
    qi=                         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Pix2Struct model configuration   )PreTrainedConfig)loggingc                   j     e Zd ZdZdZdgZddddddddZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d	 fd	Z xZS )
Pix2StructTextConfiga  
    This is the configuration class to store the configuration of a [`Pix2StructTextModel`]. It is used to instantiate
    a Pix2Struct text 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 Pix2Struct text decoder used by
    the [google/pix2struct-base](https://huggingface.co/google/pix2struct-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:
        vocab_size (`int`, *optional*, defaults to 50244):
            Vocabulary size of the `Pix2Struct` text model. Defines the number of different tokens that can be
            represented by the `inputs_ids` passed when calling [`Pix2StructTextModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        d_kv (`int`, *optional*, defaults to 64):
            Dimensionality of the key, query, value projections in each attention head.
        d_ff (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
            The number of buckets to use for each attention layer.
        relative_attention_max_distance (`int`, *optional*, defaults to 128):
            The maximum distance of the longer sequences for the bucket separation.
        dropout_rate (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the layer normalization layers.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
        dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`):
            The non-linear activation function (function or string).
        decoder_start_token_id (`int`, *optional*, defaults to 0):
            The id of the `decoder_start_token_id` token.
        use_cache (`bool`, *optional*, defaults to `False`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the `padding` token.
        eos_token_id (`int`, *optional*, defaults to 1):
            The id of the `end-of-sequence` token.

    Example:

    ```python
    >>> from transformers import Pix2StructTextConfig, Pix2StructTextModel

    >>> # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration
    >>> configuration = Pix2StructTextConfig()

    >>> # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration
    >>> model = Pix2StructTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```pix2struct_text_modelpast_key_valueshidden_size	num_heads
num_layers)r	   num_attention_headsnum_hidden_layersdecoder_attention_headsencoder_attention_headsencoder_layersdecoder_layersc                 X   || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        t)        | T  di | y N )
vocab_sizer	   d_kvd_ffr   r
   relative_attention_num_bucketsrelative_attention_max_distancedropout_ratelayer_norm_epsiloninitializer_factor	use_cacheeos_token_idbos_token_iddecoder_start_token_iddense_act_fnpad_token_idtie_word_embeddings
is_decoderadd_cross_attentionsuper__init__)selfr   r	   r   r   r   r
   r   r   r   r   r   r!   r    r   r"   r   r   r#   r$   r%   kwargs	__class__s                         i/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/pix2struct/configuration_pix2struct.pyr'   zPix2StructTextConfig.__init__`   s    0 %&		$".L+/N,("4"4"((&<# )((&<##6 $#6 "6"    )iD     @         r0          g?ư>      ?gelu_new    Fr6      NFTF)	__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr'   __classcell__r*   s   @r+   r   r      s}    :x )J#4"5$*)#.#.&&M ')(+ !!+2# 2#r,   r   c                   F     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )Pix2StructVisionConfiga  
    This is the configuration class to store the configuration of a [`Pix2StructVisionModel`]. It is used to
    instantiate a Pix2Struct vision model according to the specified arguments, defining the model architecture.
    Instantiating a configuration defaults will yield a similar configuration to that of the Pix2Struct-base
    [google/pix2struct-base](https://huggingface.co/google/pix2struct-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:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        patch_embed_hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the input patch_embedding layer in the Transformer encoder.
        d_ff (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        d_kv (`int`, *optional*, defaults to 64):
            Dimensionality of the key, query, value projections per attention head.
        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.
        dense_act_fn (`str` or `function`, *optional*, defaults to `"gelu_new"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        dropout_rate (`float`, *optional*, defaults to 0.0):
            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.
        initializer_range (`float`, *optional*, defaults to 1e-10):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
        seq_len (`int`, *optional*, defaults to 4096):
            Maximum sequence length (here number of patches) supported by the model.
        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
            The number of buckets to use for each attention layer.
        relative_attention_max_distance (`int`, *optional*, defaults to 128):
            The maximum distance (in tokens) to use for each attention layer.

