# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile

import numpy as np
import torch
from huggingface_hub.hf_api import list_models
from torch import nn
from tqdm import tqdm

from transformers import MarianConfig, MarianMTModel, MarianTokenizer


def remove_suffix(text: str, suffix: str):
    if text.endswith(suffix):
        return text[: -len(suffix)]
    return text  # or whatever


def remove_prefix(text: str, prefix: str):
    if text.startswith(prefix):
        return text[len(prefix) :]
    return text  # or whatever


def convert_encoder_layer(opus_dict, layer_prefix: str, converter: dict):
    sd = {}
    for k in opus_dict:
        if not k.startswith(layer_prefix):
            continue
        stripped = remove_prefix(k, layer_prefix)
        v = opus_dict[k].T  # besides embeddings, everything must be transposed.
        sd[converter[stripped]] = torch.tensor(v).squeeze()
    return sd


def load_layers_(layer_lst: nn.ModuleList, opus_state: dict, converter, is_decoder=False):
    for i, layer in enumerate(layer_lst):
        layer_tag = f"decoder_l{i + 1}_" if is_decoder else f"encoder_l{i + 1}_"
        sd = convert_encoder_layer(opus_state, layer_tag, converter)
        layer.load_state_dict(sd, strict=False)


def find_pretrained_model(src_lang: str, tgt_lang: str) -> List[str]:
    """Find models that can accept src_lang as input and return tgt_lang as output."""
    prefix = "Helsinki-NLP/opus-mt-"
    model_list = list_models()
    model_ids = [x.id for x in model_list if x.id.startswith("Helsinki-NLP")]
    src_and_targ = [
        remove_prefix(m, prefix).lower().split("-") for m in model_ids if "+" not in m
    ]  # + cant be loaded.
    matching = [f"{prefix}{a}-{b}" for (a, b) in src_and_targ if src_lang in a and tgt_lang in b]
    return matching


def add_emb_entries(wemb, final_bias, n_special_tokens=1):
    vsize, d_model = wemb.shape
    embs_to_add = np.zeros((n_special_tokens, d_model))
    new_embs = np.concatenate([wemb, embs_to_add])
    bias_to_add = np.zeros((n_special_tokens, 1))
    new_bias = np.concatenate((final_bias, bias_to_add), axis=1)
    return new_embs, new_bias


def _cast_yaml_str(v):
    bool_dct = {"true": True, "false": False}
    if not isinstance(v, str):
        return v
    elif v in bool_dct:
        return bool_dct[v]
    try:
        return int(v)
    except (TypeError, ValueError):
        return v


def cast_marian_config(raw_cfg: Dict[str, str]) -> Dict:
    return {k: _cast_yaml_str(v) for k, v in raw_cfg.items()}


CONFIG_KEY = "special:model.yml"


def load_config_from_state_dict(opus_dict):
    import yaml

    cfg_str = "".join([chr(x) for x in opus_dict[CONFIG_KEY]])
    yaml_cfg = yaml.load(cfg_str[:-1], Loader=yaml.BaseLoader)
    return cast_marian_config(yaml_cfg)


def find_model_file(dest_dir):  # this one better
    model_files = list(Path(dest_dir).glob("*.npz"))
    if len(model_files) != 1:
        raise ValueError(f"Found more than one model file: {model_files}")
    model_file = model_files[0]
    return model_file


