Ë
    ÛÓ^j}  ã                   óä   — d dl mZ ddlmZ ddlmZ ddlmZ  ed¬«      e G d„ d	e«      «       «       Z ed¬«      e G d
„ de«      «       «       Z	 ed¬«      e G d„ de«      «       «       Z
g d¢Zy)é    )Ústricté   )ÚPreTrainedConfig)ÚRopeParameters)Úauto_docstringzEmu3-community/Emu3-Chat-hf)Ú
checkpointc                   ó  — e Zd ZU dZdZdZdZeed<   dZ	eed<   dZ
eed<   d	Zeed
<   dZeed<   dZeed<   dZeed<   dZeed<   dZee   eedf   z  ed<   dZeed<   dZee   eedf   z  ed<   dZeed<   dZeed<   dZeez  ed<   y)ÚEmu3VQVAEConfigaz  
    embed_dim (`int`, *optional*, defaults to 4):
        Dimension of the quantized vector in codebook.
    out_channels (`int`, *optional*, defaults to 3):
        Output channel of decoder.
    temporal_downsample_factor (`int`, *optional*, defaults to 4):
        Temporal downsample factor.
    base_channels (`int`, *optional*, defaults to 256):
        Basic channel number of the intermediate blocks.
    channel_multiplier (`list[int]`, *optional*, defaults to `[1, 2, 2, 4]`):
        Channel scaling factor of the intermediate blocks.
    num_res_blocks (`int`, *optional*, defaults to 2):
        Residual block number in each stage.
    attn_resolutions (`list[int]`, *optional*, defaults to `[3]`):
        Stage indices to apply attention.

    ```python
    >>> from transformers import Emu3VQVAE, Emu3VQVAEConfig

    >>> # Initializing a video VQ model of Emu3 configuration
    >>> configuration = Emu3VQVAEConfig()

    >>> # Initializing a model from the Emu3 VQ model style configuration
    >>> model = Emu3VQVAE(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    Ú
emu3_vqganÚ	vq_configi €  Úcodebook_sizeé   Ú	embed_dimÚlatent_channelsFÚdouble_latentr   Úin_channelsÚout_channelsÚtemporal_downsample_factoré   Úbase_channels)é   é   r   r   .Úchannel_multiplierr   Únum_res_blocks)r   Úattn_resolutionsi   Úhidden_sizer   Únum_attention_headsg        Úattention_dropoutN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typeÚbase_config_keyr   ÚintÚ__annotations__r   r   r   Úboolr   r   r   r   r   ÚlistÚtupler   r   r   r   r   Úfloat© ó    úv/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/emu3/configuration_emu3.pyr
   r
      sÊ   … ñð< €JØ!€Oà€M�3ÓØ€IˆsÓØ€O�SÓØ€M�4ÓØ€K�ÓØ€L�#ÓØ&'Ð Ó'Ø€M�3ÓØ6BÐ˜˜S™	 E¨#¨s¨(¡OÑ3ÓBØ€N�CÓØ48Ð�d˜3‘i %¨¨S¨¡/Ñ1Ó8Ø€K�ÓØ Ð˜Ó Ø%(Ð�u˜s‘{Ô(r,   r
   c                   óD  — e Zd ZU dZdZdZdgZdZdZe	e
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Ze	e
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d#<   d$Z!ee
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d&<   y)'ÚEmu3TextConfigaž  
    Example:

    ```python
    >>> from transformers import Emu3Model, Emu3Config

    >>> # Initializing a Emu3-community/Emu3-Chat-hf style configuration
    >>> configuration = Emu3Config()

