o
    h4                     @   sv   d Z ddlZddlmZ ddlmZ ddlmZ ee	Z
ddiZG d	d
 d
eZG dd deZG dd deZdS )z KOSMOS-2 model configuration    N)Union   )PretrainedConfig)loggingzmicrosoft/kosmos-2-patch14-224zNhttps://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/config.jsonc                       s|   e Zd ZdZdZdgZddddZ						
														d fdd	Zede	e
ejf ddfddZ  ZS )Kosmos2TextConfigav  
    This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a
    KOSMOS-2 text decoder 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 decoder of the KOSMOS-2
    [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 65037):
            Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Kosmos2Model`].
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            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).
        embed_dim (`int`, *optional*, defaults to 2048):
            Dimensionality of the layers and the pooler layer.
        layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        ffn_dim (`int`, *optional*, defaults to 8192):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        activation_function (`str` or `function`, *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.
        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 0.1):
            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.
        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.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        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 `True`):
            Scale embeddings by diving by sqrt(embed_dim).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    ```kosmos_2_text_modelpast_key_valuesattention_heads	embed_dimlayers)num_attention_headshidden_sizenum_hidden_layers                gelu皙?        h㈵>{Gz?T   r      c                    sx   t  jd|||d| || _|| _|| _|| _|| _|| _|| _|| _	|	| _
|
| _|| _|| _|| _|| _|| _d S )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizemax_position_embeddingsr
   r   ffn_dimr	   activation_functiondropoutattention_dropoutactivation_dropout	layerdroplayer_norm_epsinit_stdscale_embedding	use_cache)selfr!   r"   r
   r   r#   r	   r$   r%   r&   r'   r(   r)   r*   r+   r,   r   r   r   kwargs	__class__r   g/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/kosmos2/configuration_kosmos2.pyr    Y   s,   
zKosmos2TextConfig.__init__pretrained_model_name_or_pathreturnr   c                 K      |  | | j|fi |\}}|ddkr|d }d|v r:t| dr:|d | jkr:td|d  d| j d | j|fi |S )N
model_typekosmos-2text_configYou are using a model of type   to instantiate a model of type N. This is not supported for all configurations of models and can yield errors._set_token_in_kwargsget_config_dictgethasattrr5   loggerwarning	from_dictclsr2   r.   config_dictr   r   r1   from_pretrained      
 z!Kosmos2TextConfig.from_pretrained)r   r   r   r   r   r   r   r   r   r   r   r   r   TTr   r   r   )__name__
__module____qualname____doc__r5   keys_to_ignore_at_inferenceattribute_mapr    classmethodr   strosPathLikerF   __classcell__r   r   r/   r1   r   "   s:    .-&r   c                       s^   e Zd ZdZdZ									
				d fdd	Zedeee	j
f ddfddZ  ZS )Kosmos2VisionConfiga	  
    This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
    KOSMOS-2 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 KOSMOS-2
    [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 14):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float`, *optional*, defaults to 1):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
    ```kosmos_2_vision_model      r      r         
quick_gelur   r   r         ?c                    s^   t  jdi | || _|| _|| _|| _|| _|| _|| _|| _	|| _
|
| _|	| _|| _d S )Nr   )r   r    r   intermediate_sizer   r   num_channels
patch_size
image_sizeinitializer_rangeinitializer_factorr&   r)   
hidden_act)r-   r   r\   r   r   r]   r_   r^   rb   r)   r&   r`   ra   r.   r/   r   r1   r       s   
zKosmos2VisionConfig.__init__r2   r3   r   c                 K   r4   )Nr5   r6   vision_configr8   r9   r:   r;   rC   r   r   r1   rF      rG   z#Kosmos2VisionConfig.from_pretrained)rU   rV   r   rW   r   rX   rY   rZ   r   r   r   r[   )rH   rI   rJ   rK   r5   r    rN   r   rO   rP   rQ   rF   rR   r   r   r/   r1   rS      s$    &&rS   c                       s0   e Zd ZdZdZdZ			d fdd	Z  ZS )	Kosmos2Configat  
    This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a
    KOSMOS-2 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 KOSMOS-2
    [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
        latent_query_num (`int`, *optional*, defaults to 64):
            The number of latent query tokens that represent the image features used in the text decoder component.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import Kosmos2Config, Kosmos2Model

    >>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
    >>> configuration = Kosmos2Config()

    >>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
    >>> model = Kosmos2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```r6   TN@   c                    sh   t  jdi | |d u ri }td |d u ri }td tdi || _tdi || _|| _d S )NzR`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.zV`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.r   )	r   r    r@   infor   r7   rS   rc   latent_query_num)r-   r7   rc   rg   r.   r/   r   r1   r      s   


zKosmos2Config.__init__)NNre   )rH   rI   rJ   rK   r5   is_compositionr    rR   r   r   r/   r1   rd      s    rd   )rK   rP   typingr   configuration_utilsr   utilsr   
get_loggerrH   r@   %KOSMOS2_PRETRAINED_CONFIG_ARCHIVE_MAPr   rS   rd   r   r   r   r1   <module>   s   
w[