import json
import os
from datetime import datetime, timezone

from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO, EVAL_RESULTS_PATH, RESULTS_REPO
from src.submission.check_validity import (
    already_submitted_models,
    check_model_card,
    get_model_size,
    is_model_on_hub,
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def add_new_eval(
    model: str,
    base_model: str,
    revision: str,
    precision: str,
    weight_type: str,
    model_type: str,
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    user_name = ""
    model_path = model
    if "/" in model:
        user_name = model.split("/")[0]
        model_path = model.split("/")[1]

    precision = precision.split(" ")[0]
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    if model_type is None or model_type == "":
        return styled_error("Please select a model type.")

    # Does the model actually exist?
    if revision == "":
        revision = "main"

    # Is the model on the hub?
    if weight_type in ["Delta", "Adapter"]:
        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
        if not base_model_on_hub:
            return styled_error(f'Base model "{base_model}" {error}')

    if not weight_type == "Adapter":
        model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
        if not model_on_hub:
            return styled_error(f'Model "{model}" {error}')

    # Is the model info correctly filled?
    try:
        model_info = API.model_info(repo_id=model, revision=revision)
    except Exception:
        return styled_error("Could not get your model information. Please fill it up properly.")

    model_size = get_model_size(model_info=model_info, precision=precision)

    # Were the model card and license filled?
    try:
        license = model_info.cardData["license"]
    except Exception:
        return styled_error("Please select a license for your model")

    modelcard_OK, error_msg = check_model_card(model)
    if not modelcard_OK:
        return styled_error(error_msg)

    # Seems good, creating the eval
    print("Adding new eval")

    eval_entry = {
        "model": model,
        "base_model": base_model,
        "revision": revision,
        "precision": precision,
        "weight_type": weight_type,
        "status": "PENDING",
        "submitted_time": current_time,
        "model_type": model_type,
        "likes": model_info.likes,
        "params": model_size,
        "license": license,
        "private": False,
    }

    # Check for duplicate submission
    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
        return styled_warning("This model has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    print("Uploading eval file")
    API.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to eval queue",
    )

    # Remove the local file
    os.remove(out_path)

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
    )

# --------------------------------------------------------
# Manual metrics submission (bypass evaluation queue)
# --------------------------------------------------------

ALL_SUBJECTIVE_FIELDS = [
    "readability",
    "relevance",
    "explanation_clarity",
    "problem_identification",
    "actionability",
    "completeness",
    "specificity",
    "contextual_adequacy",
    "consistency",
    "brevity",
]

def _compute_multimetric(payload: dict) -> float:
    """Average of the 10 subjective metrics."""
    total = sum(float(payload[f]) for f in ALL_SUBJECTIVE_FIELDS)
    return total / len(ALL_SUBJECTIVE_FIELDS)

def add_manual_results(
    model: str,
    revision: str,
    bleu: float,
    readability: int,
    relevance: int,
    explanation_clarity: int,
    problem_identification: int,
    actionability: int,
    completeness: int,
    specificity: int,
    contextual_adequacy: int,
    consistency: int,
    brevity: int,
    pass_at_1: float,
    pass_at_5: float,
    pass_at_10: float,
):
    """Directly submit evaluation metrics for a model and push them to the results dataset."""

    # Basic validation
    if model == "":
        return styled_error("Please specify a model name.")

    if revision == "":
        revision = "main"

    if pass_at_5 < pass_at_1:
        return styled_error("pass@5 must be greater or equal to pass@1")
    if pass_at_10 < pass_at_5:
        return styled_error("pass@10 must be greater or equal to pass@5")

    # Prepare dictionary in the same format used by read_evals.py
    payload_dict = {
        "model": model,
        "revision": revision,
        "bleu": bleu,
        "readability": readability,
        "relevance": relevance,
        "explanation_clarity": explanation_clarity,
        "problem_identification": problem_identification,
        "actionability": actionability,
        "completeness": completeness,
        "specificity": specificity,
        "contextual_adequacy": contextual_adequacy,
        "consistency": consistency,
        "brevity": brevity,
        "pass_at_1": pass_at_1,
        "pass_at_5": pass_at_5,
        "pass_at_10": pass_at_10,
    }

    multimetric = _compute_multimetric(payload_dict)

    # Compose final results file (same structure as api_submit_results)
    result_json = {
        "config": {
            "model_dtype": "unknown",
            "model_name": model,
            "model_sha": revision,
        },
        "results": {
            "bleu": {"score": bleu},
            "multimetric": {"score": multimetric},
            "pass_at_1": {"score": pass_at_1},
            "pass_at_5": {"score": pass_at_5},
            "pass_at_10": {"score": pass_at_10},
        },
    }

    # Add subjective metrics
    for field in ALL_SUBJECTIVE_FIELDS:
        result_json["results"][field] = {"score": payload_dict[field]}

    # Write file locally then upload
    try:
        os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)
    except Exception:
        pass

    from datetime import datetime, timezone
    import uuid

    ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
    unique_id = uuid.uuid4().hex[:8]
    filename = f"results_{model.replace('/', '_')}_{ts}_{unique_id}.json"
    local_path = os.path.join(EVAL_RESULTS_PATH, filename)

    try:
        with open(local_path, "w") as fp:
            json.dump(result_json, fp)

        API.upload_file(
            path_or_fileobj=local_path,
            path_in_repo=filename,
            repo_id=RESULTS_REPO,
            repo_type="dataset",
            commit_message=f"Add manual results for {model}",
        )
    except Exception as e:
        return styled_error(f"Failed to upload results: {e}")
    finally:
        if os.path.exists(local_path):
            os.remove(local_path)

    return styled_message("Metrics successfully submitted! The leaderboard will refresh shortly.")