    Example:

    ```python
    >>> from transformers import Pix2StructVisionConfig, Pix2StructVisionModel

    >>> # Initializing a Pix2StructVisionConfig with google/pix2struct-base style configuration
    >>> configuration = Pix2StructVisionConfig()

    >>> # Initializing a Pix2StructVisionModel (with random weights) from the google/pix2struct-base style configuration
    >>> model = Pix2StructVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```pix2struct_vision_modelc                     t        |   di | || _        || _        || _        |	| _        || _        || _        || _        || _	        |
| _
        || _        || _        || _        || _        || _        || _        y r   )r&   r'   r	   patch_embed_hidden_sizer   r   r   r   initializer_ranger   attention_dropoutlayer_norm_epsr!   seq_lenr   r   r   )r(   r	   rE   r   r   r   r   r!   rH   r   rG   rF   r   rI   r   r   r)   r*   s                    r+   r'   zPix2StructVisionConfig.__init__   s    & 	"6"&'>$	(!2#6 !2"4!2,(.L+/N,	r,   )r-   r-   r/   r.   r0   r0   r5   r3           rJ   g|=r4   i   r1   r2   )r8   r9   r:   r;   r<   r'   r?   r@   s   @r+   rB   rB      sI    8t +J  #')(+!# #r,   rB   c                   @     e Zd ZdZdZeedZ	 	 	 	 	 	 	 d fd	Z xZ	S )Pix2StructConfiga2	  
    [`Pix2StructConfig`] is the configuration class to store the configuration of a
    [`Pix2StructForConditionalGeneration`]. It is used to instantiate a Pix2Struct 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 Pix2Struct-base
    [google/pix2struct-base](https://huggingface.co/google/pix2struct-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 [`Pix2StructTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`Pix2StructVisionConfig`].
        initializer_factor (`float`, *optional*, defaults to 1.0):
            Factor to multiply the initialization range with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        is_vqa (`bool`, *optional*, defaults to `False`):
            Whether the model has been fine-tuned for VQA or not.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration

    >>> # Initializing a Pix2StructConfig with google/pix2struct-base style configuration
    >>> configuration = Pix2StructConfig()

    >>> # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration
    >>> model = Pix2StructForConditionalGeneration(configuration)

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

    >>> # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig

    >>> # Initializing a Pix2Struct text and Pix2Struct vision configuration
    >>> config_text = Pix2StructTextConfig()
    >>> config_vision = Pix2StructVisionConfig()

    >>> config = Pix2StructConfig(text_config=config_text, vision_config=config_vision)
    ```
pix2struct)text_configvision_configc                    |$t        ||d      }t        j                  d       n%t        |t              r||d<   ||d<   t        di |}| t               }t        j                  d       nt        |t              rt        di |}|| _        || _        | j                  j                  | _        | j                  j                  | _	        | j                  j                  | _
        || _        || _        | j                  | j                  _        | j                  | j                  _        || _        || _        t        	| @  dd|i| y )N)is_encoder_decoderr#   zU`text_config` is `None`. initializing the `Pix2StructTextConfig` with default values.rQ   r#   zY`vision_config` is `None`. initializing the `Pix2StructVisionConfig` with default values.r   )r   loggerinfo
isinstancedictrB   rN   rO   r    r"   r   r   rF   is_vqar#   r&   r'   )
r(   rN   rO   r   rF   rV   r#   rQ   r)   r*   s
            r+   r'   zPix2StructConfig.__init__+  s5    .'9RefK KKopT*0BK,-1DK-..==K 24MKKstt,2C]CM&*&*&6&6&M&M# ,,99 ,,99"4!2-1-C-C*/3/E/E,#6 I,>I&Ir,   )NNr4   g{Gz?FFT)
r8   r9   r:   r;   r<   r   rB   sub_configsr'   r?   r@   s   @r+   rL   rL      s>    -^ J"6I_`K !*J *Jr,   rL   )rL   r   rB   N)r;   configuration_utilsr   utilsr   
get_loggerr8   rR   r   rB   rL   __all__r   r,   r+   <module>r\      s^    % 3  
		H	%{#+ {#|`- `F]J' ]J@ Qr,   