# Group Names Logic: change long opus model names to something shorter, like opus-mt-en-ROMANCE
ROM_GROUP = (
    "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT"
    "+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co"
    "+nap+scn+vec+sc+ro+la"
)
GROUPS = [
    ("cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh", "ZH"),
    (ROM_GROUP, "ROMANCE"),
    ("de+nl+fy+af+da+fo+is+no+nb+nn+sv", "NORTH_EU"),
    ("da+fo+is+no+nb+nn+sv", "SCANDINAVIA"),
    ("se+sma+smj+smn+sms", "SAMI"),
    ("nb_NO+nb+nn_NO+nn+nog+no_nb+no", "NORWAY"),
    ("ga+cy+br+gd+kw+gv", "CELTIC"),  # https://en.wikipedia.org/wiki/Insular_Celtic_languages
]
GROUP_TO_OPUS_NAME = {
    "opus-mt-ZH-de": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-de",
    "opus-mt-ZH-fi": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
    "opus-mt-ZH-sv": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-sv",
    "opus-mt-SCANDINAVIA-SCANDINAVIA": "da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv",
    "opus-mt-NORTH_EU-NORTH_EU": "de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv",
    "opus-mt-de-ZH": "de-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
    "opus-mt-en_el_es_fi-en_el_es_fi": "en+el+es+fi-en+el+es+fi",
    "opus-mt-en-ROMANCE": (
        "en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
        "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
        "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la"
    ),
    "opus-mt-en-CELTIC": "en-ga+cy+br+gd+kw+gv",
    "opus-mt-es-NORWAY": "es-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
    "opus-mt-fi_nb_no_nn_ru_sv_en-SAMI": "fi+nb+no+nn+ru+sv+en-se+sma+smj+smn+sms",
    "opus-mt-fi-ZH": "fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
    "opus-mt-fi-NORWAY": "fi-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
    "opus-mt-ROMANCE-en": (
        "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
        "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
        "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en"
    ),
    "opus-mt-CELTIC-en": "ga+cy+br+gd+kw+gv-en",
    "opus-mt-sv-ZH": "sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
    "opus-mt-sv-NORWAY": "sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
}
OPUS_GITHUB_URL = "https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/"
ORG_NAME = "Helsinki-NLP/"


def convert_opus_name_to_hf_name(x):
    """For OPUS-MT-Train/ DEPRECATED"""
    for substr, grp_name in GROUPS:
        x = x.replace(substr, grp_name)
    return x.replace("+", "_")


def convert_hf_name_to_opus_name(hf_model_name):
    """
    Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
    """
    hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
    if hf_model_name in GROUP_TO_OPUS_NAME:
        opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
    else:
        opus_w_prefix = hf_model_name.replace("_", "+")
    return remove_prefix(opus_w_prefix, "opus-mt-")


def get_system_metadata(repo_root):
    import git

    return {
        "helsinki_git_sha": git.Repo(path=repo_root, search_parent_directories=True).head.object.hexsha,
        "transformers_git_sha": git.Repo(path=".", search_parent_directories=True).head.object.hexsha,
        "port_machine": socket.gethostname(),
        "port_time": time.strftime("%Y-%m-%d-%H:%M"),
    }


# docstyle-ignore
FRONT_MATTER_TEMPLATE = """---
language:
{}
tags:
- translation

license: apache-2.0
---
"""
DEFAULT_REPO = "Tatoeba-Challenge"
DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")


def write_model_card(
    hf_model_name: str,
    repo_root=DEFAULT_REPO,
    save_dir=Path("marian_converted"),
    dry_run=False,
    extra_metadata={},
) -> str:
    """
    Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
    s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
    """
    import pandas as pd

    hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
    opus_name: str = convert_hf_name_to_opus_name(hf_model_name)
    if repo_root not in ("OPUS-MT-train", "Tatoeba-Challenge"):
        raise ValueError(f"Repos root is {repo_root}. Expected either OPUS-MT-train or Tatoeba-Challenge")
    opus_readme_path = Path(repo_root).joinpath("models", opus_name, "README.md")
    if not (opus_readme_path.exists()):
        raise ValueError(f"Readme file {opus_readme_path} not found")

    opus_src, opus_tgt = [x.split("+") for x in opus_name.split("-")]

    readme_url = f"https://github.com/Helsinki-NLP/{repo_root}/tree/master/models/{opus_name}/README.md"

    s, t = ",".join(opus_src), ",".join(opus_tgt)
    metadata = {
        "hf_name": hf_model_name,
        "source_languages": s,
        "target_languages": t,
        "opus_readme_url": readme_url,
        "original_repo": repo_root,
        "tags": ["translation"],
    }
    metadata.update(extra_metadata)
    metadata.update(get_system_metadata(repo_root))

    # combine with opus markdown

    extra_markdown = (
        f"### {hf_model_name}\n\n* source group: {metadata['src_name']} \n* target group: "
        f"{metadata['tgt_name']} \n*  OPUS readme: [{opus_name}]({readme_url})\n"
    )

    content = opus_readme_path.open().read()
    content = content.split("\n# ")[-1]  # Get the lowest level 1 header in the README -- the most recent model.
    splat = content.split("*")[2:]
    print(splat[3])
    content = "*".join(splat)
    content = (
        FRONT_MATTER_TEMPLATE.format(metadata["src_alpha2"])
        + extra_markdown
        + "\n* "
        + content.replace("download", "download original weights")
    )

    items = "\n\n".join([f"- {k}: {v}" for k, v in metadata.items()])
    sec3 = "\n### System Info: \n" + items
    content += sec3
    if dry_run:
        return content, metadata
    sub_dir = save_dir / f"opus-mt-{hf_model_name}"
    sub_dir.mkdir(exist_ok=True)
    dest = sub_dir / "README.md"
    dest.open("w").write(content)
    pd.Series(metadata).to_json(sub_dir / "metadata.json")

