Fixed imports

Project_Model/Libs/TransformerUtils/ModelType.py

@@ -0,0 +1,6 @@
+from enum import Enum, auto
+
+class ModelType(Enum):
+
+    ENCODER_ONLY = auto()
+    DECODER_ONLY = auto()

Project_Model/Libs/TransformerUtils/__init__.py

@@ -0,0 +1,8 @@
+from .model_utils import decompose_nano_socrates, create_standalone_model
+from .ModelType import ModelType
+
+__all__ = [
+    "ModelType",
+    "decompose_nano_socrates",
+    "create_standalone_model"
+]

Project_Model/Libs/TransformerUtils/model_utils.py

@@ -0,0 +1,53 @@
+import torch
+from Project_Model.Libs.Embedder import NanoSocratesEmbedder
+from Project_Model.Libs.Transformer import TrainingModel, NanoSocraDecoder, NanoSocratEncoder, DeToken, Encoder, Decoder
+from .ModelType import ModelType
+
+
+
+def decompose_nano_socrates(
+    model: TrainingModel, vocabulary_size: int, embedding_size: int
+) -> tuple[TrainingModel, NanoSocratEncoder, NanoSocraDecoder]:
+
+    encoder_pieces, decoder_pieces = model.take_pieces()
+    encoder_embedder, encoder = encoder_pieces
+    encoder_detokener = DeToken(embedding_size, vocabulary_size)
+    decoder_embedder, decoder, decoder_detokener = decoder_pieces
+
+    return (
+        model,
+        NanoSocratEncoder(encoder_embedder, encoder, encoder_detokener),
+        NanoSocraDecoder(decoder_embedder, decoder, decoder_detokener),
+    )
+
+
+def create_standalone_model(
+    model_type: ModelType,
+    vocabulary_size: int,
+    latent_space: int = 256,
+    feed_forward_multiplier: int = 4,
+    attention_heads: int = 4,
+    layer_number: int = 2,
+) -> NanoSocratEncoder | NanoSocraDecoder:
+
+    feed_forward_latent_space = latent_space * feed_forward_multiplier
+
+    embedder = NanoSocratesEmbedder(vocabulary_size, latent_space)
+    detokener = DeToken(latent_space, vocabulary_size)
+
+    if model_type == ModelType.ENCODER_ONLY:
+        TMP_ENCODERS = [
+            Encoder(latent_space, feed_forward_latent_space, attention_heads)
+        ] * layer_number
+
+        encoder = torch.nn.Sequential(*TMP_ENCODERS)
+
+        return NanoSocratEncoder(embedder, encoder, detokener)
+
+    TMP_DECODERS = [
+        Decoder(latent_space, feed_forward_latent_space, attention_heads)
+    ] * layer_number
+
+    decoder = torch.nn.Sequential(*TMP_DECODERS)
+
+    return NanoSocraDecoder(embedder, decoder, detokener)