src/display/utils.py

from dataclasses import dataclass, make_dataclass
from enum import Enum

import pandas as pd

from src.about import Tasks, TasksMultimodal, TasksMib_Subgraph, TasksMib_Causalgraph

def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False

## Leaderboard columns
auto_eval_column_dict = []
auto_eval_column_dict_multimodal = []

auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict.append(["hf_repo", ColumnContent, ColumnContent("HF Repo", "str", False)])
auto_eval_column_dict.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
#Scores
for task in Tasks:
    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])

auto_eval_column_dict_multimodal.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_multimodal.append(["hf_repo", ColumnContent, ColumnContent("HF Repo", "str", False)])
auto_eval_column_dict_multimodal.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
for task in TasksMultimodal:
    auto_eval_column_dict_multimodal.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
    if task.value.col_name in ("ewok", "EWoK"):   # make sure this appears in the right order
        auto_eval_column_dict_multimodal.append(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict_multimodal.append(["vision_average", ColumnContent, ColumnContent("Vision Average", "number", True)])
auto_eval_column_dict_multimodal.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict_multimodal.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])



AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
AutoEvalColumnMultimodal = make_dataclass("AutoEvalColumnMultimodal", auto_eval_column_dict_multimodal, frozen=True)










auto_eval_column_dict_mib_subgraph = []

# Method name column
auto_eval_column_dict_mib_subgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

# For each task and model combination
for task in TasksMib_Subgraph:
    for model in task.value.models:
        col_name = f"{task.value.benchmark}_{model}"  # ioi_meta_llama, mcqa_qwen, etc.
        auto_eval_column_dict_mib_subgraph.append([
            col_name, 
            ColumnContent, 
            ColumnContent(col_name, "number", True)
        ])

# Average column
auto_eval_column_dict_mib_subgraph.append(["average", ColumnContent, ColumnContent("Average", "number", True)])


# Create the dataclass for MIB columns
AutoEvalColumn_mib_subgraph = make_dataclass("AutoEvalColumn_mib_subgraph", auto_eval_column_dict_mib_subgraph, frozen=True)

# Column selection for display
COLS_MIB_SUBGRAPH = [c.name for c in fields(AutoEvalColumn_mib_subgraph) if not c.hidden]


BENCHMARK_COLS_MIB_SUBGRAPH = []
for task in TasksMib_Subgraph:
    for model in task.value.models:
        col_name = f"{task.value.col_name}_{model.replace('-', '_')}"
        BENCHMARK_COLS_MIB_SUBGRAPH.append(col_name)

# Implement the same for causal graph, auto_eval_column_dict_mib_causalgraph, AutoEvalColumn_mib_causalgraph
AutoEvalColumn_mib_causalgraph = []
COLS_MIB_CAUSALGRAPH = []
BENCHMARK_COLS_MIB_CAUSALGRAPH = []






# # Initialize the MIB causal graph columns
# auto_eval_column_dict_mib_causalgraph = []

# # Method name column
# auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

# # For each model-task-intervention combination
# for task in TasksMib_Causalgraph:
#     for model in task.value.models:
#         for intervention in task.value.interventions:
#             col_name = f"{model}_{task.value.benchmark}_{intervention}".lower()
#             auto_eval_column_dict_mib_causalgraph.append([
#                 col_name, 
#                 ColumnContent, 
#                 ColumnContent(col_name, "number", True)
#             ])

# auto_eval_column_dict_mib_causalgraph = []

# # Method name column
# auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

# # For each model-task-intervention combination
# for task in TasksMib_Causalgraph:
#     for model in task.value.models:
#         model_name = model.lower()  # Convert model name to lowercase
#         for layer in task.value.layers:
#             for intervention in task.value.interventions:
#                 for counterfactual in task.value.counterfactuals:
#                     # Include model name in the column name
#                     col_name = f"{model_name}_layer{layer}_{intervention}_{counterfactual}"
#                     field_name = col_name.lower()
#                     auto_eval_column_dict_mib_causalgraph.append([
#                         field_name, 
#                         ColumnContent, 
#                         ColumnContent(col_name, "number", True)
#                     ])

# # In utils.py, modify auto_eval_column_dict_mib_causalgraph:
# auto_eval_column_dict_mib_causalgraph = []

# # Method name column
# auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

# # For each model-task-intervention-counterfactual combination
# for task in TasksMib_Causalgraph:
#     for model in ["qwen2forcausallm", "gemma2forcausallm", "llamaforcausallm"]:  # exact model names
#         for layer in task.value.layers:
#             for intervention in task.value.interventions:
#                 for counterfactual in task.value.counterfactuals:
#                     # Match the exact format from the data
#                     col_name = f"{model}_layer{layer}_{intervention}_{counterfactual}".lower()
#                     auto_eval_column_dict_mib_causalgraph.append([
#                         col_name, 
#                         ColumnContent, 
#                         ColumnContent(col_name, "number", True)
#                     ])




auto_eval_column_dict_mib_causalgraph = []