    # if dry_run:
    return content, metadata


def make_registry(repo_path="Opus-MT-train/models"):
    if not (Path(repo_path) / "fr-en" / "README.md").exists():
        raise ValueError(
            f"repo_path:{repo_path} does not exist: "
            "You must run: git clone git@github.com:Helsinki-NLP/Opus-MT-train.git before calling."
        )
    results = {}
    for p in Path(repo_path).iterdir():
        n_dash = p.name.count("-")
        if n_dash == 0:
            continue
        else:
            lns = list(open(p / "README.md").readlines())
            results[p.name] = _parse_readme(lns)
    return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]


def convert_all_sentencepiece_models(model_list=None, repo_path=None, dest_dir=Path("marian_converted")):
    """Requires 300GB"""
    save_dir = Path("marian_ckpt")
    dest_dir = Path(dest_dir)
    dest_dir.mkdir(exist_ok=True)
    save_paths = []
    if model_list is None:
        model_list: list = make_registry(repo_path=repo_path)
    for k, prepro, download, test_set_url in tqdm(model_list):
        if "SentencePiece" not in prepro:  # dont convert BPE models.
            continue
        if not os.path.exists(save_dir / k):
            download_and_unzip(download, save_dir / k)
        pair_name = convert_opus_name_to_hf_name(k)
        convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")

        save_paths.append(dest_dir / f"opus-mt-{pair_name}")
    return save_paths


def lmap(f, x) -> List:
    return list(map(f, x))


def fetch_test_set(test_set_url):
    import wget

    fname = wget.download(test_set_url, "opus_test.txt")
    lns = Path(fname).open().readlines()
    src = lmap(str.strip, lns[::4])
    gold = lmap(str.strip, lns[1::4])
    mar_model = lmap(str.strip, lns[2::4])
    if not (len(gold) == len(mar_model) == len(src)):
        raise ValueError(f"Gold, marian and source lengths {len(gold)}, {len(mar_model)}, {len(src)} mismatched")
    os.remove(fname)
    return src, mar_model, gold


def convert_whole_dir(path=Path("marian_ckpt/")):
    for subdir in tqdm(list(path.ls())):
        dest_dir = f"marian_converted/{subdir.name}"
        if (dest_dir / "pytorch_model.bin").exists():
            continue
        convert(source_dir, dest_dir)


def _parse_readme(lns):
    """Get link and metadata from opus model card equivalent."""
    subres = {}
    for ln in [x.strip() for x in lns]:
        if not ln.startswith("*"):
            continue
        ln = ln[1:].strip()

        for k in ["download", "dataset", "models", "model", "pre-processing"]:
            if ln.startswith(k):
                break
        else:
            continue
        if k in ["dataset", "model", "pre-processing"]:
            splat = ln.split(":")
            _, v = splat
            subres[k] = v
        elif k == "download":
            v = ln.split("(")[-1][:-1]
            subres[k] = v
    return subres


def save_tokenizer_config(dest_dir: Path, separate_vocabs=False):
    dname = dest_dir.name.split("-")
    dct = {"target_lang": dname[-1], "source_lang": "-".join(dname[:-1]), "separate_vocabs": separate_vocabs}
    save_json(dct, dest_dir / "tokenizer_config.json")


def add_to_vocab_(vocab: Dict[str, int], special_tokens: List[str]):
    start = max(vocab.values()) + 1
    added = 0
    for tok in special_tokens:
        if tok in vocab:
            continue
        vocab[tok] = start + added
        added += 1
    return added


def find_vocab_file(model_dir):
    return list(model_dir.glob("*vocab.yml"))[0]


def find_src_vocab_file(model_dir):
    return list(model_dir.glob("*src.vocab.yml"))[0]


def find_tgt_vocab_file(model_dir):
    return list(model_dir.glob("*trg.vocab.yml"))[0]


