _        || _        || _        |	| _        |
| _        || _        || _        || _        || _        || _        || _        || _        || _        || _        | | _        |!| _        |"| _        |#| _        |$| _        |%| _        t?        '|   d|||||||d|& y )N)pad_token_idbos_token_ideos_token_idis_encoder_decoderdecoder_start_token_idsuppress_tokensbegin_suppress_tokens )!
vocab_sizenum_mel_binsr0   encoder_layersr/   decoder_layersdecoder_attention_headsdecoder_ffn_dimencoder_ffn_dimdropoutattention_dropoutactivation_dropoutactivation_functioninit_stdencoder_layerdropdecoder_layerdrop	use_cachenum_hidden_layersscale_embeddingmax_source_positionsmax_target_positionsclassifier_proj_sizeuse_weighted_layer_sumapply_spec_augmentmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masksmedian_filter_widthsuper__init__)(selfrC   rD   rE   r/   rF   rG   rH   rI   rO   rP   r?   rQ   r>   rM   r0   rJ   rK   rL   rN   rS   rT   rU   r;   r<   r=   r@   rA   rW   rV   rX   rY   rZ   r[   r\   r]   r^   r_   kwargs	__class__s(                                          d/var/www/html/venv/lib/python3.12/site-packages/transformers/models/whisper/configuration_whisper.pyra   zWhisperConfig.__init__   s.   R %(,'>$,'>$..!2"4#6  !2!2"!/.$8!$8! %9!&<# #5, 0#6 !2#6 &<##6  		
%%%1#9+"7		
 		
    )	__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapra   __classcell__rd   s   @re   r,   r,   ;   s    @D J#4"588 M  ! !$"! "El$   MW
 W
rf   r,   c                        e Zd Zedeeeeef   f   fd       Z	 	 	 	 	 	 	 dded   dedede	de
d	   d
edededeeef   f fdZedefd       Z xZS )WhisperOnnxConfigreturnc                     t        dddddfg      }| j                  rddi|d<   nddd	|d<   | j                  r| j                  |d
       |S )Ninput_featuresbatchfeature_sizeencoder_sequence)r   r   r   r   decoder_input_idsdecoder_sequence)r   r   inputs)	direction)r   use_pastfill_with_past_key_values_)rb   common_inputss     re   rz   zWhisperOnnxConfig.inputs!  sj    #!w>FX#YZ

 ==23WM-.5<AS1TM-.==++MX+Nrf   preprocessor)r   r   
batch_size
seq_lengthis_pair	frameworkr   sampling_ratetime_duration	frequencyc	           	      l   t               }	t        j                  | |j                  |||||      }
|
d   j                  d   }| j
                  r|dz  n|}t        | 	  |j                  ||||      }|
j                  d      |	d<   |j                  d      |	d<   d|v r|j                  d      |	d<   |	S )N)r   r   r   r   r   r   rt   r   rx   r.   )	r   r   generate_dummy_inputsfeature_extractorshaper|   r`   	tokenizerpop)rb   r   r   r   r   r   r   r   r   dummy_inputsencoder_inputsencoder_sequence_lengthdecoder_inputsrd   s                re   r   z'WhisperOnnxConfig.generate_dummy_inputs2  s     #}#99%77!''
 #11A"B"H"H"K59]],1

6""J
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 *8););<L)M%&,:,>,>?R,S()..<.@.@AR.SL*+rf   c                      y)NgMbP?rB   )rb   s    re   atol_for_validationz%WhisperOnnxConfig.atol_for_validationV  s    rf   )r   FNi"V  g      @r8   )rg   rh   ri   propertyr   strintrz   r   boolr   floatr   r   r   rn   ro   s   @re   rq   rq      s    WS#X%6 67  & ,0"""OP" " 	"
 " L)" " " " 
c	"H U  rf   rq   N)rj   collectionsr   typingr   r   r   r   r   configuration_utilsr
   onnxr   r   utilsr   feature_extraction_utilsr   tokenization_utils_baser   r   
get_loggerrg   loggerNON_SPEECH_TOKENSNON_SPEECH_TOKENS_MULTIr,   rq   rB   rf   re   <module>r      si    " # ? ? 3 9  BB#			H	%
 
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