#!/usr/bin/env python3

import logging
import math
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn
from torch import Tensor

from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
    FairseqEncoder,
    FairseqEncoderDecoderModel,
    register_model,
    register_model_architecture,
)
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.transformer import Embedding, TransformerDecoder
from fairseq.modules import (
    FairseqDropout,
    LayerNorm,
    PositionalEmbedding,
    TransformerEncoderLayer,
)

logger = logging.getLogger(__name__)


class Conv1dSubsampler(nn.Module):
    """Convolutional subsampler: a stack of 1D convolution (along temporal
    dimension) followed by non-linear activation via gated linear units
    (https://arxiv.org/abs/1911.08460)

    Args:
        in_channels (int): the number of input channels
        mid_channels (int): the number of intermediate channels
        out_channels (int): the number of output channels
        kernel_sizes (List[int]): the kernel size for each convolutional layer
    """

    def __init__(
        self,
        in_channels: int,
        mid_channels: int,
        out_channels: int,
        kernel_sizes: List[int] = (3, 3),
    ):
        super(Conv1dSubsampler, self).__init__()
        self.n_layers = len(kernel_sizes)
        self.conv_layers = nn.ModuleList(
            nn.Conv1d(
                in_channels if i == 0 else mid_channels // 2,
                mid_channels if i < self.n_layers - 1 else out_channels * 2,
                k,
                stride=2,
                padding=k // 2,
            )
            for i, k in enumerate(kernel_sizes)
        )

    def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
        out = in_seq_lens_tensor.clone()
        for _ in range(self.n_layers):
            out = ((out.float() - 1) / 2 + 1).floor().long()
        return out

    def forward(self, src_tokens, src_lengths):
        bsz, in_seq_len, _ = src_tokens.size()  # B x T x (C x D)
        x = src_tokens.transpose(1, 2).contiguous()  # -> B x (C x D) x T
        for conv in self.conv_layers:
            x = conv(x)
            x = nn.functional.glu(x, dim=1)
        _, _, out_seq_len = x.size()
        x = x.transpose(1, 2).transpose(0, 1).contiguous()  # -> T x B x (C x D)
        return x, self.get_out_seq_lens_tensor(src_lengths)


@register_model("s2t_transformer")
class S2TTransformerModel(FairseqEncoderDecoderModel):
    """Adapted Transformer model (https://arxiv.org/abs/1706.03762) for
    speech-to-text tasks. The Transformer encoder/decoder remains the same.
    A trainable input subsampler is prepended to the Transformer encoder to
    project inputs into the encoder dimension as well as downsample input
    sequence for computational efficiency."""

    @classmethod
    def hub_models(cls):
        base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t"
        model_ids = [
            "s2t_transformer_s-en-asr-librispeech",
            "s2t_transformer_m-en-asr-librispeech",
            "s2t_transformer_l-en-asr-librispeech",
        ]
        return {i: f"{base_url}/{i}.tar.gz" for i in model_ids}

    @classmethod
    def from_pretrained(
        cls,
        model_name_or_path,
        checkpoint_file="model.pt",
        data_name_or_path=".",
        config_yaml="config.yaml",
        **kwargs,
    ):
        from fairseq import hub_utils

        x = hub_utils.from_pretrained(
            model_name_or_path,
            checkpoint_file,
            data_name_or_path,
            archive_map=cls.hub_models(),
            config_yaml=config_yaml,
            **kwargs,
        )
        return S2THubInterface(x["args"], x["task"], x["models"][0])

