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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
```trocrpast_key_valuesdecoder_attention_headsd_modeldecoder_layers)num_attention_headshidden_sizenum_hidden_layersiY  
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is_decodertie_word_embeddings )&__name__
__module____qualname____firstlineno____doc__
model_typekeys_to_ignore_at_inferenceattribute_mapr   int__annotations__r   r   r   r   r   strr   r   floatr   r   r   r   r    r!   boolr"   r#   r$   r&   r'   r(   listr)   r*   r+   __static_attributes__r,       ~/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/trocr/configuration_trocr.pyr	   r	      sC   * J#4"58 -M JGSNC#%S%OS%%#&S&GUS[%(us{(&))"#C#He%(us{(It!OT!,0#T0 $$ L#*  L#* +,L#S	/D(,.2t2J $$r<   r	   N)	r1   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   r	   __all__r,   r<   r=   <module>rB      sG      . 3 # =>4%" 4%  ?4%n /r<   