def add_special_tokens_to_vocab(model_dir: Path, separate_vocab=False) -> None:
    if separate_vocab:
        vocab = load_yaml(find_src_vocab_file(model_dir))
        vocab = {k: int(v) for k, v in vocab.items()}
        num_added = add_to_vocab_(vocab, ["<pad>"])
        save_json(vocab, model_dir / "vocab.json")

        vocab = load_yaml(find_tgt_vocab_file(model_dir))
        vocab = {k: int(v) for k, v in vocab.items()}
        num_added = add_to_vocab_(vocab, ["<pad>"])
        save_json(vocab, model_dir / "target_vocab.json")
        save_tokenizer_config(model_dir, separate_vocabs=separate_vocab)
    else:
        vocab = load_yaml(find_vocab_file(model_dir))
        vocab = {k: int(v) for k, v in vocab.items()}
        num_added = add_to_vocab_(vocab, ["<pad>"])
        print(f"added {num_added} tokens to vocab")
        save_json(vocab, model_dir / "vocab.json")
        save_tokenizer_config(model_dir)


def check_equal(marian_cfg, k1, k2):
    v1, v2 = marian_cfg[k1], marian_cfg[k2]
    if v1 != v2:
        raise ValueError(f"hparams {k1},{k2} differ: {v1} != {v2}")


def check_marian_cfg_assumptions(marian_cfg):
    assumed_settings = {
        "layer-normalization": False,
        "right-left": False,
        "transformer-ffn-depth": 2,
        "transformer-aan-depth": 2,
        "transformer-no-projection": False,
        "transformer-postprocess-emb": "d",
        "transformer-postprocess": "dan",  # Dropout, add, normalize
        "transformer-preprocess": "",
        "type": "transformer",
        "ulr-dim-emb": 0,
        "dec-cell-base-depth": 2,
        "dec-cell-high-depth": 1,
        "transformer-aan-nogate": False,
    }
    for k, v in assumed_settings.items():
        actual = marian_cfg[k]
        if actual != v:
            raise ValueError(f"Unexpected config value for {k} expected {v} got {actual}")


BIAS_KEY = "decoder_ff_logit_out_b"
BART_CONVERTER = {  # for each encoder and decoder layer
    "self_Wq": "self_attn.q_proj.weight",
    "self_Wk": "self_attn.k_proj.weight",
    "self_Wv": "self_attn.v_proj.weight",
    "self_Wo": "self_attn.out_proj.weight",
    "self_bq": "self_attn.q_proj.bias",
    "self_bk": "self_attn.k_proj.bias",
    "self_bv": "self_attn.v_proj.bias",
    "self_bo": "self_attn.out_proj.bias",
    "self_Wo_ln_scale": "self_attn_layer_norm.weight",
    "self_Wo_ln_bias": "self_attn_layer_norm.bias",
    "ffn_W1": "fc1.weight",
    "ffn_b1": "fc1.bias",
    "ffn_W2": "fc2.weight",
    "ffn_b2": "fc2.bias",
    "ffn_ffn_ln_scale": "final_layer_norm.weight",
    "ffn_ffn_ln_bias": "final_layer_norm.bias",
    # Decoder Cross Attention
    "context_Wk": "encoder_attn.k_proj.weight",
    "context_Wo": "encoder_attn.out_proj.weight",
    "context_Wq": "encoder_attn.q_proj.weight",
    "context_Wv": "encoder_attn.v_proj.weight",
    "context_bk": "encoder_attn.k_proj.bias",
    "context_bo": "encoder_attn.out_proj.bias",
    "context_bq": "encoder_attn.q_proj.bias",
    "context_bv": "encoder_attn.v_proj.bias",
    "context_Wo_ln_scale": "encoder_attn_layer_norm.weight",
    "context_Wo_ln_bias": "encoder_attn_layer_norm.bias",
}


class OpusState:
    def __init__(self, source_dir, eos_token_id=0):
        npz_path = find_model_file(source_dir)
        self.state_dict = np.load(npz_path)
        cfg = load_config_from_state_dict(self.state_dict)
        if cfg["dim-vocabs"][0] != cfg["dim-vocabs"][1]:
            raise ValueError
        if "Wpos" in self.state_dict:
            raise ValueError("Wpos key in state dictionary")
        self.state_dict = dict(self.state_dict)
        if cfg["tied-embeddings-all"]:
            cfg["tied-embeddings-src"] = True
            cfg["tied-embeddings"] = True
        self.share_encoder_decoder_embeddings = cfg["tied-embeddings-src"]

