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  ã                   ój   — d Z ddlmZ ddlmZ ddlmZ  ed¬«      e G d„ d	e«      «       «       Zd	gZy
)zTrOCR model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringz microsoft/trocr-base-handwritten)Ú
checkpointc                   ó¦  — e Zd ZU dZdZdgZddddœZdZee	d	<   d
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ee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZeez  e	d<   dZeez  e	d<   dZeez  e	d<   dZee	d<   dZee	d<   dZeez  e	d<   dZee	d<   dZee	d <   dZee	d!<   dZee	d"<   d#Zed$z  e	d%<   d&Zed$z  e	d'<   dZee e   z  d$z  e	d(<   d$Z!ed$z  e	d)<   dZ"ee	d*<   dZ#ee	d+<   y$),ÚTrOCRConfigaó  
    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
    ```ÚtrocrÚpast_key_valuesÚdecoder_attention_headsÚd_modelÚdecoder_layers)Únum_attention_headsÚhidden_sizeÚnum_hidden_layersiYÄ  Ú
vocab_sizei   é   é   i   Údecoder_ffn_dimÚgeluÚactivation_functioni   Úmax_position_embeddingsgš™™™™™¹?Údropoutg        Úattention_dropoutÚactivation_dropouté   Údecoder_start_token_idg{®Gáz”?Úinit_stdÚdecoder_layerdropTÚ	use_cacheFÚscale_embeddingÚuse_learned_position_embeddingsÚlayernorm_embeddingé   NÚpad_token_idr   Úbos_token_idÚeos_token_idÚcross_attention_hidden_sizeÚ
is_decoderÚtie_word_embeddings)$Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚattribute_mapr   ÚintÚ__annotations__r   r   r   r   r   Ústrr   r   Úfloatr   r   r   r   r   r    Úboolr!   r"   r#   r%   r&   r'   Úlistr(   r)   r*   © ó    úx/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/trocr/configuration_trocr.pyr	   r	      sC  … ñð* €JØ#4Ð"5Ðà8Ø Ø-ñ€Mð €J�ÓØ€GˆSÓØ€N�CÓØ#%Ð˜SÓ%Ø€O�SÓØ%Ð˜Ó%Ø#&Ð˜SÓ&Ø€GˆU�S‰[ÓØ%(Ð�u˜s‘{Ó(Ø&)Ð˜ ™Ó)Ø"#Ð˜CÓ#Ø€HˆeÓØ%(Ð�u˜s‘{Ó(Ø€IˆtÓØ!€O�TÓ!Ø,0Ð# TÓ0Ø $Ð˜Ó$Ø €L�#˜‘*Ó Ø €L�#˜‘*Ó Ø+,€L�#˜˜S™	‘/ DÑ(Ó,Ø.2Ð  t¡Ó2Ø€J�ÓØ $Ð˜Ô$r9   r	   N)	r.   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r8   r9   r:   ú<module>r?      sG   ðñ  å .å 3Ý #ñ Ð=Ô>Øô4%Ð"ó 4%ó ó ?ð4%ðn ˆ/�r9   