    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        # input
        parser.add_argument(
            "--conv-kernel-sizes",
            type=str,
            metavar="N",
            help="kernel sizes of Conv1d subsampling layers",
        )
        parser.add_argument(
            "--conv-channels",
            type=int,
            metavar="N",
            help="# of channels in Conv1d subsampling layers",
        )
        # Transformer
        parser.add_argument(
            "--activation-fn",
            type=str,
            default="relu",
            choices=utils.get_available_activation_fns(),
            help="activation function to use",
        )
        parser.add_argument(
            "--dropout", type=float, metavar="D", help="dropout probability"
        )
        parser.add_argument(
            "--attention-dropout",
            type=float,
            metavar="D",
            help="dropout probability for attention weights",
        )
        parser.add_argument(
            "--activation-dropout",
            "--relu-dropout",
            type=float,
            metavar="D",
            help="dropout probability after activation in FFN.",
        )
        parser.add_argument(
            "--encoder-embed-dim",
            type=int,
            metavar="N",
            help="encoder embedding dimension",
        )
        parser.add_argument(
            "--encoder-ffn-embed-dim",
            type=int,
            metavar="N",
            help="encoder embedding dimension for FFN",
        )
        parser.add_argument(
            "--encoder-layers", type=int, metavar="N", help="num encoder layers"
        )
        parser.add_argument(
            "--encoder-attention-heads",
            type=int,
            metavar="N",
            help="num encoder attention heads",
        )
        parser.add_argument(
            "--encoder-normalize-before",
            action="store_true",
            help="apply layernorm before each encoder block",
        )
        parser.add_argument(
            "--decoder-embed-dim",
            type=int,
            metavar="N",
            help="decoder embedding dimension",
        )
        parser.add_argument(
            "--decoder-ffn-embed-dim",
            type=int,
            metavar="N",
            help="decoder embedding dimension for FFN",
        )
        parser.add_argument(
            "--decoder-layers", type=int, metavar="N", help="num decoder layers"
        )
        parser.add_argument(
            "--decoder-attention-heads",
            type=int,
            metavar="N",
            help="num decoder attention heads",
        )
        parser.add_argument(
            "--decoder-normalize-before",
            action="store_true",
            help="apply layernorm before each decoder block",
        )
        parser.add_argument(
            "--share-decoder-input-output-embed",
            action="store_true",
            help="share decoder input and output embeddings",
        )
        parser.add_argument(
            "--layernorm-embedding",
            action="store_true",
            help="add layernorm to embedding",
        )
        parser.add_argument(
            "--no-scale-embedding",
            action="store_true",
            help="if True, dont scale embeddings",
        )
        parser.add_argument(
            "--load-pretrained-encoder-from",
            type=str,
            metavar="STR",
            help="model to take encoder weights from (for initialization)",
        )
        parser.add_argument(
            "--encoder-freezing-updates",
            type=int,
            metavar="N",
            help="freeze encoder for first N updates",
        )

    @classmethod
    def build_encoder(cls, args):
        encoder = S2TTransformerEncoder(args)
        pretraining_path = getattr(args, "load_pretrained_encoder_from", None)
        if pretraining_path is not None:
            if not Path(pretraining_path).exists():
                logger.warning(
                    f"skipped pretraining because {pretraining_path} does not exist"
                )
            else:
                encoder = checkpoint_utils.load_pretrained_component_from_model(
                    component=encoder, checkpoint=pretraining_path
                )
                logger.info(f"loaded pretrained encoder from: {pretraining_path}")
        return encoder

    @classmethod
    def build_decoder(cls, args, task, embed_tokens):
        return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens)

    @classmethod
    def build_model(cls, args, task):
        """Build a new model instance."""

        # make sure all arguments are present in older models
        base_architecture(args)

        def build_embedding(dictionary, embed_dim):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            return Embedding(num_embeddings, embed_dim, padding_idx)

        decoder_embed_tokens = build_embedding(
            task.target_dictionary, args.decoder_embed_dim
        )
        encoder = cls.build_encoder(args)
        decoder = cls.build_decoder(args, task, decoder_embed_tokens)
        return cls(encoder, decoder)

    def get_normalized_probs(
        self,
        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
        log_probs: bool,
        sample: Optional[Dict[str, Tensor]] = None,
    ):
        # net_output['encoder_out'] is a (B, T, D) tensor
        lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
        lprobs.batch_first = True
        return lprobs

    def forward(self, src_tokens, src_lengths, prev_output_tokens):
        """
        The forward method inherited from the base class has a **kwargs
        argument in its input, which is not supported in torchscript. This
        method overwrites the forward method definition without **kwargs.
        """
        encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
        decoder_out = self.decoder(
            prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
        )
        return decoder_out


class S2TTransformerEncoder(FairseqEncoder):
    """Speech-to-text Transformer encoder that consists of input subsampler and
    Transformer encoder."""

    def __init__(self, args):
        super().__init__(None)

        self.encoder_freezing_updates = args.encoder_freezing_updates
        self.num_updates = 0

        self.dropout_module = FairseqDropout(
            p=args.dropout, module_name=self.__class__.__name__
        )
        self.embed_scale = math.sqrt(args.encoder_embed_dim)
        if args.no_scale_embedding:
            self.embed_scale = 1.0
        self.padding_idx = 1

        self.subsample = Conv1dSubsampler(
            args.input_feat_per_channel * args.input_channels,
            args.conv_channels,
            args.encoder_embed_dim,
            [int(k) for k in args.conv_kernel_sizes.split(",")],
        )

        self.embed_positions = PositionalEmbedding(
            args.max_source_positions, args.encoder_embed_dim, self.padding_idx
        )

        self.transformer_layers = nn.ModuleList(
            [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)]
        )
        if args.encoder_normalize_before:
            self.layer_norm = LayerNorm(args.encoder_embed_dim)
        else:
            self.layer_norm = None

    def _forward(self, src_tokens, src_lengths, return_all_hiddens=False):
        x, input_lengths = self.subsample(src_tokens, src_lengths)
        x = self.embed_scale * x

        encoder_padding_mask = lengths_to_padding_mask(input_lengths)
        positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
        x += positions
        x = self.dropout_module(x)

        encoder_states = []

        for layer in self.transformer_layers:
            x = layer(x, encoder_padding_mask)
            if return_all_hiddens:
                encoder_states.append(x)

