import torch
import torch.nn as nn
import base64
import gzip
import warnings
from tqdm import tqdm
from dataclasses import dataclass
from typing import Dict, Iterable, Optional, Union
import os
import urllib
import hashlib
import numpy as np

import torch.nn.functional as F
from torch import Tensor

from whisper.decoding import decode as decode_function
from whisper.transcribe import transcribe as transcribe_function


@dataclass
class ModelDimensions:
    n_mels: int
    n_audio_ctx: int
    n_audio_state: int
    n_audio_head: int
    n_audio_layer: int
    n_vocab: int
    n_text_ctx: int
    n_text_state: int
    n_text_head: int
    n_text_layer: int


class LayerNorm(nn.LayerNorm):
    def forward(self, x: Tensor) -> Tensor:
        return super().forward(x.float()).type(x.dtype)


class Linear(nn.Linear):
    def forward(self, x: Tensor) -> Tensor:
        return F.linear(
            x,
            self.weight.to(x.dtype),
            None if self.bias is None else self.bias.to(x.dtype),
        )


class Conv1d(nn.Conv1d):
    def _conv_forward(
        self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
    ) -> Tensor:
        return super()._conv_forward(
            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
        )


def sinusoids(length, channels, max_timescale=10000):
    """Returns sinusoids for positional embedding"""
    assert channels % 2 == 0
    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)


class MultiHeadAttention(nn.Module):
    def __init__(self, n_state: int, n_head: int):
        super().__init__()
        self.n_head = n_head
        self.query = Linear(n_state, n_state)
        self.key = Linear(n_state, n_state, bias=False)
        self.value = Linear(n_state, n_state)
        self.out = Linear(n_state, n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
    ):
        q = self.query(x)

        if kv_cache is None or xa is None or self.key not in kv_cache:
            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
            # otherwise, perform key/value projections for self- or cross-attention as usual.
            k = self.key(x if xa is None else xa)
            v = self.value(x if xa is None else xa)
        else:
            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
            k = kv_cache[self.key]
            v = kv_cache[self.value]

        wv, qk = self.qkv_attention(q, k, v, mask)
        return self.out(wv), qk

    def qkv_attention(
        self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
    ):
        n_batch, n_ctx, n_state = q.shape
        scale = (n_state // self.n_head) ** -0.25
        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)

        qk = q @ k
        if mask is not None:
            qk = qk + mask[:n_ctx, :n_ctx]
        qk = qk.float()

        w = F.softmax(qk, dim=-1).to(q.dtype)
        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()


class ResidualAttentionBlock(nn.Module):
    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
        super().__init__()

        self.attn = MultiHeadAttention(n_state, n_head)
        self.attn_ln = LayerNorm(n_state)

        self.cross_attn = (
            MultiHeadAttention(n_state, n_head) if cross_attention else None
        )
        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None

        n_mlp = n_state * 4
        self.mlp = nn.Sequential(
            Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
        )
        self.mlp_ln = LayerNorm(n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
    ):
        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
        if self.cross_attn:
            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
        x = x + self.mlp(self.mlp_ln(x))
        return x


class AudioEncoder(nn.Module):
    def __init__(
        self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
    ):
        super().__init__()
        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))

        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
        )
        self.ln_post = LayerNorm(n_state)

    def forward(self, x: Tensor):
        """
        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
            the mel spectrogram of the audio
        """
        x = F.gelu(self.conv1(x))
        x = F.gelu(self.conv2(x))
        x = x.permute(0, 2, 1)

        # change whisper to process audio with any length
        x = (x + self.positional_embedding[:x.shape[1],:]).to(x.dtype)

        for block in self.blocks:
            x = block(x)

        x = self.ln_post(x)
        return x


class TextDecoder(nn.Module):
    def __init__(
        self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
    ):
        super().__init__()

        self.token_embedding = nn.Embedding(n_vocab, n_state)
        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))

        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
            [
                ResidualAttentionBlock(n_state, n_head, cross_attention=True)
                for _ in range(n_layer)
            ]
        )
        self.ln = LayerNorm(n_state)

        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
        self.register_buffer("mask", mask, persistent=False)

    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
        """
        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
            the text tokens
        xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
            the encoded audio features to be attended on
        """
        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
        x = (
            self.token_embedding(x)
            + self.positional_embedding[offset : offset + x.shape[-1]]
        )
        x = x.to(xa.dtype)

        for block in self.blocks:
            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)

        x = self.ln(x)
        logits = (
            x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
        ).float()

        return logits

class Whisper(nn.Module):
    def __init__(self, dims: ModelDimensions):
        super().__init__()
        self.dims = dims
        self.encoder = AudioEncoder(
            self.dims.n_mels,
            self.dims.n_audio_ctx,
            self.dims.n_audio_state,
            self.dims.n_audio_head,
            self.dims.n_audio_layer,
        )
        self.decoder = TextDecoder(
            self.dims.n_vocab,
            self.dims.n_text_ctx,
            self.dims.n_text_state,
            self.dims.n_text_head,
            self.dims.n_text_layer,
        )
        # use the last half layers for alignment by default; see `set_alignment_heads()` below
        all_heads = torch.zeros(
            self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
        )
        all_heads[self.dims.n_text_layer // 2 :] = True
        self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)

