U 9%eCD@svdZddlZddlmZddlmZddlmZee Z ddiZ Gd d d eZ Gd d d eZ Gd ddeZdS)z Pix2Struct model configurationN)Union)PretrainedConfig)loggingzgoogle/pix2struct-textcaps-basezOhttps://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.jsoncsVeZdZdZdZdgZddddZdfdd Zee e e j fddddZ ZS)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 probabilitiy 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 ```Zpix2struct_text_modelZpast_key_values hidden_size num_heads num_layers)rnum_attention_headsnum_hidden_layersD@ 皙?ư>?gelu_newrFTc s|||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||_ | |_ | |_tjf||| ||d|dS)N) pad_token_id eos_token_iddecoder_start_token_idtie_word_embeddings is_decoder) vocab_sizerd_kvd_ffr rrelative_attention_num_bucketsrelative_attention_max_distance dropout_ratelayer_norm_epsiloninitializer_factor use_cacherr dense_act_fnsuper__init__)selfrrrrr rr r!r"r#r$r&rr%rrrrkwargs __class__v/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/pix2struct/configuration_pix2struct.pyr(es0zPix2StructTextConfig.__init__r!pretrainehidden_size_name_or_pathreturncKs~|||j|f|\}}|ddkr2|d}d|krpt|drp|d|jkrptd|dd|jd|j|f|S)N model_type pix2struct text_configYou are using a model of type to instantiate a model of type N. This is not supported for all configurations of models and can yield errors.Z_set_token_in_kwargsZget_config_dictgethasattrr2loggerwarning from_dictclsr0r*Z config_dictr-r-r.from_pretraineds  z$Pix2StructTextConfig.from_pretrained)r r rrrrrrrrrrrFrrFT)__name__ __module__ __qualname____doc__r2Zkeys_to_ignore_at_inferenceZ attribute_mapr( classmethodrstrosPathLiker@ __classcell__r-r-r+r.r!s<;2 rcsDeZdZdZdZdfdd Zeeee j fddddZ 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-6): The epsilon used by the layer normalization layers. dropout_rate (`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 1e-10): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float``, *optional*, defaults to 1): 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 ```Zpix2struct_vision_modelr rrrrr绽|=rrrc sltjf|||_||_||_| |_||_||_| |_| |_ | |_ ||_ ||_ | |_ ||_||_||_dS)N)r'r(rpatch_embed_hidden_sizerr"r r initializer_ranger$attention_dropoutlayer_norm_epsr&seq_lenr r!r)r)rrNrrr r r&rQr"rPrOr$rRr r!r*r+r-r.r(s zPix2StructVisionConfig.__init__rr/cKs~|||j|f|\}}|ddkr2|d}d|krpt|drp|d|jkrptd|dd|jd|j|f|S)Nr2r3 vision_configr5r6r7r8r>r-r-r.r@s  z&Pix2StructVisionConfig.from_pretrained)r r rrrrrrrKrKrLrrMrr) rArBrCrDr2r(rErrFrGrHr@rIr-r-r+r.rJs,:% rJcs:eZdZdZdZd fdd Zeeed d d Z 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) ```r3Nr{Gz?FTc  stjf||d||dkr.i}td|dkrDi}tdtf||_tf||_|jj|_|jj |_ |jj |_ ||_ ||_ |j |j_ |j |j_ ||_ dS)N)ris_encoder_decoderzOtext_config is None. Initializing the Pix2StructTextConfig with default values.zSvision_config is None. Initializing the Pix2StructVisionConfig with default values.)r'r(r;inforr4rJrSrrrr$rOis_vqa) r)r4rSr$rOrXrrVr*r+r-r.r(Us"          zPix2StructConfig.__init__r4rScKs|f||d|S)z Instantiate a [`Pix2StructConfig`] (or a derived class) from pix2struct text model configuration and pix2struct vision model configuration. Returns: [`Pix2StructConfig`]: An instance of a configuration object rY)to_dict)r?r4rSr*r-r-r.from_text_vision_configsys z)Pix2StructConfig.from_text_vision_configs)NNrrUFFT) rArBrCrDr2r(rErrJr[rIr-r-r+r.rT#s/$rT)rDrGtypingrZconfiguration_utilsrutilsrZ get_loggerrAr;Z(PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAPrrJrTr-r-r-r.s     w