U ,È-ezã@sBdZddlmZddlmZe e¡ZddiZGdd„deƒZ dS) z TrOCR model configurationé)ÚPretrainedConfig)Úloggingz microsoft/trocr-base-handwrittenzPhttps://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.jsoncs8eZdZdZdZdgZddddœZd‡fdd„ Z‡ZS)Ú TrOCRConfigaÆ This is the configuration class to store the configuration of a [`TrOCRForCausalLM`]. It is used to instantiate an TrOCR 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 TrOCR [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) 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 50265): Vocabulary size of the TrOCR model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`TrOCRForCausalLM`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. max_position_embeddings (`int`, *optional*, defaults to 512): 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). dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). scale_embedding (`bool`, *optional*, defaults to `False`): Whether or not to scale the word embeddings by sqrt(d_model). use_learned_position_embeddings (`bool`, *optional*, defaults to `True`): Whether or not to use learned position embeddings. If not, sinusoidal position embeddings will be used. layernorm_embedding (`bool`, *optional*, defaults to `True`): Whether or not to use a layernorm after the word + position embeddings. Example: ```python >>> from transformers import TrOCRConfig, TrOCRForCausalLM >>> # Initializing a TrOCR-base style configuration >>> configuration = TrOCRConfig() >>> # Initializing a model (with random weights) from the TrOCR-base style configuration >>> model = TrOCRForCausalLM(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZtrocrZpast_key_valuesÚdecoder_attention_headsÚd_modelÚdecoder_layers)Znum_attention_headsZ hidden_sizeZnum_hidden_layerséYÄéé ééÚgeluéçš™™™™™¹?çéç{®Gáz”?TFééc s€||_||_||_||_||_||_||_||_| |_| |_ | |_ | |_ ||_ ||_ ||_||_tƒjf|||| dœ|—ŽdS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_idÚdecoder_start_token_id)Ú vocab_sizerrrÚdecoder_ffn_dimÚactivation_functionÚmax_position_embeddingsÚdropoutÚattention_dropoutÚactivation_dropoutÚinit_stdÚdecoder_layerdropÚ use_cacheÚscale_embeddingÚuse_learned_position_embeddingsÚlayernorm_embeddingÚsuperÚ__init__)Úselfrrrrrrrrrrrr r!r"r#r$r%rrrÚkwargs©Ú __class__©ún/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/trocr/configuration_trocr.pyr'fs0üûzTrOCRConfig.__init__)rr r r r r rrrrrrrTFTTrrr) Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferenceZ attribute_mapr'Ú __classcell__r,r,r*r-rs8>ýërN) r1Zconfiguration_utilsrÚutilsrZ get_loggerr.ÚloggerZ#TROCR_PRETRAINED_CONFIG_ARCHIVE_MAPrr,r,r,r-Ús   þ