Zj SrSSKJr SSKJr SSKJrJr \R"\ 5r \"SS9\"SS \555r \"SS9\"S S \555r \"SS9\"S S \555r /SQrg)zPix2Struct model configuration)strict)PreTrainedConfig)auto_docstringloggingzgoogle/pix2struct-base) checkpointct\rSrSr%SrSrS/rSSSSSSSS.rS r\ \ S 'S r \ \ S'S r \ \ S 'Sr \ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\\ -\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\ \ S'Sr\\ S 'Sr\ S!-\ S"'S#r\ \\ -S!-\ S$'S!r\ S!-\ S%'Sr\\ S&'S'r \\ S('Sr!\\ S)'S*r"g!)+Pix2StructTextConfigan relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string). Example: ```python >>> from transformers import Pix2StructTextConfig, Pix2StructTextModel >>> # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration >>> configuration = Pix2StructTextConfig() >>> # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration >>> model = Pix2StructTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```pix2struct_text_modelpast_key_values hidden_size num_heads num_layers)rnum_attention_headsnum_hidden_layersdecoder_attention_headsencoder_attention_headsencoder_layersdecoder_layersiD vocab_size@d_kvd_ff relative_attention_num_bucketsrelative_attention_max_distanceg? dropout_rateư>layer_norm_epsilon?initializer_factorgelu_new dense_act_fnrdecoder_start_token_idF use_cacheN pad_token_id eos_token_id bos_token_idtie_word_embeddingsT is_decoderadd_cross_attention)#__name__ __module__ __qualname____firstlineno____doc__ model_typekeys_to_ignore_at_inference attribute_maprint__annotations__rrrrrrr!r"floatr$r&r(strr)r*boolr+r-listr.r/r0r1__static_attributes__r2ڈ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/pix2struct/configuration_pix2struct.pyr r s .)J#4"5$*)#.#.&&MJKD#ND#JIs*,"C,+.#S. #L%#+# $$ ##"L#""#C#It L#* +,L#S /D(,#L#*# %%J %%rBr c\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S'S r \\S 'S r \\S 'S r \\S 'Sr\\S'Sr\\S'Sr\\-\S'Sr\\-\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Srg )!Pix2StructVisionConfigUa* 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. The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`): The non-linear activation function (function or string). 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_modelrrpatch_embed_hidden_sizerrrrrrrr'r(r#layer_norm_epsgr"attention_dropoutg|=initializer_ranger%r&iseq_lenrrr r!r2N)r3r4r5r6r7r8rr;r<rHrrrrr(r>rIr=r"rJrKr&rLrr!rAr2rBrCrErEUs B+JK#&S&D#D#Ns!!"L#" NE #L%#+#%(us{($u$ ##GS*,"C,+.#S.rBrEc^\rSrSr%SrSr\\S.rSr \ \ -S-\ S'Sr \ \ -S-\ S'Sr\\ S 'S r\\ S 'S r\\ S 'S r\\ S'Sr\\ S'U4SjrSrU=r$)Pix2StructConfiga is_vqa (`bool`, *optional*, defaults to `False`): Whether the model has been fine-tuned for VQA or not. Example: ```python >>> from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration >>> # Initializing a Pix2StructConfig with google/pix2struct-base style configuration >>> configuration = Pix2StructConfig() >>> # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration >>> model = Pix2StructForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig >>> # Initializing a Pix2Struct text and Pix2Struct vision configuration >>> config_text = Pix2StructTextConfig() >>> config_vision = Pix2StructVisionConfig() >>> config = Pix2StructConfig(text_config=config_text, vision_config=config_vision) ``` pix2struct) text_config vision_configNrQrRr%r&g{Gz?rKFis_vqar/Tis_encoder_decoderc l>URc9[URURS9Ul[R S5 Ok[ UR[5(aLURURS'URURS'[S0URD6UlURc%[5Ul[R S5 O9[ UR[5(a[S0URD6UlURRUl URRUl URRUl URURl URURl [TU]<"S0UD6 g)N)rTr/zU`text_config` is `None`. initializing the `Pix2StructTextConfig` with default values.rTr/zY`vision_config` is `None`. initializing the `Pix2StructVisionConfig` with default values.r2)rQr rTr/loggerinfo isinstancedictrRrEr)r+r-rKsuper __post_init__)selfkwargs __class__s rCr[Pix2StructConfig.__post_init__sU    #3#'#:#:$($<$< D  KKo p (($ / /595L5LD  1 26:6N6ND  2 33Gd6F6FGD     %!7!9D  KKs t **D 1 1!7!M$:L:L!MD &*&6&6&M&M# ,,99 ,,99-1-C-C*/3/E/E, ''rB)r)r-r+rQrR)r3r4r5r6r7r8r rE sub_configsrQrYrr<rRr&r=rKrSr?r/rTr[rA __classcell__)r^s@rCrNrNs 6J"6I_`K26K((4/648M4**T18 ###u#FD %%##((rBrN)rNr rEN)r7huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr3rVr rErN__all__r2rBrCrgs%.3,   H %347&+7&57&t342/-2/52/j34@('@(5@(F QrB