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

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
from huggingface_hub import hf_hub_download, HfFileSystem
from huggingface_hub.utils import validate_repo_id
from pathlib import Path
import fnmatch
from huggingface_hub.hf_api import get_hf_file_metadata, hf_hub_url


# 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, "Please add a model card to your model to explain how you trained/fine-tuned it.", None

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

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

    return True, "", card


def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=True, 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:
                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:
        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:
        if "You are trying to access a gated repo." in str(e):
            return True, "uses a gated model.", None
        return False, f"was not found or misconfigured on the hub! Error raised was {e.args[0]}", 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)
        num_parameters = 0
        mem = 0
        for key in safetensors.parameter_count:
            if key in ["F16", "BF16"]:
                mem += safetensors.parameter_count[key] * 2
            else:
                mem += safetensors.parameter_count[key] * 4

            num_parameters += safetensors.parameter_count[key]

        params_b = round(num_parameters / 1e9, 2)
        size_gb = round(mem / 1e9,2)
        return params_b, size_gb
    except Exception as e:
        print(str(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
    if precision == "16bit":
        size_gb = model_size * 2
    else:
        size_gb = model_size * 4
    return model_size, size_gb

KNOWN_SIZE_FACTOR = {
    "gptq": {"4bit": 8, "8bit": 4, "2bit": 8, "3bit": 12},
    "awq": {"4bit": 8},
    "bitsandbytes": {"4bit": 2},
    "aqlm": {"4bit": 8, "8bit": 4, "2bit": 8, "3bit": 6},
}

BYTES = {
    "I32": 4,
    "I16": 2,
    "I8": 1,
    "F16": 2,
    "BF16": 2,
    "F32": 4,
    "U8": 1}


def get_quantized_model_parameters_memory(model_info: ModelInfo, quant_method="", bits="4bit"):
    try:
        safetensors = get_safetensors_metadata(model_info.id)
        num_parameters = 0
        mem = 0
        for key in safetensors.parameter_count:
            mem += safetensors.parameter_count[key] * BYTES[key]
            if key in ["I32", "U8", "I16", "I8"]:
                param = safetensors.parameter_count[key] * KNOWN_SIZE_FACTOR[quant_method][bits]
                if key == "I8":
                    param = param / 2
                num_parameters += param

        params_b = round(num_parameters / 1e9, 2)
        size_gb = round(mem / 1e9,2)
        return params_b, size_gb
    except Exception as e:
        print(str(e))

    filenames = [sib.rfilename for sib in model_info.siblings]
    if "pytorch_model.bin" in filenames:
        url = hf_hub_url(model_info.id, filename="pytorch_model.bin")
        meta = get_hf_file_metadata(url)
        params_b = round(meta.size * 2 / 1e9, 2)
        size_gb = round(meta.size / 1e9, 2)
        return params_b, size_gb

    if "pytorch_model.bin.index.json" in filenames:
        index_path = hf_hub_download(model_info.id, filename="pytorch_model.bin.index.json")
        """
        {
        "metadata": {
            "total_size": 28272820224
        },....
        """
        size = json.load(open(index_path))
        bytes_per_param = 2
        if ("metadata" in size) and ("total_size" in size["metadata"]):
            return round(size["metadata"]["total_size"] / bytes_per_param / 1e9, 2), \
                    round(size["metadata"]["total_size"] / 1e9, 2)

    return None, None

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 = 2 * rate_limit_quota

    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)
                    # {quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json
                    quant_type = info.get("quant_type", "None")
                    weight_dtype = info.get("weight_dtype", "None")
                    compute_dtype = info.get("compute_dtype", "None")
                    file_names.append(f"{info['model']}_{info['revision']}_{quant_type}_{info['precision']}_{weight_dtype}_{compute_dtype}")

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

                    try:
                        organisation, _ = info["model"].split("/")
                    except:
                        print(info["model"])
                        organisation = "local" # temporary "local"
                    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

    tags = []
    if model_card is None:
        return tags
    if model_card.data.tags:
        is_merge_from_metadata = any([tag in model_card.data.tags for tag in ["merge", "moerge", "mergekit", "lazymergekit"]])
        is_moe_from_metadata = any([tag in model_card.data.tags for tag in ["moe", "moerge"]])

    is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in ["merged model", "merge model", "moerge"])
    if is_merge_from_model_card or is_merge_from_metadata:
        tags.append("merge")
    is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in ["moe", "mixtral"])
    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")

    return tags

def is_gguf_on_hub(repo_id: str, filename="*Q4_0.gguf"):

    validate_repo_id(repo_id)

    hffs = HfFileSystem()

    files = [
        file["name"] if isinstance(file, dict) else file
        for file in hffs.ls(repo_id)
    ]

    # split each file into repo_id, subfolder, filename
    file_list: List[str] = []
    for file in files:
        rel_path = Path(file).relative_to(repo_id)
        file_list.append(str(rel_path))

    print(file_list)

    matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)]  # type: ignore
    if len(matching_files) > 0:
        return True, None, matching_files, None

    matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename.lower())]

    if len(matching_files) > 0:
        return True, None, matching_files, filename.lower()
    else:
        return False, f"the model {repo_id} don't contains any {filename}.", None, None