o
    hyQ                     @   s   d Z ddlZddlmZ ddlmZ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 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G dd deZdS )z GroupViT model configuration    NOrderedDict)TYPE_CHECKINGAnyMappingOptionalUnion   )PretrainedConfig)
OnnxConfig)logging)ProcessorMixin)
TensorTypeznvidia/groupvit-gcc-yfcczHhttps://huggingface.co/nvidia/groupvit-gcc-yfcc/resolve/main/config.jsonc                       sd   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 )GroupViTTextConfiga?  
    This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an
    GroupViT 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 GroupViT
    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) 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 49408):
            Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`GroupViTModel`].
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 4):
            Number of attention heads for each attention layer in the Transformer encoder.
        max_position_embeddings (`int`, *optional*, defaults to 77):
            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).
        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.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        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 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from transformers import GroupViTTextConfig, GroupViTTextModel

    >>> # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration
    >>> configuration = GroupViTTextConfig()

    >>> model = GroupViTTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```groupvit_text_model               M   
quick_geluh㈵>        {Gz?      ?       c                    sf   t  jd|||d| || _|| _|| _|	| _|| _|| _|| _|| _	|| _
|| _|| _|
| _d S )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizehidden_sizeintermediate_sizedropoutnum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps
hidden_actinitializer_rangeinitializer_factorattention_dropout)selfr%   r&   r'   r)   r*   r+   r-   r,   r(   r0   r.   r/   r   r    r!   kwargs	__class__r"   i/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/models/groupvit/configuration_groupvit.pyr$   ^   s   
zGroupViTTextConfig.__init__pretrained_model_name_or_pathreturnr
   c                 K      |  | | j|fi |\}}|ddkr|d }d|v r:t| dr:|d | jkr:td|d  d| j d | j|fi |S )N
model_typegroupvittext_configYou are using a model of type   to instantiate a model of type N. This is not supported for all configurations of models and can yield errors._set_token_in_kwargsget_config_dictgethasattrr9   loggerwarning	from_dictclsr6   r2   config_dictr"   r"   r5   from_pretrained      
 z"GroupViTTextConfig.from_pretrained)r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   __name__
__module____qualname____doc__r9   r$   classmethodr   strosPathLikerJ   __classcell__r"   r"   r3   r5   r   &   s*    5"&r   c                       sz   e Zd ZdZdZddg ddg dg dd	d
dddddddddddgf fdd	Zedeee	j
f ddfddZ  ZS )GroupViTVisionConfigaB  
    This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate
    an GroupViT 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 GroupViT
    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) 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 384):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 1536):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        depths (`List[int]`, *optional*, defaults to [6, 3, 3]):
            The number of layers in each encoder block.
        num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]):
            The number of group tokens for each stage.
        num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]):
            The number of output groups for each stage, 0 means no group.
        num_attention_heads (`int`, *optional*, defaults to 6):
            Number of attention heads for each attention layer in the Transformer encoder.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"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.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy 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 0.02):
            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).

    Example:

    ```python
    >>> from transformers import GroupViTVisionConfig, GroupViTVisionModel

    >>> # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration
    >>> configuration = GroupViTVisionConfig()

