
    gQ                         d Z ddlZddlm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dS )zCLIPSeg model configuration    N)Union   )PretrainedConfig)loggingc                   ~     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Zedeee	j
        f         ddfd            Z xZS )CLIPSegTextConfiga  
    This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
    CLIPSeg 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 CLIPSeg
    [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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 CLIPSeg text model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`CLIPSegModel`].
        hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            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 8):
            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-05):
            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 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
        pad_token_id (`int`, *optional*, defaults to 1):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 49406):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 49407):
            End of stream token id.

    Example:

    ```python
    >>> from transformers import CLIPSegTextConfig, CLIPSegTextModel

    >>> # Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration
    >>> configuration = CLIPSegTextConfig()

    >>> # Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
    >>> model = CLIPSegTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clipseg_text_model               M   
quick_geluh㈵>        {Gz?      ?       c                      t                      j        d|||d| || _        || _        || _        || _        || _        || _        || _        || _	        |
| _
        || _        |	| _        d S )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizehidden_sizeintermediate_size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$   r&   r%   r)   r'   r(   r   r   r   kwargs	__class__s                   m/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/clipseg/configuration_clipseg.pyr   zCLIPSegTextConfig.__init__X   s    $ 	sl\hsslrsss$&!2!2#6 '>$,$!2"4!2    pretrained_model_name_or_pathreturnr   c                 N   |                      |            | j        |fi |\  }}|                    d          dk    r|d         }d|v rMt          | d          r=|d         | j        k    r,t
                              d|d          d| j         d            | j        |fi |S )N
model_typeclipseg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hasattrr2   loggerwarning	from_dictclsr/   r+   config_dicts       r-   from_pretrainedz!CLIPSegTextConfig.from_pretrainedx   s      (((1c12OZZSYZZV ??<((I55%m4K;&&73+E+E&+VbJcgjguJuJuNNr\1J r r>r r r  
 s}[33F333r.   )r
   r   r   r   r   r   r   r   r   r   r   r   r   r   __name__
__module____qualname____doc__r2   r   classmethodr   strosPathLikerC   __classcell__r,   s   @r-   r   r      s        8 8t &J  "3 3 3 3 3 3@ 4E#r{BR<S 4bt 4 4 4 [4 4 4 4 4r.   r   c                   z     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 d fd	Zedeee	j
        f         ddfd            Z xZS )CLIPSegVisionConfigaG  
    This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
    CLIPSeg 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 CLIPSeg
    [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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.
        intermediate_size (`int`, *optional*, defaults to 3072):
            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 12):
            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 32):
            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-05):
            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 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from transformers import CLIPSegVisionConfig, CLIPSegVisionModel

    >>> # Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration
    >>> configuration = CLIPSegVisionConfig()

    >>> # Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
    >>> model = CLIPSegVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clipseg_vision_model      r   r          r   r   r   r   r   c                      t                      j        di | || _        || _        || _        || _        || _        || _        || _        || _	        || _
        |
| _        |	| _        || _        d S )Nr   )r   r   r    r!   r"   r#   num_channels
patch_size
image_sizer'   r(   r)   r%   r&   )r*   r    r!   r"   r#   rW   rY   rX   r&   r%   r)   r'   r(   r+   r,   s                 r-   r   zCLIPSegVisionConfig.__init__   s      	""6"""&!2!2#6 ($$!2"4!2,$r.   r/   r0   r   c                 N   |                      |            | j        |fi |\  }}|                    d          dk    r|d         }d|v rMt          | d          r=|d         | j        k    r,t
                              d|d          d| j         d            | j        |fi |S )Nr2   r3   vision_configr5   r6   r7   r8   r@   s       r-   rC   z#CLIPSegVisionConfig.from_pretrained   s      (((1c12OZZSYZZV ??<((I55%o6K;&&73+E+E&+VbJcgjguJuJuNNr\1J r r>r r r  
 s}[33F333r.   )rR   rS   r   r   r   rT   rU   r   r   r   r   r   rD   rN   s   @r-   rP   rP      s        2 2h (J % % % % % %> 4E#r{BR<S 4bt 4 4 4 [4 4 4 4 4r.   rP   c                   d     e Zd ZdZdZddddg dddd	d
dddf fd	Zededefd            Z	 xZ
S )CLIPSegConfiga  
    [`CLIPSegConfig`] is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to
    instantiate a CLIPSeg 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 CLIPSeg
    [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64) 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 [`CLIPSegTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`CLIPSegVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The initial value of the *logit_scale* parameter. Default is used as per the original CLIPSeg implementation.
        extract_layers (`List[int]`, *optional*, defaults to `[3, 6, 9]`):
            Layers to extract when forwarding the query image through the frozen visual backbone of CLIP.
        reduce_dim (`int`, *optional*, defaults to 64):
            Dimensionality to reduce the CLIP vision embedding.
        decoder_num_attention_heads (`int`, *optional*, defaults to 4):
            Number of attention heads in the decoder of CLIPSeg.
        decoder_attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        decoder_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.
        decoder_intermediate_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layers in the Transformer decoder.
        conditional_layer (`int`, *optional*, defaults to 0):
            The layer to use of the Transformer encoder whose activations will be combined with the condition
            embeddings using FiLM (Feature-wise Linear Modulation). If 0, the last layer is used.
        use_complex_transposed_convolution (`bool`, *optional*, defaults to `False`):
            Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained
            segmentation.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import CLIPSegConfig, CLIPSegModel