# Method name column
auto_eval_column_dict_mib_causalgraph.append(["method", ColumnContent, ColumnContent("Method", "markdown", True, never_hidden=True)])

# Add eval_name column
auto_eval_column_dict_mib_causalgraph.append(["eval_name", ColumnContent, ColumnContent("eval_name", "str", True)])

# For each model-task-intervention-counterfactual combination
for task in TasksMib_Causalgraph:
    for model in task.value.models:  # Use exact model names with correct casing
        model_name = model  # Don't convert to lowercase
        for layer in task.value.layers:
            for intervention in task.value.interventions:
                for counterfactual in task.value.counterfactuals:
                    # Match exact format from the actual data
                    col_name = f"{model_name}_layer{layer}_{intervention}_{counterfactual}"
                    # Use the exact column name as both the field name and display name
                    auto_eval_column_dict_mib_causalgraph.append([
                        col_name, 
                        ColumnContent, 
                        ColumnContent(col_name, "number", True)
                    ])

# Create the dataclass
AutoEvalColumn_mib_causalgraph = make_dataclass(
    "AutoEvalColumn_mib_causalgraph", 
    auto_eval_column_dict_mib_causalgraph,
    frozen=True
)

# Column selection for display 
COLS_MIB_CAUSALGRAPH = [c.name for c in fields(AutoEvalColumn_mib_causalgraph) if not c.hidden]


BENCHMARK_COLS_MIB_CAUSALGRAPH = [f"{model}_{task.value.benchmark}_{intervention}".lower()
                                 for task in TasksMib_Causalgraph
                                 for model in task.value.models
                                 for intervention in task.value.interventions]





















## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn:  # Queue column
    model = ColumnContent("model", "markdown", True)
    track = ColumnContent("track", "str", True)
    revision = ColumnContent("revision", "str", True)
    private = ColumnContent("private", "bool", True)
    status = ColumnContent("status", "str", True)

## All the model information that we might need
@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = "" # emoji

# Column selection

COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
COLS_MULTIMODAL = [c.name for c in fields(AutoEvalColumnMultimodal) if not c.hidden]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

BENCHMARK_COLS = [t.value.col_name for t in Tasks]
BENCHMARK_COLS_MULTIMODAL = [t.value.col_name for t in TasksMultimodal]

TEXT_TASKS = {
    "glue": ["cola", "sst2", "mrpc", "qqp", "mnli", "mnli-mm", "qnli", "rte",
            "boolq", "multirc", "wsc"],
    # Lots of BLiMP tasks – use verifier function below to see if you've included everything.
    "blimp": ["adjunct_island","anaphor_gender_agreement","anaphor_number_agreement","animate_subject_passive","animate_subject_trans",
        "causative","complex_NP_island","coordinate_structure_constraint_complex_left_branch","coordinate_structure_constraint_object_extraction","determiner_noun_agreement_1",
        "determiner_noun_agreement_2","determiner_noun_agreement_irregular_1","determiner_noun_agreement_irregular_2","determiner_noun_agreement_with_adjective_1",
        "determiner_noun_agreement_with_adj_2","determiner_noun_agreement_with_adj_irregular_1","determiner_noun_agreement_with_adj_irregular_2","distractor_agreement_relational_noun",
        "distractor_agreement_relative_clause","drop_argument","ellipsis_n_bar_1","ellipsis_n_bar_2",
        "existential_there_object_raising", "existential_there_quantifiers_1",
        "existential_there_quantifiers_2", "existential_there_subject_raising", "expletive_it_object_raising",
        "inchoative", "intransitive","irregular_past_participle_adjectives", "irregular_past_participle_verbs",
        "irregular_plural_subject_verb_agreement_1", "irregular_plural_subject_verb_agreement_2", "left_branch_island_echo_question", "left_branch_island_simple_question",
        "matrix_question_npi_licensor_present", "npi_present_1", "npi_present_2", "only_npi_licensor_present", "only_npi_scope", "passive_1", "passive_2",
        "principle_A_case_1", "principle_A_case_2", "principle_A_c_command", "principle_A_domain_1",
        "principle_A_domain_2", "principle_A_domain_3", "principle_A_reconstruction", "regular_plural_subject_verb_agreement_1",
        "regular_plural_subject_verb_agreement_2", "sentential_negation_npi_licensor_present", "sentential_negation_npi_scope", "sentential_subject_island",
        "superlative_quantifiers_1", "superlative_quantifiers_2", "tough_vs_raising_1", "tough_vs_raising_2",
        "transitive", "wh_island", "wh_questions_object_gap", "wh_questions_subject_gap",
        "wh_questions_subject_gap_long_distance", "wh_vs_that_no_gap", "wh_vs_that_no_gap_long_distance", "wh_vs_that_with_gap",
        "wh_vs_that_with_gap_long_distance"
    ],
    "blimp_supplement": ["hypernym", "qa_congruence_easy", "qa_congruence_tricky",
                "subject_aux_inversion", "turn_taking"],
    "ewok": ["agent-properties", "material-dynamics", "material-properties", "physical-dynamics",
            "physical-interactions", "physical-relations", "quantitative-properties",
            "social-interactions", "social-properties", "social-relations", "spatial-relations"]
}