    >>> model = GroupViTVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```groupvit_vision_modeli  i   )   r	   r	   r   )@      r   )rY   rZ   rZ   rX         r	   gelur   r   r   r   g      ?r   c                    s   t  jdi | || _|| _|| _|t|kr%td| dt|  || _|| _	|| _
|| _|| _|	| _|
| _|| _|| _|| _|| _|| _|| _|| _|| _d S )Nz&Manually setting num_hidden_layers to z1, but we expect num_hidden_layers = sum(depth) = r"   )r#   r$   r&   r'   depthssumrD   rE   r)   num_group_tokensnum_output_groupsr*   
image_size
patch_sizenum_channelsr-   r,   r(   r0   r.   r/   
assign_epsassign_mlp_ratio)r1   r&   r'   r^   r)   r`   ra   r*   rb   rc   rd   r-   r,   r(   r0   r.   r/   re   rf   r2   r3   r"   r5   r$      s2   
zGroupViTVisionConfig.__init__r6   r7   r
   c                 K   r8   )Nr9   r:   vision_configr<   r=   r>   r?   rG   r"   r"   r5   rJ      rK   z$GroupViTVisionConfig.from_pretrainedrL   r"   r"   r3   r5   rV      s0    70&rV   c                       sF   e Zd ZdZdZ					d fdd	Zed	ed
efddZ	  Z
S )GroupViTConfiga  
    [`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to
    instantiate a GroupViT 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 GroupViT
    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) 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 [`GroupViTTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`GroupViTVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 256):
            Dimentionality of text and vision projection layers.
        projection_intermediate_dim (`int`, *optional*, defaults to 4096):
            Dimentionality of intermediate layer of text and vision projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The inital value of the *logit_scale* parameter. Default is used as per the original GroupViT
            implementation.
        kwargs (*optional*):
            Dictionary of keyword arguments.
    r:   Nr      /L
F@c                    s  | dd }| dd }t jdi | |d ur]|d u ri }tdi | }	|	 D ]+\}
}|
|v rW|||
 krW|
dvrW|
|v rLd|
 d|
 d}nd|
 d}t| q,||	 |d ur|d u rgi }t	di | }d	|v rd
d |d	  D |d	< | D ]+\}
}|
|v r|||
 kr|
dvr|
|v rd|
 d|
 d}nd|
 d}t| q|| |d u ri }t
d |d u ri }t
d tdi || _t	di || _|| _|| _|| _d| _d| _d| _d S )Ntext_config_dictvision_config_dict)transformers_version`zp` is found in both `text_config_dict` and `text_config` but with different values. The value `text_config_dict["z"]` will be used instead.zn`text_config_dict` is provided which will be used to initialize `GroupViTTextConfig`. The value `text_config["z"]` will be overriden.id2labelc                 S   s   i | ]	\}}t ||qS r"   )rR   ).0keyvaluer"   r"   r5   
<dictcomp>c  s    z+GroupViTConfig.__init__.<locals>.<dictcomp>zv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zt`vision_config_dict` is provided which will be used to initialize `GroupViTVisionConfig`. The value `vision_config["zS`text_config` is `None`. Initializing the `GroupViTTextConfig` with default values.zW`vision_config` is `None`. initializing the `GroupViTVisionConfig` with default values.r   r   Fr"   )popr#   r$   r   to_dictitemsrD   rE   updaterV   infor;   rg   projection_dimprojection_intermediate_dimlogit_scale_init_valuer.   r/   output_segmentation)r1   r;   rg   ry   rz   r{   r2   rk   rl   _text_config_dictrq   rr   message_vision_config_dictr3   r"   r5   r$   ,  sr   








zGroupViTConfig.__init__r;   rg   c                 K   s   | d|  |  d|S )z
        Instantiate a [`GroupViTConfig`] (or a derived class) from groupvit text model configuration and groupvit
        vision model configuration.

        Returns:
            [`GroupViTConfig`]: An instance of a configuration object
        )r;   rg   Nr"   )ru   )rH   r;   rg   r2   r"   r"   r5   from_text_vision_configs  s   
z'GroupViTConfig.from_text_vision_configs)NNr   ri   rj   )rM   rN   rO   rP   r9   r$   rQ   r   rV   r   rU   r"   r"   r3   r5   rh     s    arh   c                       s   e Zd Zedeeeeef f fddZedeeeeef f fddZede	fddZ
				dd
ddededed deeef f
 fddZedefddZ  ZS )GroupViTOnnxConfigr7   c                 C   s0   t ddddfdddddd	fd
dddfgS )N	input_idsbatchsequence)r   r   pixel_valuesrd   heightwidth)r   r      r	   attention_maskr   r1   r"   r"   r5   inputs  s   zGroupViTOnnxConfig.inputsc                 C   s0   t dddifdddifdddifdddifgS )Nlogits_per_imager   r   logits_per_texttext_embedsimage_embedsr   r   r"   r"   r5   outputs  s   



zGroupViTOnnxConfig.outputsc                 C      dS )Ng-C6?r"   r   r"   r"   r5   atol_for_validation     z&GroupViTOnnxConfig.atol_for_validationN	processorr   
batch_size
seq_length	frameworkr   c                    s6   t  j|j|||d}t  j|j||d}i ||S )N)r   r   r   )r   r   )r#   generate_dummy_inputs	tokenizerimage_processor)r1   r   r   r   r   text_input_dictimage_input_dictr3   r"   r5   r     s   
z(GroupViTOnnxConfig.generate_dummy_inputsc                 C   r   )N   r"   r   r"   r"   r5   default_onnx_opset  r   z%GroupViTOnnxConfig.default_onnx_opset)r   r   N)rM   rN   rO   propertyr   rR   intr   r   floatr   r   r   r   r   rU   r"   r"   r3   r5   r     s.     	 

r   )rP   rS   collectionsr   typingr   r   r   r   r   configuration_utilsr
   onnxr   utilsr   processing_utilsr   r   
get_loggerrM   rD   &GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAPr   rV   rh   r   r"   r"   r"   r5   <module>   s$   
m} 