
    ꬜iY<                         d Z ddlmZ ddlmZ  ej
                  e      Z G d de      Z G d de      Z	 G d d	e      Z
g d
Zy)zALIGN model configuration   )PreTrainedConfig)loggingc                   J     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )AlignTextConfiga  
    This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a
    ALIGN text encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the text encoder of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are
    copied from BERT.

    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 30522):
            Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`AlignTextModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *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.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            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).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`AlignTextModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*):
            End of stream token id.

    Example:

    ```python
    >>> from transformers import AlignTextConfig, AlignTextModel

    >>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignTextConfig()

    >>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```align_text_modeltext_configc                     t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        y )N )super__init__
vocab_sizehidden_sizenum_hidden_layersnum_attention_heads
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epspad_token_idbos_token_ideos_token_id)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   kwargs	__class__s                    i/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/align/configuration_align.pyr   zAlignTextConfig.__init__W   s    & 	"6"$&!2#6 $!2#6 ,H)'>$.!2,(((    )i:w  i      r!   i   gelu皙?r#   i      {Gz?g-q=    NN)__name__
__module____qualname____doc__
model_typebase_config_keyr   __classcell__r   s   @r   r   r      sN    :x $J#O %( #!#) #)r    r   c            )            e Zd ZdZdZdZdddddg d	g d
g dg g dg dg dddddddddfdedededededee   dee   dee   dee   d ee   d!ee   d"ee   d#ed$e	d%ed&e	d'ed(ed)ed*ef( fd+Z
 xZS ),AlignVisionConfiga  
    This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a
    ALIGN vision encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the vision encoder of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied
    from EfficientNet (efficientnet-b7)

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

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 600):
            The input image size.
        width_coefficient (`float`, *optional*, defaults to 2.0):
            Scaling coefficient for network width at each stage.
        depth_coefficient (`float`, *optional*, defaults to 3.1):
            Scaling coefficient for network depth at each stage.
        depth_divisor `int`, *optional*, defaults to 8):
            A unit of network width.
        kernel_sizes (`list[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
            List of kernel sizes to be used in each block.
        in_channels (`list[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
            List of input channel sizes to be used in each block for convolutional layers.
        out_channels (`list[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
            List of output channel sizes to be used in each block for convolutional layers.
        depthwise_padding (`list[int]`, *optional*, defaults to `[]`):
            List of block indices with square padding.
        strides (`list[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
            List of stride sizes to be used in each block for convolutional layers.
        num_block_repeats (`list[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
            List of the number of times each block is to repeated.
        expand_ratios (`list[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
            List of scaling coefficient of each block.
        squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
            Squeeze expansion ratio.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
        hidden_dim (`int`, *optional*, defaults to 1280):
            The hidden dimension of the layer before the classification head.
        pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
            Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
            `"max"`]
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        batch_norm_eps (`float`, *optional*, defaults to 1e-3):
            The epsilon used by the batch normalization layers.
        batch_norm_momentum (`float`, *optional*, defaults to 0.99):
            The momentum used by the batch normalization layers.
        drop_connect_rate (`float`, *optional*, defaults to 0.2):
            The drop rate for skip connections.

    Example:

    ```python
    >>> from transformers import AlignVisionConfig, AlignVisionModel

    >>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignVisionConfig()

    >>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```align_vision_modelvision_configr   iX  g       @g@   )r   r      r   r4   r4   r   )          (   P   p      )r6   r7   r8   r9   r:   r;   i@  )   r$   r$   r$   r<   r$   r<   )r<   r$   r$   r   r      r<   )r<      r>   r>   r>   r>   r>   g      ?swishi 
  meanr%   gMbP?gGz?g?num_channels
image_sizewidth_coefficientdepth_coefficientdepth_divisorkernel_sizesin_channelsout_channelsdepthwise_paddingstridesnum_block_repeatsexpand_ratiossqueeze_expansion_ratior   
hidden_dimpooling_typer   batch_norm_epsbatch_norm_momentumdrop_connect_ratec                 b   t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        t-        |      dz  | _        y )Nr=   r
   )r   r   rA   rB   rC   rD   rE   rF   rG   rH   rI   rJ   rK   rL   rM   r   rN   rO   r   rP   rQ   rR   sumr   )r   rA   rB   rC   rD   rE   rF   rG   rH   rI   rJ   rK   rL   rM   r   rN   rO   r   rP   rQ   rR   r   r   s                         r   r   zAlignVisionConfig.__init__   s    0 	"6"($!2!2*(&(!2!2*'>$$$(!2,#6 !2!$%6!7!!;r    )r'   r(   r)   r*   r+   r,   intfloatliststrr   r-   r.   s   @r   r0   r0   }   s@   CJ &J%O #&#&"7!?"A')2'<#8)-!"#' %%)#&+.<.< .< !	.<
 !.< .< 3i.< #Y.< 3i.<  9.< c.<  9.< Cy.< "'.< .<  !.<" #.<$ !%.<& '.<( #).<* !+.< .<r    r0   c                   <     e Zd ZdZdZeedZ	 	 	 	 	 d fd	Z xZ	S )AlignConfiga  
    [`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to
    instantiate a ALIGN model according to the specified arguments, defining the text model and vision model configs.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the ALIGN
    [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture.

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

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`AlignTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`AlignVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 640):
            Dimensionality of text and vision projection layers.
        temperature_init_value (`float`, *optional*, defaults to 1.0):
            The initial value of the *temperature* parameter. Default is used as per the original ALIGN implementation.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import AlignConfig, AlignModel

    >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignConfig()

    >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

    >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
    >>> from transformers import AlignTextConfig, AlignVisionConfig

    >>> # Initializing ALIGN Text and Vision configurations
    >>> config_text = AlignTextConfig()
    >>> config_vision = AlignVisionConfig()

    >>> config = AlignConfig(text_config=config_text, vision_config=config_vision)
    ```align)r   r2   c                 ^   | t               }t        j                  d       nt        |t              rt        di |}| t               }t        j                  d       nt        |t              rt        di |}|| _        || _        || _        || _	        || _
        t        | 0  di | y )NzP`text_config` is `None`. Initializing the `AlignTextConfig` with default values.zT`vision_config` is `None`. initializing the `AlignVisionConfig` with default values.r
   )r   loggerinfo
isinstancedictr0   r   r2   projection_dimtemperature_init_valuer   r   r   )r   r   r2   ra   rb   r   r   r   s          r   r   zAlignConfig.__init__*  s     )+KKKjkT*)8K8K -/MKKnot,->>M&*,&<#!2"6"r    )NNi  g      ?r%   )
r'   r(   r)   r*   r+   r   r0   sub_configsr   r-   r.   s   @r   rZ   rZ      s6    -^ J"1DUVK "# #r    rZ   )r   r0   rZ   N)r*   configuration_utilsr   utilsr   
get_loggerr'   r]   r   r0   rZ   __all__r
   r    r   <module>rh      s\      3  
		H	%c)& c)Lw<( w<tN#" N#b Br    