import io
import os
import sys
import struct
import json
import code
import torch
import numpy as np

from transformers import WhisperForConditionalGeneration

conv_map = {'self_attn_layer_norm': 'attn_ln',
 'encoder_attn.k_proj': 'attn.key',
 'self_attn.out_proj': 'attn.out',
 'encoder_attn.out_proj': 'cross_attn.out',
 'self_attn.q_proj': 'attn.query',
 'encoder_attn.q_proj': 'cross_attn.query',
 'self_attn.v_proj': 'attn.value',
 'encoder_attn.v_proj': 'cross_attn.value',
 'encoder_attn_layer_norm': 'cross_attn_ln',
 'fc1': 'mlp.0',
 'fc2': 'mlp.2',
 'final_layer_norm': 'mlp_ln',
 'encoder.layer_norm.bias': 'encoder.ln_post.bias',
 'encoder.layer_norm.weight': 'encoder.ln_post.weight',
 'encoder.embed_positions.weight': 'encoder.positional_embedding',
 'decoder.layer_norm.bias': 'decoder.ln.bias',
 'decoder.layer_norm.weight': 'decoder.ln.weight',
 'decoder.embed_positions.weight': 'decoder.positional_embedding',
 'decoder.embed_tokens.weight': 'decoder.token_embedding.weight',
}

# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))

if len(sys.argv) < 4:
    print("Usage: convert-h5-to-ggml.py dir_model path-to-whisper-repo dir-output [use-f32]\n")
    sys.exit(1)

dir_model   = sys.argv[1]
dir_whisper = sys.argv[2]
dir_out     = sys.argv[3]

with open(dir_model + "/vocab.json", "r") as f:
    encoder = json.load(f)
with open(dir_model + "/added_tokens.json", "r") as f:
    encoder_added = json.load(f)
with open(dir_model + "/config.json", "r") as f:
    hparams = json.load(f)

model = WhisperForConditionalGeneration.from_pretrained(dir_model)

#code.interact(local=locals())

n_mels = hparams["num_mel_bins"]
with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
    filters = torch.from_numpy(f[f"mel_{n_mels}"])

dir_tokenizer = dir_model

fname_out = dir_out + "/ggml-model.bin"

with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
    tokens = json.load(f)


use_f16 = True

fout = open(fname_out, "wb")

fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["max_source_positions"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
fout.write(struct.pack("i", hparams["decoder_layers"]))
fout.write(struct.pack("i", hparams["max_length"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
fout.write(struct.pack("i", hparams["encoder_layers"]))
fout.write(struct.pack("i", hparams["num_mel_bins"]))
fout.write(struct.pack("i", use_f16))

fout.write(struct.pack("i", filters.shape[0]))
fout.write(struct.pack("i", filters.shape[1]))
for i in range(filters.shape[0]):
    for j in range(filters.shape[1]):
        fout.write(struct.pack("f", filters[i][j]))

byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}

fout.write(struct.pack("i", len(tokens)))

tokens = sorted(tokens.items(), key=lambda x: x[1])
for key in tokens:
    text = bytearray([byte_decoder[c] for c in key[0]])
    fout.write(struct.pack("i", len(text)))
    fout.write(text)

list_vars = model.state_dict()
for name in list_vars.keys():
    if name == "proj_out.weight":
        print('Skipping', name)
        continue

    src = name

    nn = name
    nn = nn.split(".")[1:]
    if nn[1] == "layers":
        nn[1] = "blocks"
        if ".".join(nn[3:-1]) == "self_attn.k_proj":
            mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
        else:
            mapped = conv_map[".".join(nn[3:-1])]
        name = ".".join(nn[:3] + [mapped] + nn[-1:])
    else:
        name = ".".join(nn)
        name = conv_map[name] if name in conv_map else name

    print(src, ' -> ', name)
    data = list_vars[src].squeeze().numpy()
    data = data.astype(np.float16)

    # reshape conv bias from [n] to [n, 1]
    if name == "encoder.conv1.bias" or \
       name == "encoder.conv2.bias":
        data = data.reshape(data.shape[0], 1)
        print("  Reshaped variable: " + name + " to shape: ", data.shape)

    n_dims = len(data.shape)
    print(name, n_dims, data.shape)

    # looks like the whisper models are in f16 by default
    # so we need to convert the small tensors to f32 until we fully support f16 in ggml
    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype = 1;
    if use_f16:
        if n_dims < 2 or \
                name == "encoder.conv1.bias"   or \
                name == "encoder.conv2.bias"   or \
                name == "encoder.positional_embedding" or \
                name == "decoder.positional_embedding":
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype = 0
    else:
        data = data.astype(np.float32)
        ftype = 0

    # header
    str = name.encode('utf-8')
    fout.write(struct.pack("iii", n_dims, len(str), ftype))
    for i in range(n_dims):
        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
    fout.write(str);

    # data
    data.tofile(fout)

fout.close()

print("Done. Output file: " + fname_out)
print("")