        # create the tokenizer here because we need to know the eos_token_id
        self.source_dir = source_dir
        self.tokenizer = self.load_tokenizer()
        # retrieve EOS token and set correctly
        tokenizer_has_eos_token_id = (
            hasattr(self.tokenizer, "eos_token_id") and self.tokenizer.eos_token_id is not None
        )
        eos_token_id = self.tokenizer.eos_token_id if tokenizer_has_eos_token_id else 0

        if cfg["tied-embeddings-src"]:
            self.wemb, self.final_bias = add_emb_entries(self.state_dict["Wemb"], self.state_dict[BIAS_KEY], 1)
            self.pad_token_id = self.wemb.shape[0] - 1
            cfg["vocab_size"] = self.pad_token_id + 1
        else:
            self.wemb, _ = add_emb_entries(self.state_dict["encoder_Wemb"], self.state_dict[BIAS_KEY], 1)
            self.dec_wemb, self.final_bias = add_emb_entries(
                self.state_dict["decoder_Wemb"], self.state_dict[BIAS_KEY], 1
            )
            # still assuming that vocab size is same for encoder and decoder
            self.pad_token_id = self.wemb.shape[0] - 1
            cfg["vocab_size"] = self.pad_token_id + 1
            cfg["decoder_vocab_size"] = self.pad_token_id + 1

        if cfg["vocab_size"] != self.tokenizer.vocab_size:
            raise ValueError(
                f"Original vocab size {cfg['vocab_size']} and new vocab size {len(self.tokenizer.encoder)} mismatched."
            )

        # self.state_dict['Wemb'].sha
        self.state_keys = list(self.state_dict.keys())
        if "Wtype" in self.state_dict:
            raise ValueError("Wtype key in state dictionary")
        self._check_layer_entries()
        self.cfg = cfg
        hidden_size, intermediate_shape = self.state_dict["encoder_l1_ffn_W1"].shape
        if hidden_size != cfg["dim-emb"]:
            raise ValueError(f"Hidden size {hidden_size} and configured size {cfg['dim_emb']} mismatched")

        # Process decoder.yml
        decoder_yml = cast_marian_config(load_yaml(source_dir / "decoder.yml"))
        check_marian_cfg_assumptions(cfg)
        self.hf_config = MarianConfig(
            vocab_size=cfg["vocab_size"],
            decoder_vocab_size=cfg.get("decoder_vocab_size", cfg["vocab_size"]),
            share_encoder_decoder_embeddings=cfg["tied-embeddings-src"],
            decoder_layers=cfg["dec-depth"],
            encoder_layers=cfg["enc-depth"],
            decoder_attention_heads=cfg["transformer-heads"],
            encoder_attention_heads=cfg["transformer-heads"],
            decoder_ffn_dim=cfg["transformer-dim-ffn"],
            encoder_ffn_dim=cfg["transformer-dim-ffn"],
            d_model=cfg["dim-emb"],
            activation_function=cfg["transformer-ffn-activation"],
            pad_token_id=self.pad_token_id,
            eos_token_id=eos_token_id,
            forced_eos_token_id=eos_token_id,
            bos_token_id=0,
            max_position_embeddings=cfg["dim-emb"],
            scale_embedding=True,
            normalize_embedding="n" in cfg["transformer-preprocess"],
            static_position_embeddings=not cfg["transformer-train-position-embeddings"],
            tie_word_embeddings=cfg["tied-embeddings"],
            dropout=0.1,  # see opus-mt-train repo/transformer-dropout param.
            # default: add_final_layer_norm=False,
            num_beams=decoder_yml["beam-size"],
            decoder_start_token_id=self.pad_token_id,
            bad_words_ids=[[self.pad_token_id]],
            max_length=512,
        )

    def _check_layer_entries(self):
        self.encoder_l1 = self.sub_keys("encoder_l1")
        self.decoder_l1 = self.sub_keys("decoder_l1")
        self.decoder_l2 = self.sub_keys("decoder_l2")
        if len(self.encoder_l1) != 16:
            warnings.warn(f"Expected 16 keys for each encoder layer, got {len(self.encoder_l1)}")
        if len(self.decoder_l1) != 26:
            warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")
        if len(self.decoder_l2) != 26:
            warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")

