Added a way to detach models and create them standalone

Project_Model/Libs/Transformer/Models/NanoSocraDecoder.py

@@ -0,0 +1,32 @@
+import torch
+import Project_Model.Libs.Embedder as Embedder
+from ..Classes import DeToken
+
+class NanoSocraDecoder(torch.nn.Module):
+
+    def __init__(
+        self,
+        decoder_embedder: Embedder.NanoSocratesEmbedder,
+        decoder_layers: torch.nn.Sequential,
+        detokener: DeToken
+    ) -> None:
+        super().__init__()
+
+
+        self.__decoder_embedder = decoder_embedder
+        self.__decoder = decoder_layers
+        self.__detokener = detokener
+
+    def forward(self, args: tuple[torch.Tensor, torch.Tensor]):
+
+        decoder_embedder_input, tgt_padding = args
+
+        decoder_tensor = self.__decoder_embedder(decoder_embedder_input)
+
+        decoder_output, _, _, _, _, _ = self.__decoder(
+            (decoder_tensor, decoder_tensor, decoder_tensor, tgt_padding, tgt_padding, False)
+        )
+
+        logits: torch.Tensor = self.__detokener(decoder_output)
+
+        return logits

Project_Model/Libs/Transformer/Models/NanoSocratEncoder.py

@@ -0,0 +1,29 @@
+import torch
+import Project_Model.Libs.Embedder as Embedder
+from ..Classes import DeToken
+
+class NanoSocratEncoder(torch.nn.Module):
+
+    def __init__(
+        self,
+        encoder_embedder: Embedder.NanoSocratesEmbedder,
+        encoder_layers: torch.nn.Sequential,
+        detokener: DeToken
+    ) -> None:
+        super().__init__()
+
+        self.__encoder_embedder = encoder_embedder
+        self.__encoder = encoder_layers
+        self.__detokener = detokener
+
+    def forward(self,  args: tuple[torch.Tensor, torch.Tensor]):
+
+        encoder_embedder_input, src_padding = args
+
+        encoder_tensor = self.__encoder_embedder(encoder_embedder_input)
+
+        encoder_output, _ = self.__encoder((encoder_tensor, src_padding))
+
+        logits: torch.Tensor = self.__detokener(encoder_output)
+
+        return logits

Project_Model/Libs/Transformer/Models/TrainingModel.py

@@ -46,10 +46,17 @@ class TrainingModel(torch.nn.Module):
 
         encoder_output, _ = self.__encoder((encoder_tensor, src_padding))
 
-        decoder_output, _, _, _, _ = self.__decoder(
-            (decoder_tensor, encoder_output, encoder_output, src_padding, tgt_padding)
+        decoder_output, _, _, _, _, _ = self.__decoder(
+            (decoder_tensor, encoder_output, encoder_output, src_padding, tgt_padding, False)
         )
 
         logits: torch.Tensor = self.__detokener(decoder_output)
 
         return logits
+
+    def take_pieces(self):
+
+        return (
+            (self.__encoder_embedder, self.__encoder),
+            (self.__decoder_embedder, self.__decoder, self.__detokener)
+        )

Project_Model/Libs/Transformer/Models/__init__.py

@@ -1,5 +1,9 @@
 from .TrainingModel import TrainingModel
+from .NanoSocratEncoder import NanoSocratEncoder
+from .NanoSocraDecoder import NanoSocraDecoder
 
 __all__ = [
-    "TrainingModel"
+    "TrainingModel",
+    "NanoSocratEncoder",
+    "NanoSocraDecoder"
 ]