
     `i
<                         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dS )zPix2Struct model configuration   )PretrainedConfig)loggingc                   f     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_layersD     @                皙?ư>      ?gelu_new    F   Tc           	         || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _         t                      j        d|||||d| d S )N)pad_token_ideos_token_iddecoder_start_token_idtie_word_embeddings
is_decoder )
vocab_sizer	   d_kvd_ffr   r
   relative_attention_num_bucketsrelative_attention_max_distancedropout_ratelayer_norm_epsiloninitializer_factor	use_cacher!   r"   dense_act_fnsuper__init__)selfr&   r	   r'   r(   r   r
   r)   r*   r+   r,   r-   r/   r"   r.   r    r!   r#   r$   kwargs	__class__s                       /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/pix2struct/configuration_pix2struct.pyr1   zPix2StructTextConfig.__init__a   s    , %&		$".L+/N,("4"4"(&<# ) 	
%%#9 3!	
 	
 	
 	
 	
 	
 	
    )r   r   r   r   r   r   r   r   r   r   r   r   r   Fr   r   FT)	__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr1   __classcell__r4   s   @r5   r   r      s        : :x )J#4"5$*)#.#.&& M ')(+ !'0
 0
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r6   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_modelr   r   r   r   r   r           绽|=r      r   r   c                     t                      j        di | || _        || _        || _        |	| _        || _        || _        || _        || _	        |
| _
        || _        || _        || _        || _        || _        || _        d S )Nr%   )r0   r1   r	   patch_embed_hidden_sizer(   r+   r   r   initializer_ranger-   attention_dropoutlayer_norm_epsr/   seq_lenr)   r*   r'   )r2   r	   rG   r(   r'   r   r   r/   rJ   r+   rI   rH   r-   rK   r)   r*   r3   r4   s                    r5   r1   zPix2StructVisionConfig.__init__   s    & 	""6"""&'>$	(!2#6 !2"4!2,(.L+/N,			r6   )r   r   r   r   r   r   r   r   rC   rC   rD   r   rE   r   r   )r7   r8   r9   r:   r;   r1   r>   r?   s   @r5   rA   rA      s~        8 8t +J  #')(+!# # # # # # # # # #r6   rA   c                   @     e Zd ZdZdZeedZ	 	 	 	 	 	 	 d
 fd		Z xZ	S )Pix2StructConfiga1	  
    [`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.from_text_vision_configs(config_text, config_vision)
    ```
pix2struct)text_configvision_configNr   {Gz?FTc                     t                      j        d||d| |i }t                              d           |i }t                              d           ||d<   ||d<   t	          di || _        t          di || _        | j        j        | _        | j        j	        | _	        | j        j
        | _
        || _        || _        | j        | j        _        | j        | j        _        || _        d S )N)r#   is_encoder_decoderzOtext_config is None. Initializing the Pix2StructTextConfig with default values.zSvision_config is None. Initializing the Pix2StructVisionConfig with default values.rS   r#   r%   )r0   r1   loggerinfor   rO   rA   rP   r"   r    r!   r-   rH   is_vqa)
r2   rO   rP   r-   rH   rV   r#   rS   r3   r4   s
            r5   r1   zPix2StructConfig.__init__*  s    	r-@UgrrkqrrrKKKijjj MKKmnnn,>()-@)*/>>+>>3DDmDD&*&6&M# ,9 ,9"4!2-1-C*/3/E,r6   )NNr   rQ   FFT)
r7   r8   r9   r:   r;   r   rA   sub_configsr1   r>   r?   s   @r5   rM   rM      st        - -^ J"6I_``K !$ $ $ $ $ $ $ $ $ $r6   rM   )rM   r   rA   N)r:   configuration_utilsr   utilsr   
get_loggerr7   rT   r   rA   rM   __all__r%   r6   r5   <module>r\      s    % $ 3 3 3 3 3 3       
	H	%	%y
 y
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x` ` ` ` `- ` ` `FW W W W W' W W Wt Q
P
Pr6   