    @property
    def extra_keys(self):
        extra = []
        for k in self.state_keys:
            if (
                k.startswith("encoder_l")
                or k.startswith("decoder_l")
                or k in [CONFIG_KEY, "Wemb", "encoder_Wemb", "decoder_Wemb", "Wpos", "decoder_ff_logit_out_b"]
            ):
                continue
            else:
                extra.append(k)
        return extra

    def sub_keys(self, layer_prefix):
        return [remove_prefix(k, layer_prefix) for k in self.state_dict if k.startswith(layer_prefix)]

    def load_tokenizer(self):
        # save tokenizer
        add_special_tokens_to_vocab(self.source_dir, not self.share_encoder_decoder_embeddings)
        return MarianTokenizer.from_pretrained(str(self.source_dir))

    def load_marian_model(self) -> MarianMTModel:
        state_dict, cfg = self.state_dict, self.hf_config

        if not cfg.static_position_embeddings:
            raise ValueError("config.static_position_embeddings should be True")
        model = MarianMTModel(cfg)

        if "hidden_size" in cfg.to_dict():
            raise ValueError("hidden_size is in config")
        load_layers_(
            model.model.encoder.layers,
            state_dict,
            BART_CONVERTER,
        )
        load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True)

        # handle tensors not associated with layers
        if self.cfg["tied-embeddings-src"]:
            wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
            bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
            model.model.shared.weight = wemb_tensor
            model.model.encoder.embed_tokens = model.model.decoder.embed_tokens = model.model.shared
        else:
            wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
            model.model.encoder.embed_tokens.weight = wemb_tensor

            decoder_wemb_tensor = nn.Parameter(torch.FloatTensor(self.dec_wemb))
            bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
            model.model.decoder.embed_tokens.weight = decoder_wemb_tensor

        # handle tied embeddings, otherwise "from_pretrained" loads them incorrectly
        if self.cfg["tied-embeddings"]:
            model.lm_head.weight.data = model.model.decoder.embed_tokens.weight.data.clone()

        model.final_logits_bias = bias_tensor

        if "Wpos" in state_dict:
            print("Unexpected: got Wpos")
            wpos_tensor = torch.tensor(state_dict["Wpos"])
            model.model.encoder.embed_positions.weight = wpos_tensor
            model.model.decoder.embed_positions.weight = wpos_tensor

        if cfg.normalize_embedding:
            if "encoder_emb_ln_scale_pre" not in state_dict:
                raise ValueError("encoder_emb_ln_scale_pre is not in state dictionary")
            raise NotImplementedError("Need to convert layernorm_embedding")

        if self.extra_keys:
            raise ValueError(f"Failed to convert {self.extra_keys}")

        if model.get_input_embeddings().padding_idx != self.pad_token_id:
            raise ValueError(
                f"Padding tokens {model.get_input_embeddings().padding_idx} and {self.pad_token_id} mismatched"
            )
        return model


def download_and_unzip(url, dest_dir):
    try:
        import wget
    except ImportError:
        raise ImportError("you must pip install wget")

    filename = wget.download(url)
    unzip(filename, dest_dir)
    os.remove(filename)


def convert(source_dir: Path, dest_dir):
    dest_dir = Path(dest_dir)
    dest_dir.mkdir(exist_ok=True)

    opus_state = OpusState(source_dir)

    # save tokenizer
    opus_state.tokenizer.save_pretrained(dest_dir)

    # save_json(opus_state.cfg, dest_dir / "marian_original_config.json")
    # ^^ Uncomment to save human readable marian config for debugging

    model = opus_state.load_marian_model()
    model = model.half()
    model.save_pretrained(dest_dir)
    model.from_pretrained(dest_dir)  # sanity check


def load_yaml(path):
    import yaml

    with open(path, encoding="utf-8") as f:
        return yaml.load(f, Loader=yaml.BaseLoader)


def save_json(content: Union[Dict, List], path: str) -> None:
    with open(path, "w") as f:
        json.dump(content, f)


def unzip(zip_path: str, dest_dir: str) -> None:
    with ZipFile(zip_path, "r") as zipObj:
        zipObj.extractall(dest_dir)


if __name__ == "__main__":
    """
    Tatoeba conversion instructions in scripts/tatoeba/README.md
    """
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("--src", type=str, help="path to marian model sub dir", default="en-de")
    parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model.")
    args = parser.parse_args()

    source_dir = Path(args.src)
    if not source_dir.exists():
        raise ValueError(f"Source directory {source_dir} not found")
    dest_dir = f"converted-{source_dir.name}" if args.dest is None else args.dest
    convert(source_dir, dest_dir)
