g^dZddlmZddlmZejeZGddeZdS)z'Swin2SR Transformer model configuration)PretrainedConfig)loggingcfeZdZdZdZddddZddd d d gd gd d dddddddddddddffd ZxZS) Swin2SRConfiga This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin Transformer v2 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 Swin Transformer v2 [caidas/swin2sr-classicalsr-x2-64](https://huggingface.co/caidas/swin2sr-classicalsr-x2-64) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`int`, *optional*, defaults to 64): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 1): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_channels_out (`int`, *optional*, defaults to `num_channels`): The number of output channels. If not set, it will be set to `num_channels`. embed_dim (`int`, *optional*, defaults to 180): Dimensionality of patch embedding. depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): Number of attention heads in each layer of the Transformer encoder. window_size (`int`, *optional*, defaults to 8): Size of windows. mlp_ratio (`float`, *optional*, defaults to 2.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. use_absolute_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to add absolute position embeddings to the patch embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. upscale (`int`, *optional*, defaults to 2): The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact reduction img_range (`float`, *optional*, defaults to 1.0): The range of the values of the input image. resi_connection (`str`, *optional*, defaults to `"1conv"`): The convolutional block to use before the residual connection in each stage. upsampler (`str`, *optional*, defaults to `"pixelshuffle"`): The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None. Example: ```python >>> from transformers import Swin2SRConfig, Swin2SRModel >>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration >>> configuration = Swin2SRConfig() >>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration >>> model = Swin2SRModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```swin2sr embed_dim num_heads num_layers) hidden_sizenum_attention_headsnum_hidden_layers@rN)rrrrrg@Tgg?geluFg{Gz?gh㈵>g?1conv pixelshufflec tjdi|||_||_||_||n||_||_||_t||_ ||_ ||_ | |_ | |_ | |_| |_| |_||_||_||_||_||_||_||_||_dS)N)super__init__ image_size patch_size num_channelsnum_channels_outrdepthslenr r window_size mlp_ratioqkv_biashidden_dropout_probattention_probs_dropout_probdrop_path_rate hidden_actuse_absolute_embeddingslayer_norm_epsinitializer_rangeupscale img_rangeresi_connection upsampler)selfrrrrrrr r!r"r#r$r%r&r'r(r*r)r+r,r-r.kwargs __class__s m/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/swin2sr/configuration_swin2sr.pyrzSwin2SRConfig.__init__gs2 ""6"""$$(0@0H N^" f++"&"  #6 ,H),$'>$,!2 ".")__name__ __module__ __qualname____doc__ model_type attribute_mapr __classcell__)r1s@r2rrsD D LJ#*)M!!!$$$%( % -0#0#0#0#0#0#0#0#0#0#r3rN) r7configuration_utilsrutilsr get_loggerr4loggerrrr3r2r?s.-333333  H % %#####$#####r3