    >>> # Initializing a model from the Emu3-community/Emu3-Chat-hf style configuration
    >>> model = Emu3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úemu3_text_modelÚtext_configÚpast_key_valuesg    €„.Ai.Ñ Ú
vocab_sizei   r   i 8  Úintermediate_sizeé    Únum_hidden_layersr   é   NÚnum_key_value_headsÚsiluÚ
hidden_acti $  Úmax_position_embeddingsgñhãˆµøä>Úrms_norm_epsTÚ	use_cachei[P Úpad_token_idi)Q Úbos_token_idi*Q Úeos_token_idÚrope_parametersFgš™™™™™¹?r   g{®Gáz”?Úinitializer_rangeÚtie_word_embeddings)#r   r    r!   r"   r#   r$   Úkeys_to_ignore_at_inferenceÚdefault_thetar3   r%   r&   r   r4   r6   r   r8   r:   Ústrr;   r<   r*   r=   r'   r>   r?   r@   r(   rA   r   ÚdictÚmlp_biasÚattention_biasr   rB   rC   r+   r,   r-   r/   r/   L   sü   … ñð  #€JØ#€OØ#4Ð"5ÐØ€Mà€J�ÓØ€K�ÓØ"Ð�sÓ"ØÐ�sÓØ!Ð˜Ó!Ø&'Ð˜˜t™Ó'Ø€J�ÓØ#'Ð˜SÓ'Ø€L�%ÓØ€IˆtÓØ€L�#ÓØ€L�#ÓØ+1€L�#˜˜S™	‘/ DÑ(Ó1Ø48€O�^ dÑ*¨TÑ1Ó8Ø€HØ€NØ%(Ð�u˜s‘{Ó(Ø#Ð�uÓ#Ø %Ð˜Ô%r,   r/   c                   ó˜   ‡ — e Zd ZU dZdZdgZeedœZdZ	e
ez  dz  ed<   dZe
ez  dz  ed<   dZe
eef   dz  ed<   d	Zeed
<   ˆ fd„Zˆ xZS )Ú
Emu3Configz¢
    vocabulary_map (`dict`, *optional*):
        A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs.
    Úemu3r2   )r1   r   Nr   r1   Úvocabulary_mapFrC   c                 óÀ  •— | j                   €t        «       | _         n4t        | j                   t        «      rt        di | j                   ¤Ž| _         | j                  €t        «       | _        n4t        | j                  t        «      rt        di | j                  ¤Ž| _        | j                  �| j                  j                  d«      nd | _        t        ‰| �(  di |¤Ž y )Nz<image>r+   )r   r
   Ú
isinstancerG   r1   r/   rM   ÚgetÚimage_token_idÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s     €r-   rS   zEmu3Config.__post_init__Š   s¬   ø€ Ø�>‰>Ð!Ü,Ó.ˆD�NÜ˜Ÿ™¬Ô-Ü,Ñ>¨t¯~©~Ñ>ˆDŒNà×ÑÐ#Ü-Ó/ˆDÕÜ˜×(Ñ(¬$Ô/Ü-ÑA°×0@Ñ0@ÑAˆDÔàDH×DWÑDWÐDc˜d×1Ñ1×5Ñ5°iÔ@ÐimˆÔÜ‰ÑÑ' Ó'r,   )r   r    r!   r"   r#   rD   r/   r
   Úsub_configsr   rG   r&   r1   rM   rF   r%   rC   r'   rS   Ú__classcell__)rV   s   @r-   rK   rK   y   sv   ø… ñð
 €JØ#4Ð"5ÐØ"0¸ÑO€Kà/3€Iˆt�oÑ%¨Ñ,Ó3Ø04€K�˜Ñ&¨Ñ-Ó4Ø,0€N�D˜˜c˜‘N TÑ)Ó0Ø %Ð˜Ó%÷(ð (r,   rK   )rK   r/   r
   N)Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úmodeling_rope_utilsr   Úutilsr   r
   r/   rK   Ú__all__r+   r,   r-   ú<module>r^      sš   ðõ" /å 3Ý 1Ý #ñ Ð8Ô9Øô/)Ð&ó /)ó ó :ð/)ñd Ð8Ô9Øô(&Ð%ó (&ó ó :ð(&ñV Ð8Ô9Øô(Ð!ó (ó ó :ð(ò< >�r,   