VISION_TASKS = {
    "vqa": ["vqa"],
    "winoground": ["winoground"],
    "devbench": ["lex-viz_vocab", "gram-trog", "sem-things"]
}

NUM_EXPECTED_EXAMPLES = {
    "glue": {
        "cola": 522,
        "sst2": 436,
        "mrpc": 204,
        "qqp": 20215,
        "mnli": 4908,
        "mnli-mm": 4916,
        "qnli": 2732,
        "rte": 139,
        "boolq": 1635,
        "multirc": 2424,
        "wsc": 52
    },
    "blimp": {
        "adjunct_island": 928,
        "anaphor_gender_agreement": 971,
        "anaphor_number_agreement": 931,
        "animate_subject_passive": 895,
        "animate_subject_trans": 923,
        "causative": 818,
        "complex_NP_island": 846,
        "coordinate_structure_constraint_complex_left_branch": 906,
        "coordinate_structure_constraint_object_extraction": 949,
        "determiner_noun_agreement_1": 929,
        "determiner_noun_agreement_2": 931,
        "determiner_noun_agreement_irregular_1": 681,
        "determiner_noun_agreement_irregular_2": 820,
        "determiner_noun_agreement_with_adjective_1": 933,
        "determiner_noun_agreement_with_adj_2": 941,
        "determiner_noun_agreement_with_adj_irregular_1": 718,
        "determiner_noun_agreement_with_adj_irregular_2": 840,
        "distractor_agreement_relational_noun": 788,
        "distractor_agreement_relative_clause": 871,
        "drop_argument": 920,
        "ellipsis_n_bar_1": 802,
        "ellipsis_n_bar_2": 828,
        "existential_there_object_raising": 812,
        "existential_there_quantifiers_1": 930,
        "existential_there_quantifiers_2": 911,
        "existential_there_subject_raising": 924,
        "expletive_it_object_raising": 759,
        "inchoative": 855,
        "intransitive": 868,
        "irregular_past_participle_adjectives": 961,
        "irregular_past_participle_verbs": 942,
        "irregular_plural_subject_verb_agreement_1": 804,
        "irregular_plural_subject_verb_agreement_2": 892,
        "left_branch_island_echo_question": 947,
        "left_branch_island_simple_question": 951,
        "matrix_question_npi_licensor_present": 929,
        "npi_present_1": 909,
        "npi_present_2": 914,
        "only_npi_licensor_present": 882,
        "only_npi_scope": 837,
        "passive_1": 840,
        "passive_2": 903,
        "principle_A_case_1": 912,
        "principle_A_case_2": 915,
        "principle_A_c_command": 946,
        "principle_A_domain_1": 914,
        "principle_A_domain_2": 915,
        "principle_A_domain_3": 941,
        "principle_A_reconstruction": 967,
        "regular_plural_subject_verb_agreement_1": 890,
        "regular_plural_subject_verb_agreement_2": 945,
        "sentential_negation_npi_licensor_present": 919,
        "sentential_negation_npi_scope": 871,
        "sentential_subject_island": 961,
        "superlative_quantifiers_1": 979,
        "superlative_quantifiers_2": 986,
        "tough_vs_raising_1": 948,
        "tough_vs_raising_2": 920,
        "transitive": 868,
        "wh_island": 960,
        "wh_questions_object_gap": 859,
        "wh_questions_subject_gap": 898,
        "wh_questions_subject_gap_long_distance": 857,
        "wh_vs_that_no_gap": 861,
        "wh_vs_that_no_gap_long_distance": 875,
        "wh_vs_that_with_gap": 919,
        "wh_vs_that_with_gap_long_distance": 910
    },
    "blimp_supplement": {
        "hypernym": 842,
        "qa_congruence_easy": 64,
        "qa_congruence_tricky": 165,
        "subject_aux_inversion": 3867,
        "turn_taking": 280
    },
    "ewok": {
        "agent-properties": 2210,
        "material-dynamics": 770,
        "material-properties": 170,
        "physical-dynamics": 120,
        "physical-interactions": 556,
        "physical-relations": 818,
        "quantitative-properties": 314,
        "social-interactions": 294,
        "social-properties": 328,
        "social-relations": 1548,
        "spatial-relations": 490
    },
    "vqa": {
        "vqa": 25230
    },
    "winoground": {
        "winoground": 746
    },
    "devbench": {
        "lex-viz_vocab": 119,
        "gram-trog": 76,
        "sem-things": 1854
    }
}