sgDdZddlZddlmZddlmZddlmZeje Z GddeZ Gd d eZ Gd d eZ y) zPix2Struct model configurationN)Union)PretrainedConfig)loggingceZdZdZdZdgZddddddddZ d fd Zed e e e jfd d fd Z xZS)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_modelpast_key_values hidden_size num_heads num_layers)r num_attention_headsnum_hidden_layersdecoder_attention_headsencoder_attention_headsencoder_layersdecoder_layersc ||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||_ | |_ | |_t|@d||| ||d|y)N) pad_token_id eos_token_iddecoder_start_token_idtie_word_embeddings is_decoder) vocab_sizer d_kvd_ffr r relative_attention_num_bucketsrelative_attention_max_distance dropout_ratelayer_norm_epsiloninitializer_factor use_cacherr dense_act_fnsuper__init__)selfrr rrr r rrr r!r"r$rr#rrrrkwargs __class__s j/var/www/html/venv/lib/python3.12/site-packages/transformers/models/pix2struct/configuration_pix2struct.pyr&zPix2StructTextConfig.__init__ds,%&  $".L+/N,("4"4"(&<#)  %%#9 3!    !pretrainehidden_size_name_or_pathreturnrc >|j||j|fi|\}}|jddk(r|d}d|vrGt|dr;|d|jk7r)t j d|dd|jd|j|fi|S)N model_type pix2struct text_configYou are using a model of type to instantiate a model of type N. This is not supported for all configurations of models and can yield errors._set_token_in_kwargsget_config_dictgethasattrr/loggerwarning from_dictclsr,r( config_dicts r*from_pretrainedz$Pix2StructTextConfig.from_pretraineds   (1c112S^W]^ V ??< (L 8%m4K ; &73 +E+VbJcgjguguJu NN0\1J0KKk>>""pr  s}}[3F33r+)iD@ rD g?ư>?gelu_newrFrFT)__name__ __module__ __qualname____doc__r/keys_to_ignore_at_inference attribute_mapr& classmethodrstrosPathLiker@ __classcell__r)s@r*rrs: x)J#4"5$*)#.#.&&M  ')(+ !'0 d405c2;;6F0G4 44r+rc~eZdZdZdZ dfd Zedeee jfddfdZ xZ 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_modelc t|di|||_||_||_| |_||_||_| |_| |_ | |_ ||_ ||_ | |_ ||_||_||_y)Nr)r%r&r patch_embed_hidden_sizerr rrinitializer_ranger"attention_dropoutlayer_norm_epsr$seq_lenrrr)r'r r[rrrrr$r^r r]r\r"r_rrr(r)s r*r&zPix2StructVisionConfig.__init__s& "6"&'>$ (!2#6 !2"4!2,( .L+/N, r+r,r-rc >|j||j|fi|\}}|jddk(r|d}d|vrGt|dr;|d|jk7r)t j d|dd|jd|j|fi|S)Nr/r0 vision_configr2r3r4r5r=s r*r@z&Pix2StructVisionConfig.from_pretrained s   (1c112S^W]^ V ??< (L 8%o6K ; &73 +E+VbJcgjguguJu NN0\1J0KKk>>""pr  s}}[3F33r+)rArArCrBrDrDrIrGrbg|=rHirErF) rKrLrMrNr/r&rQrrRrSrTr@rUrVs@r*rXrXsw8 t+J #  ')(+!#J405c2;;6F0G4 44r+rXcPeZdZdZdZ dfd ZededefdZ xZ 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) ```r0c t |d||d||i}tjd|i}tjd||d<||d<t di||_t di||_|j j|_|j j|_ |j j|_ ||_ ||_ |j|j _ |j|j_ ||_ y)N)ris_encoder_decoderzOtext_config is None. Initializing the Pix2StructTextConfig with default values.zSvision_config is None. Initializing the Pix2StructVisionConfig with default values.rfrr)r%r&r:inforr1rXrarrrr"r\is_vqa) r'r1rar"r\rhrrfr(r)s r*r&zPix2StructConfig.__init__Ts r-@Ugrkqr  K KKi j  M KKm n,> ()-@ )*/>+>3DmD&*&6&6&M&M# ,,99 ,,99"4!2-1-C-C*/3/E/E, r+r1rac P|d|j|jd|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 )r1rar)to_dict)r>r1rar(s r*from_text_vision_configsz)Pix2StructConfig.from_text_vision_configszs,f{224MDYDYD[f_effr+)NNrHg{Gz?FFT) rKrLrMrNr/r&rQrrXrkrUrVs@r*rdrd"sU- ^J!$L g. g?U g gr+rd)rNrStypingrconfiguration_utilsrutilsr get_loggerrKr:rrXrdrr+r*rps[% 3   H %M4+M4`t4-t4ndg'dgr+