    >>> # Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration
    >>> configuration = CLIPSegConfig()

    >>> # Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
    >>> model = CLIPSegModel(configuration)

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

    >>> # We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig

    >>> # Initializing a CLIPSegText and CLIPSegVision configuration
    >>> config_text = CLIPSegTextConfig()
    >>> config_vision = CLIPSegVisionConfig()

    >>> config = CLIPSegConfig.from_text_vision_configs(config_text, config_vision)
    ```r3   Nr   g/L
F@)r      	   @      r   r   r   r   Fc                    |                     dd           }|                     dd           } t                      j        di | ||i }t          di |                                }|                                D ]G\  }}||v r>|||         k    r2|dvr.||v r
d| d| d}nd| d}t                              |           H|                    |           ||i }t          di |                                }d	|v r'd
 |d	                                         D             |d	<   |                                D ]G\  }}||v r>|||         k    r2|dvr.||v r
d| d| d}nd| d}t                              |           H|                    |           |i }t                              d           |i }t                              d           t          di || _
        t          di || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _        || _        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.zm`text_config_dict` is provided which will be used to initialize `CLIPSegTextConfig`. The value `text_config["z"]` will be overridden.id2labelc                 4    i | ]\  }}t          |          |S r   )rJ   ).0keyvalues      r-   
<dictcomp>z*CLIPSegConfig.__init__.<locals>.<dictcomp>t  s1     3 3 3(2UCHHe3 3 3r.   zv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zs`vision_config_dict` is provided which will be used to initialize `CLIPSegVisionConfig`. The value `vision_config["zR`text_config` is `None`. Initializing the `CLIPSegTextConfig` with default values.zV`vision_config` is `None`. initializing the `CLIPSegVisionConfig` with default values.r   r   )popr   r   r   to_dictitemsr=   infoupdaterP   r4   r[   projection_dimlogit_scale_init_valueextract_layers
reduce_dimdecoder_num_attention_headsdecoder_attention_dropoutdecoder_hidden_actdecoder_intermediate_sizeconditional_layerr(   "use_complex_transposed_convolution)r*   r4   r[   rr   rs   rt   ru   rv   rw   rx   ry   rz   r{   r+   rc   rd   _text_config_dictrj   rk   message_vision_config_dictr,   s                        r-   r   zCLIPSegConfig.__init__6  sX   & "::&8$??#ZZ(<dCC""6"""
 '"  !2 E E4D E E M M O O 05577 ) )
U+%%%;s3C*C*CSkHkHk...[ [ [<?[ [ [  P36P P P   KK((( 0111)$ " #6"K"K8J"K"K"S"S"U"U0003 36I*6U6[6[6]6]3 3 3#J/
 27799 ) )
U-''E]35G,G,GCWoLoLo000e e eFIe e e  V9<V V V   KK(((   !4555KKKlmmm MKKpqqq,;;{;;0AA=AA,&<#,$+F()B&"4)B&!2"%2T///r.   r4   r[   c                 `     | d|                                 |                                 d|S )z
        Instantiate a [`CLIPSegConfig`] (or a derived class) from clipseg text model configuration and clipseg vision
        model configuration.

        Returns:
            [`CLIPSegConfig`]: An instance of a configuration object
        )r4   r[   r   )rn   )rA   r4   r[   r+   s       r-   from_text_vision_configsz&CLIPSegConfig.from_text_vision_configs  s:     sf{2244MDYDYD[D[ff_efffr.   )rE   rF   rG   rH   r2   r   rI   r   rP   r   rM   rN   s   @r-   r]   r]      s        = =~ J % yy$%"%'"&+0kU kU kU kU kU kUZ 	g3D 	gUh 	g 	g 	g [	g 	g 	g 	g 	gr.   r]   )rH   rK   typingr   configuration_utilsr   utilsr   
get_loggerrE   r=   r   rP   r]   r   r.   r-   <module>r      s   " ! 				       3 3 3 3 3 3       
	H	%	%m4 m4 m4 m4 m4( m4 m4 m4`f4 f4 f4 f4 f4* f4 f4 f4Ryg yg yg yg yg$ yg yg yg yg ygr.   