    def set_alignment_heads(self, dump: bytes):
        array = np.frombuffer(
            gzip.decompress(base64.b85decode(dump)), dtype=bool
        ).copy()
        mask = torch.from_numpy(array).reshape(
            self.dims.n_text_layer, self.dims.n_text_head
        )
        self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)

    def embed_audio(self, mel: torch.Tensor):
        return self.encoder(mel)

    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
        return self.decoder(tokens, audio_features)

    def forward(
        self, mel: torch.Tensor, tokens: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        return self.decoder(tokens, self.encoder(mel))

    @property
    def device(self):
        return next(self.parameters()).device

    @property
    def is_multilingual(self):
        return self.dims.n_vocab >= 51865

    @property
    def num_languages(self):
        return self.dims.n_vocab - 51765 - int(self.is_multilingual)

    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
        """
        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
        tensors calculated for the previous positions. This method returns a dictionary that stores
        all caches, and the necessary hooks for the key and value projection modules that save the
        intermediate tensors to be reused during later calculations.

        Returns
        -------
        cache : Dict[nn.Module, torch.Tensor]
            A dictionary object mapping the key/value projection modules to its cache
        hooks : List[RemovableHandle]
            List of PyTorch RemovableHandle objects to stop the hooks to be called
        """
        cache = {**cache} if cache is not None else {}
        hooks = []

        def save_to_cache(module, _, output):
            if module not in cache or output.shape[1] > self.dims.n_text_ctx:
                # save as-is, for the first token or cross attention
                cache[module] = output
            else:
                cache[module] = torch.cat([cache[module], output], dim=1).detach()
            return cache[module]

        def install_hooks(layer: nn.Module):
            if isinstance(layer, MultiHeadAttention):
                hooks.append(layer.key.register_forward_hook(save_to_cache))
                hooks.append(layer.value.register_forward_hook(save_to_cache))

        self.decoder.apply(install_hooks)
        return cache, hooks

    transcribe = transcribe_function
    decode = decode_function

_MODELS = {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
    "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}

def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
    os.makedirs(root, exist_ok=True)

    expected_sha256 = url.split("/")[-2]
    download_target = os.path.join(root, os.path.basename(url))

    if os.path.exists(download_target) and not os.path.isfile(download_target):
        raise RuntimeError(f"{download_target} exists and is not a regular file")

    if os.path.isfile(download_target):
        with open(download_target, "rb") as f:
            model_bytes = f.read()
        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
            return model_bytes if in_memory else download_target
        else:
            warnings.warn(
                f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
            )

    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
        with tqdm(
            total=int(source.info().get("Content-Length")),
            ncols=80,
            unit="iB",
            unit_scale=True,
            unit_divisor=1024,
        ) as loop:
            while True:
                buffer = source.read(8192)
                if not buffer:
                    break

                output.write(buffer)
                loop.update(len(buffer))

    model_bytes = open(download_target, "rb").read()
    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
        raise RuntimeError(
            "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
        )

    return model_bytes if in_memory else download_target

def load_model(
    name: str,
    device: Optional[Union[str, torch.device]] = 'cpu',
    download_root: str = None,
    in_memory: bool = False,
) -> Whisper:
    """
    Load a Whisper ASR model

    Parameters
    ----------
    name : str
        one of the official model names listed by `whisper.available_models()`, or
        path to a model checkpoint containing the model dimensions and the model state_dict.
    device : Union[str, torch.device]
        the PyTorch device to put the model into
    download_root: str
        path to download the model files; by default, it uses "~/.cache/whisper"
    in_memory: bool
        whether to preload the model weights into host memory

    Returns
    -------
    model : Whisper
        The Whisper ASR model instance
    """

    # if device is None:
    #     device = "cuda" if torch.cuda.is_available() else "cpu"
    if download_root is None:
        default = os.path.join(os.path.expanduser("~"), ".cache")
        download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")

    if name in _MODELS:
        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
        # alignment_heads = _ALIGNMENT_HEADS[name]
        alignment_heads = None
    elif os.path.isfile(name):
        checkpoint_file = open(name, "rb").read() if in_memory else name
        alignment_heads = None
    else:
        raise RuntimeError(
            f"Model {name} not found; available models = {available_models()}"
        )

    with (
        io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
    ) as fp:
        checkpoint = torch.load(fp, map_location=device)
    del checkpoint_file

    dims = ModelDimensions(**checkpoint["dims"])
    model = Whisper(dims)
    model.load_state_dict(checkpoint["model_state_dict"])

    return model.to(device)