feat: add SODA dataset

* Very first prototype of SODA dataset support

I'm also bringing over the version of PromptConstants from the dev branch due to needing CHAT_START_TOKEN

* More flexibility when fetching speaker names

* Make SODA a PDM instead of a VDM

* Swap order of speakers based on relation

* Oh, and fix a typo too

* Bugfix

waifu/datasets/soda.py

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+import os
+import pickle
+import typing as t
+from dataclasses import dataclass
+
+import mashumaro
+import pandas as pd
+
+from waifu.datasets import BaseDataset
+from waifu.utils.dataset import get_data_path
+
+@dataclass(frozen=True)
+class SodaEpisode(mashumaro.DataClassDictMixin):
+    narrative: str
+    dialogue: t.List[str]
+    speakers: t.List[str]
+    relation: str
+    literal: str
+
+class SodaDataset(BaseDataset[SodaEpisode]):
+    '''
+    SODA: Million-scale Dialogue Distillation with Social Commonsense
+    Contextualization
+
+    https://huggingface.co/datasets/allenai/soda
+    '''
+
+    def generator(self) -> t.Generator[SodaEpisode, None, None]:
+        root_data_path = get_data_path("soda")
+        file_path = os.path.join(root_data_path, "test.parquet")
+        df = pd.read_parquet(file_path)
+
+        # Iterate through the test part of the SODA dataset
+        for i in df.index:
+            yield SodaEpisode(
+                narrative=df['narrative'][i],
+                dialogue=df['dialogue'][i],
+                speakers=df['speakers'][i],
+                relation=df['relation'][i],
+                literal=df['literal'][i]
+            )
+        
+        

waifu/modules/soda_pdm.py

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+import typing as t
+
+from waifu.core.consts import PromptConstants
+from waifu.datasets.soda import SodaDataset
+from waifu.modules import BaseModule
+
+
+class SodaPDM(BaseModule):
+    '''Persona Dialogue Module based on the SODA dataset.'''
+
+    def generator(self) -> t.Generator[list[str], None, None]:
+        for episode in SodaDataset():
+            episode_messages = []
+            # NOTE(TG): We determine which order the speakers go on based on whether the relation is xAttr or not.
+            # This is because some speakers are more abstract concepts rather than concrete names,
+            # which would make them much more suitable as a bot 
+            if episode.relation == "xAttr":
+                bot_name = episode.speakers[0]
+                user_name = episode.speakers[1]
+            else:
+                user_name = episode.speakers[0]
+                bot_name = episode.speakers[1]
+            
+            # First, we would want to set the persona.
+            # However, the only acceptable description of a persona would be when episode.relation is "xAttr", since that directly describes
+            # a person in the conversation.
+            if episode.relation == "xAttr":
+                episode_messages.append(f"{PromptConstants.pdm_prefix_for(bot_name)}: {episode.literal}")
+            
+            # Next, set the scenario.
+            # Make sure to replace any instance of the person representing the user in the conversation with the user token
+            replaced_narrative = episode.narrative.replace(user_name, PromptConstants.USER_TOKEN)
+            scenario = f"Scenario: {replaced_narrative}"
+            episode_messages.append(scenario)
+            # Next, the start token
+            episode_messages.append(PromptConstants.CHAT_START_TOKEN)
+            
+            # I am going to assume that the length of episode.speakers is the same as the length of episode.dialogue
+            # Looked pretty clean to me in the data. Fuck it, TODO: account for the possibility of that happening
+            for i, utterance in enumerate(episode.dialogue):
+                # For now, just leave bot's name unreplaced.
+                if episode.speakers[i] == user_name:
+                    name = PromptConstants.USER_PREFIX
+                else:
+                    name = bot_name
+                episode_messages.append(f"{name}: {utterance.replace(user_name, PromptConstants.USER_TOKEN)}")
+            
+            yield episode_messages