+
    œ°³jž  ã                   óz   € R t ^ RIHt ^RIHt ^RIHt ^RIHt ]! RR7      ] ! R R]]4      4       4       t	R.t
R	# )
zFocalNet model configuration)Ústrict)ÚBackboneConfigMixin)ÚPreTrainedConfig)Úauto_docstringzmicrosoft/focalnet-tiny)Ú
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RtRtRtRtRtRtRtRtRtRtRtRtRtR	tR
t^ tRtRtV 3R ltV3R ltRtVt V ;t!# )ÚFocalNetConfiga  
use_conv_embed (`bool`, *optional*, defaults to `False`):
    Whether to use convolutional embedding. The authors noted that using convolutional embedding usually
    improve the performance, but it's not used by default.
focal_levels (`list(int)`, *optional*, defaults to `[2, 2, 2, 2]`):
    Number of focal levels in each layer of the respective stages in the encoder.
focal_windows (`list(int)`, *optional*, defaults to `[3, 3, 3, 3]`):
    Focal window size in each layer of the respective stages in the encoder.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
    The dropout probability for all fully connected layers in the embeddings and encoder.
use_layerscale (`bool`, *optional*, defaults to `False`):
    Whether to use layer scale in the encoder.
layerscale_value (`float`, *optional*, defaults to 0.0001):
    The initial value of the layer scale.
use_post_layernorm (`bool`, *optional*, defaults to `False`):
    Whether to use post layer normalization in the encoder.
use_post_layernorm_in_modulation (`bool`, *optional*, defaults to `False`):
    Whether to use post layer normalization in the modulation layer.
normalize_modulator (`bool`, *optional*, defaults to `False`):
    Whether to normalize the modulator.
encoder_stride (`int`, *optional*, defaults to 32):
    Factor to increase the spatial resolution by in the decoder head for masked image modeling.

Example:

```python
>>> from transformers import FocalNetConfig, FocalNetModel

>>> # Initializing a FocalNet microsoft/focalnet-tiny style configuration
>>> configuration = FocalNetConfig()

>>> # Initializing a model (with random weights) from the microsoft/focalnet-tiny style configuration
>>> model = FocalNetModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
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  	  up,           V n        V P	                  VP                  RR4      VP                  RR4      R7       \        SV `  ! R/ VB  R# u upi )ÚstemÚstageÚout_indicesNÚout_features)r   r   © )ÚrangeÚlenÚdepthsÚstage_namesÚ"set_output_features_output_indicesÚpopÚsuperÚ__post_init__)ÚselfÚkwargsÚidxÚ	__class__s   &, €Út/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/focalnet/configuration_focalnet.pyr   ÚFocalNetConfig.__post_init__Z   s‡   ø€ Ø"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÕI]Ô@^Ó&_Ñ@^¸¨¨s¨e£}Ñ@^Ñ&_Õ_ˆÔØ×/Ñ/ØŸ
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image_sizeÚ
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