# from texture.models import DatasetInfo

# dataset_vis_papers = DatasetInfo(
#     name="vis_papers",
#     primary_key={"name": "id", "type": "number"},
#     origin="example",
#     has_embeddings=True,
#     has_projection=True,
#     columns=[
#         {
#             "name": "Title",
#             "type": "text",
#         },
#         {
#             "name": "Abstract",
#             "type": "text",
#         },
#         {
#             "name": "word",
#             "type": "categorical",
#             "table_name": "vis_papers_words",
#             "derived_from": "Abstract",
#         },
#         # {
#         #     "name": "Abstract_num_words",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         #     "derived_how": "code",
#         # },
#         {
#             "name": "Year",
#             "type": "number",
#         },
#         {
#             "name": "Conference",
#             "type": "categorical",
#         },
#         {
#             "name": "author",
#             "type": "categorical",
#             "table_name": "vis_papers_authors",
#             "derived_from": "AuthorNames-Deduped",
#         },
#         {
#             "name": "keyword",
#             "type": "categorical",
#             "table_name": "vis_papers_keywords",
#             "derived_from": "AuthorKeywords",
#         },
#         {
#             "name": "PaperType",
#             "type": "categorical",
#         },
#         {
#             "name": "AminerCitationCount",
#             "type": "number",
#         },
#         {
#             "name": "CitationCount_CrossRef",
#             "type": "number",
#         },
#         {
#             "name": "PubsCited_CrossRef",
#             "type": "number",
#         },
#         {
#             "name": "Award",
#             "type": "categorical",
#         },
#         # {
#         #     "name": "id",
#         #     "type": "number",
#         # },
#         # {
#         #     "name": "MODEL_sport",
#         #     "type": "categorical",
#         #     "derived_from": "Abstract",
#         #     "derived_how": "model",
#         # },
#         # {
#         #     "name": "AuthorNames-Deduped",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "AuthorKeywords",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "Abstract_text_length",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "MODEL_num_participants",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "MODEL_keywords",
#         #     "type": "categorical",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "MODEL_summary",
#         #     "type": "text",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "MODEL_has_user_study",
#         #     "type": "categorical",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "DOI",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "Link",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "FirstPage",
#         #     "type": "number",
#         # },
#         # {
#         #     "name": "LastPage",
#         #     "type": "number",
#         # },
#         # {
#         #     "name": "AuthorNames",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "AuthorAffiliation",
#         #     "type": "categorical",
#         # },
#         # {
#         #     "name": "InternalReferences",
#         #     "type": "categorical",
#         # },
#         #
#         # {
#         #     "name": "Abstract_max_word_length",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "Abstract_avg_word_length",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         # },
#         # {
#         #     "name": "Abstract_perc_special_chars",
#         #     "type": "number",
#         #     "derived_from": "Abstract",
#         # },
#     ],
# )

# dataset_airline_reviews = DatasetInfo(
#     name="airline_reviews",
#     primary_key={"name": "id", "type": "number"},
#     origin="example",
#     has_embeddings=True,
#     has_projection=True,
#     columns=[
#         # {"name": "id", "type": "categorical"},
#         # {
#         #     "name": "tweet_num_characters",
#         #     "type": "number",
#         #     "derived_from": "tweet",
#         #     "derived_how": "code",
#         # },
#         # {
#         #     "name": "twitter_handle",
#         #     "type": "categorical",
#         #     "derived_from": "tweet",
#         #     "derived_how": "code",
#         # },
#         # {
#         #     "name": "is_on_hold",
#         #     "type": "categorical",
#         #     "derived_from": "tweet",
#         #     "derived_how": "model",
#         # },
#         {
#             "name": "word",
#             "type": "categorical",
#             "table_name": "tweet_words",
#             "derived_from": "tweet",
#         },
#         {"name": "airline", "type": "categorical"},
#         {"name": "airline_sentiment", "type": "categorical"},
#         {"name": "airline_sentiment_confidence", "type": "number"},
#         {"name": "negativereason", "type": "categorical"},
#         {"name": "negativereason_confidence", "type": "number"},
#         # {"name": "airline_sentiment_gold", "type": "categorical"},  # mostly null
#         {"name": "name", "type": "categorical"},
#         # {"name": "negativereason_gold", "type": "categorical"}, # mostly null
#         {"name": "retweet_count", "type": "number"},
#         {"name": "tweet", "type": "text"},
#         # {"name": "tweet_coord", "type": "categorical"}, # x, y coords I think
#         {"name": "tweet_created", "type": "date"},
#         {"name": "tweet_location", "type": "categorical"},
#         {"name": "user_timezone", "type": "categorical"},
#     ],
# )


# EXAMPLE_DATASET_INFO = {
#     "vis_papers": dataset_vis_papers,
#     "airline_reviews": dataset_airline_reviews,
# }

# EXAMPLE_DATA_PATHS = {
#     "vis_papers": {
#         "datasets": {
#             "vis_papers": "raw_data/vis_papers/vis_papers.parquet",
#             "vis_papers_words": "raw_data/vis_papers/vis_papers_words_span.parquet",
#             "vis_papers_authors": "raw_data/vis_papers/vis_papers_authors_span.parquet",
#             "vis_papers_keywords": "raw_data/vis_papers/vis_papers_keywords_span.parquet",
#         },
#         "embeddings": {"vis_papers": "raw_data/vis_papers/vis_papers_embeddings.pt"},
#     },
#     "airline_reviews": {
#         "datasets": {
#             "airline_reviews": "raw_data/airline_reviews/airlines.parquet",
#             "tweet_words": "raw_data/airline_reviews/tweet_words.parquet",
#         },
#         "embeddings": {"airline_reviews": "raw_data/airline_reviews/embeddings.pt"},
#     },
# }


# # EXAMPLE EMBEDDING FUNC
# def get_embedding(col: np.ndarray, model_name):
#     model = sentence_transformers.SentenceTransformer(model_name)
#     e = model.encode(col)
#     return e