        if self.layer_norm is not None:
            x = self.layer_norm(x)

        return {
            "encoder_out": [x],  # T x B x C
            "encoder_padding_mask": [encoder_padding_mask]
            if encoder_padding_mask.any()
            else [],  # B x T
            "encoder_embedding": [],  # B x T x C
            "encoder_states": encoder_states,  # List[T x B x C]
            "src_tokens": [],
            "src_lengths": [],
        }

    def forward(self, src_tokens, src_lengths, return_all_hiddens=False):
        if self.num_updates < self.encoder_freezing_updates:
            with torch.no_grad():
                x = self._forward(
                    src_tokens, src_lengths, return_all_hiddens=return_all_hiddens
                )
        else:
            x = self._forward(
                src_tokens, src_lengths, return_all_hiddens=return_all_hiddens
            )
        return x

    def reorder_encoder_out(self, encoder_out, new_order):
        new_encoder_out = (
            []
            if len(encoder_out["encoder_out"]) == 0
            else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
        )

        new_encoder_padding_mask = (
            []
            if len(encoder_out["encoder_padding_mask"]) == 0
            else [
                x.index_select(0, new_order)
                for x in encoder_out["encoder_padding_mask"]
            ]
        )

        new_encoder_embedding = (
            []
            if len(encoder_out["encoder_embedding"]) == 0
            else [
                x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
            ]
        )

        encoder_states = encoder_out["encoder_states"]
        if len(encoder_states) > 0:
            for idx, state in enumerate(encoder_states):
                encoder_states[idx] = state.index_select(1, new_order)

        return {
            "encoder_out": new_encoder_out,  # T x B x C
            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
            "encoder_embedding": new_encoder_embedding,  # B x T x C
            "encoder_states": encoder_states,  # List[T x B x C]
            "src_tokens": [],  # B x T
            "src_lengths": [],  # B x 1
        }

    def set_num_updates(self, num_updates):
        super().set_num_updates(num_updates)
        self.num_updates = num_updates


class TransformerDecoderScriptable(TransformerDecoder):
    def extract_features(
        self,
        prev_output_tokens,
        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        full_context_alignment: bool = False,
        alignment_layer: Optional[int] = None,
        alignment_heads: Optional[int] = None,
    ):
        # call scriptable method from parent class
        x, _ = self.extract_features_scriptable(
            prev_output_tokens,
            encoder_out,
            incremental_state,
            full_context_alignment,
            alignment_layer,
            alignment_heads,
        )
        return x, None


@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer")
def base_architecture(args):
    args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0)
    # Convolutional subsampler
    args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
    args.conv_channels = getattr(args, "conv_channels", 1024)
    # Transformer
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
    args.encoder_layers = getattr(args, "encoder_layers", 12)
    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
    args.decoder_ffn_embed_dim = getattr(
        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
    )
    args.decoder_layers = getattr(args, "decoder_layers", 6)
    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
    args.dropout = getattr(args, "dropout", 0.1)
    args.attention_dropout = getattr(args, "attention_dropout", args.dropout)
    args.activation_dropout = getattr(args, "activation_dropout", args.dropout)
    args.activation_fn = getattr(args, "activation_fn", "relu")
    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
    args.share_decoder_input_output_embed = getattr(
        args, "share_decoder_input_output_embed", False
    )
    args.no_token_positional_embeddings = getattr(
        args, "no_token_positional_embeddings", False
    )
    args.adaptive_input = getattr(args, "adaptive_input", False)
    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
    args.decoder_output_dim = getattr(
        args, "decoder_output_dim", args.decoder_embed_dim
    )
    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)


@register_model_architecture("s2t_transformer", "s2t_transformer_s")
def s2t_transformer_s(args):
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8)
    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
    args.dropout = getattr(args, "dropout", 0.1)
    base_architecture(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_xs")
def s2t_transformer_xs(args):
    args.encoder_layers = getattr(args, "encoder_layers", 6)
    args.decoder_layers = getattr(args, "decoder_layers", 3)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4)
    args.dropout = getattr(args, "dropout", 0.3)
    s2t_transformer_s(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_sp")
def s2t_transformer_sp(args):
    args.encoder_layers = getattr(args, "encoder_layers", 16)
    s2t_transformer_s(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_m")
def s2t_transformer_m(args):
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4)
    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
    args.dropout = getattr(args, "dropout", 0.15)
    base_architecture(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_mp")
def s2t_transformer_mp(args):
    args.encoder_layers = getattr(args, "encoder_layers", 16)
    s2t_transformer_m(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_l")
def s2t_transformer_l(args):
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4)
    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
    args.dropout = getattr(args, "dropout", 0.2)
    base_architecture(args)


@register_model_architecture("s2t_transformer", "s2t_transformer_lp")
def s2t_transformer_lp(args):
    args.encoder_layers = getattr(args, "encoder_layers", 16)
    s2t_transformer_l(args)
