import json
import os
import re
from collections import defaultdict
from datetime import datetime, timedelta, timezone
import traceback

import huggingface_hub
from huggingface_hub import ModelCard
from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata
from transformers import AutoConfig, AutoTokenizer

from src.envs import HAS_HIGHER_RATE_LIMIT, TRUST_REMOTE_CODE


# ht to @Wauplin, thank you for the snippet!
# See https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/317
def check_model_card(repo_id: str) -> tuple[bool, str]:
    # Returns operation status, and error message
    try:
        card = ModelCard.load(repo_id)
    except huggingface_hub.utils.EntryNotFoundError:
        return False, False, "Please add a model card to your model to explain how you trained/fine-tuned it.", None
    except Exception as e:
        return False, False, f"Error while loading the model card. Exception: {str(e)}", None
    
    license = True
    if card.data.license is None:
        if not ("license_name" in card.data and "license_link" in card.data):
            license = False

    # Enforce card content
    if len(card.text) < 200:
        return False, license, "Please add a description to your model card bigger than 200 characters, it is too short.", None

    # Enforce license metadata
    if not license:
        return True, False, (
            "License not found. Please add a license to your model card using the `license` metadata or a"
            " `license_name`/`license_link` pair."
        ), None
 
    return True, True, "", card


def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=TRUST_REMOTE_CODE, test_tokenizer=False) -> tuple[bool, str, AutoConfig]:
    try:
        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) #, force_download=True)
        if test_tokenizer:
            try:
                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
            except ValueError as e:
                return (
                    False,
                    f"uses a tokenizer which is not in a transformers release: {e}",
                    None
                )
            except Exception as e:
                traceback.print_exc()
                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
        return True, None, config

    except ValueError as e:
        traceback.print_exc()
        return (
            False,
            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
            None
        )

    except Exception as e:
        traceback.print_exc()
        if "You are trying to access a gated repo." in str(e):
            return True, "uses a gated model.", None
        return False, "was not found on hub!", None

def get_model_size(model_info: ModelInfo, precision: str):
    size_pattern = re.compile(r"(\d+\.)?\d+(b|m)")
    safetensors = None
    try:
        safetensors = get_safetensors_metadata(model_info.id)
    except Exception as e:
        print(e)

    if safetensors is not None:
        model_size = round(sum(safetensors.parameter_count.values()) / 1e9, 3)
    else:
        try:
            size_match = re.search(size_pattern, model_info.id.lower())
            model_size = size_match.group(0)
            model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
        except AttributeError as e:
            return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py

    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.id.lower()) else 1
    model_size = size_factor * model_size
    return model_size

def get_model_arch(model_info: ModelInfo):
    return model_info.config.get("architectures", "Unknown")

def user_submission_permission(org_or_user, users_to_submission_dates, rate_limit_period, rate_limit_quota):
    if org_or_user not in users_to_submission_dates:
        return True, ""
    submission_dates = sorted(users_to_submission_dates[org_or_user])

    time_limit = (datetime.now(timezone.utc) - timedelta(days=rate_limit_period)).strftime("%Y-%m-%dT%H:%M:%SZ")
    submissions_after_timelimit = [d for d in submission_dates if d > time_limit]

    num_models_submitted_in_period = len(submissions_after_timelimit)
    if org_or_user in HAS_HIGHER_RATE_LIMIT:
        rate_limit_quota = 10000

    if num_models_submitted_in_period > rate_limit_quota:
        error_msg = f"Organisation or user `{org_or_user}`"
        error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
        error_msg += f"in the last {rate_limit_period} days.\n"
        error_msg += (
            "Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗"
        )
        return False, error_msg
    return True, ""


def already_submitted_models(requested_models_dir: str) -> set[str]:
    depth = 1
    file_names = []
    users_to_submission_dates = defaultdict(list)

    for root, _, files in os.walk(requested_models_dir):
        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
        if current_depth == depth:
            for file in files:
                if not file.endswith(".json"):
                    continue
                with open(os.path.join(root, file), "r") as f:
                    info = json.load(f)
                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")

                    # Select organisation
                    if info["model"].count("/") == 0 or "submitted_time" not in info:
                        continue
                    organisation, _ = info["model"].split("/")
                    users_to_submission_dates[organisation].append(info["submitted_time"])

    return set(file_names), users_to_submission_dates

def get_model_tags(model_card, model: str):
    is_merge_from_metadata = False
    is_moe_from_metadata = False
    is_merge_from_model_card = False
    is_moe_from_model_card = False

    # Storing the model tags
    tags = []
    merge_keywords = ["merged model", "merge model"]
    moe_keywords = ["moe", "mixtral"]

    if model_card is not None:
        if model_card.data.tags:
            is_merge_from_metadata = "merge" in model_card.data.tags
            is_moe_from_metadata = "moe" in model_card.data.tags
        # If the model is a merge but not saying it in the metadata, we flag it
        is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in merge_keywords)
        if is_merge_from_model_card or is_merge_from_metadata:
            tags.append("merge")
            if not is_merge_from_metadata:
                tags.append("flagged:undisclosed_merge")
        is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in moe_keywords)
    is_moe_from_name = "moe" in model.lower().replace("/", "-").replace("_", "-").split("-")
    if is_moe_from_model_card or is_moe_from_name or is_moe_from_metadata:
        tags.append("moe")
        # We no longer tag undisclosed MoEs
        #if not is_moe_from_metadata:
        #    tags.append("flagged:undisclosed_moe")

    return tags