
    sg"                         d Z ddlmZ ddlmZmZmZmZ ddlm	Z	 ddl
mZ ddlmZ erddlmZmZ  ej"                  e      Z G d	 d
e	      Z G d de      Zy)zOpenAI ImageGPT configuration    OrderedDict)TYPE_CHECKINGAnyMappingOptional   )PretrainedConfig)
OnnxConfig)logging)FeatureExtractionMixin
TensorTypec                   ^     e Zd ZdZdZdgZdddddZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d
 fd		Z xZS )ImageGPTConfigam  
    This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
    used to instantiate a GPT-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 ImageGPT
    [openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) 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 512):
            Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`].
        n_positions (`int`, *optional*, defaults to 32*32):
            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).
        n_embd (`int`, *optional*, defaults to 512):
            Dimensionality of the embeddings and hidden states.
        n_layer (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        n_head (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        n_inner (`int`, *optional*, defaults to None):
            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
        activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
            Activation function (can be one of the activation functions defined in src/transformers/activations.py).
            Defaults to "quick_gelu".
        resid_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (`int`, *optional*, defaults to 0.1):
            The dropout ratio for the embeddings.
        attn_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon to use in the layer normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        scale_attn_weights (`bool`, *optional*, defaults to `True`):
            Scale attention weights by dividing by sqrt(hidden_size)..
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
            Whether to additionally scale attention weights by `1 / layer_idx + 1`.
        reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
            Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
            dot-product/softmax to float() when training with mixed precision.

    Example:

    ```python
    >>> from transformers import ImageGPTConfig, ImageGPTModel

    >>> # Initializing a ImageGPT configuration
    >>> configuration = ImageGPTConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = ImageGPTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```imagegptpast_key_valuesn_embdn_positionsn_headn_layer)hidden_sizemax_position_embeddingsnum_attention_headsnum_hidden_layersc                    || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        t#        | H  dd|i| y )Ntie_word_embeddings )
vocab_sizer   r   r   r   n_inneractivation_functionresid_pdrop
embd_pdrop
attn_pdroplayer_norm_epsiloninitializer_rangescale_attn_weights	use_cachescale_attn_by_inverse_layer_idxreorder_and_upcast_attnr   super__init__)selfr   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r'   r   r(   r)   kwargs	__class__s                      f/var/www/html/venv/lib/python3.12/site-packages/transformers/models/imagegpt/configuration_imagegpt.pyr+   zImageGPTConfig.__init__h   s    * %&#6 &$$"4!2"4"/N,'>$#6 K-@KFK    )i  i   i         N
quick_gelu皙?r4   r4   gh㈵>g{Gz?TTFFF)	__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr+   __classcell__)r.   s   @r/   r   r      sm    =~ J#4"5#0'&	M (!(- %%'L 'Lr0   r   c                       e Zd Zedeeeeef   f   fd       Z	 	 	 	 	 	 	 ddddededed	e	d
   dedededeee
f   fdZy)ImageGPTOnnxConfigreturnc                 $    t        ddddfg      S )N	input_idsbatchsequence)r      r   )r,   s    r/   inputszImageGPTOnnxConfig.inputs   s!    'j9:
 	
r0   Npreprocessorr   
batch_size
seq_lengthis_pair	frameworkr   num_channelsimage_widthimage_heightc	                 T    | j                  ||||      }	t         ||	|            }
|
S )a  
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
                The preprocessor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            num_choices (`int`, *optional*, defaults to -1):
                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2)
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        )imagesreturn_tensors)_generate_dummy_imagesdict)r,   rF   rG   rH   rI   rJ   rK   rL   rM   input_imagerE   s              r/   generate_dummy_inputsz(ImageGPTOnnxConfig.generate_dummy_inputs   s2    L 11*lLZefl+iPQr0   )rD   FNr	       rV   )r5   r6   r7   propertyr   strintrE   boolr   r   rT   r   r0   r/   r>   r>      s    
WS#X%6 67 
 
 ,0).) ) 	)
 ) L)) ) ) ) 
c	)r0   r>   N)r8   collectionsr   typingr   r   r   r   configuration_utilsr
   onnxr   utilsr    r   r   
get_loggerr5   loggerr   r>   r   r0   r/   <module>rc      sT    $ # 8 8 3   6			H	%pL% pLf2 2r0   