# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import contextlib
import copy
import logging
import math
from argparse import Namespace
from dataclasses import dataclass, field
from typing import Any, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from omegaconf import II, MISSING, open_dict

from fairseq import checkpoint_utils, tasks, utils
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models import (
    BaseFairseqModel,
    FairseqEncoder,
    FairseqEncoderDecoderModel,
    FairseqIncrementalDecoder,
    register_model,
)
from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES
from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer
from fairseq.tasks import FairseqTask

logger = logging.getLogger(__name__)


@dataclass
class HubertAsrConfig(FairseqDataclass):
    w2v_path: str = field(default=MISSING, metadata={"help": "path to hubert model"})
    no_pretrained_weights: bool = field(
        default=False,
        metadata={"help": "if true, does not load pretrained weights"},
    )
    dropout_input: float = field(
        default=0.0,
        metadata={"help": "dropout to apply to the input (after feat extr)"},
    )
    final_dropout: float = field(
        default=0.0,
        metadata={"help": "dropout after transformer and before final projection"},
    )
    dropout: float = field(
        default=0.0,
        metadata={"help": "dropout probability inside hubert model"},
    )
    attention_dropout: float = field(
        default=0.0,
        metadata={
            "help": "dropout probability for attention weights " "inside hubert model"
        },
    )
    activation_dropout: float = field(
        default=0.0,
        metadata={
            "help": "dropout probability after activation in FFN " "inside hubert model"
        },
    )
    encoder_embed_dim: Optional[int] = field(
        default=768, metadata={"help": "encoder embedding dimension"}
    )

    # masking
    apply_mask: bool = field(
        default=False, metadata={"help": "apply masking during fine-tuning"}
    )
    mask_length: int = field(
        default=10, metadata={"help": "repeat the mask indices multiple times"}
    )
    mask_prob: float = field(
        default=0.5,
        metadata={
            "help": "probability of replacing a token with mask "
            "(normalized by length)"
        },
    )
    mask_selection: MASKING_DISTRIBUTION_CHOICES = field(
        default="static", metadata={"help": "how to choose masks"}
    )
    mask_other: float = field(
        default=0,
        metadata={
            "help": "secondary mask argument "
            "(used for more complex distributions), "
            "see help in compute_mask_indices"
        },
    )
    no_mask_overlap: bool = field(
        default=False, metadata={"help": "whether to allow masks to overlap"}
    )

    # channel masking
    mask_channel_length: int = field(
        default=10,
        metadata={"help": "length of the mask for features (channels)"},
    )
    mask_channel_prob: float = field(
        default=0.0,
        metadata={"help": "probability of replacing a feature with 0"},
    )
    mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field(
        default="static",
        metadata={"help": "how to choose mask length for channel masking"},
    )
    mask_channel_other: float = field(
        default=0,
        metadata={
            "help": "secondary mask argument "
            "(used for more complex distributions), "
            "see help in compute_mask_indices"
        },
    )
    no_mask_channel_overlap: bool = field(
        default=False,
        metadata={"help": "whether to allow channel masks to overlap"},
    )
    freeze_finetune_updates: int = field(
        default=0,
        metadata={"help": "dont finetune hubert for this many updates"},
    )
    feature_grad_mult: float = field(
        default=0.0,
        metadata={"help": "reset feature grad mult in hubert to this"},
    )
    layerdrop: float = field(
        default=0.0,
        metadata={"help": "probability of dropping a layer in hubert"},
    )
    normalize: bool = II("task.normalize")
    data: str = II("task.data")

    # this holds the loaded hubert args
    w2v_args: Any = None


@dataclass
class HubertCtcConfig(HubertAsrConfig):
    pass


@register_model("hubert_ctc", dataclass=HubertCtcConfig)
class HubertCtc(BaseFairseqModel):
    def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel):
        super().__init__()
        self.cfg = cfg
        self.w2v_encoder = w2v_encoder

    def upgrade_state_dict_named(self, state_dict, name):
        super().upgrade_state_dict_named(state_dict, name)
        return state_dict

    @classmethod
    def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask):
        """Build a new model instance."""
        w2v_encoder = HubertEncoder(cfg, task)
        return cls(cfg, w2v_encoder)

    def get_normalized_probs(self, net_output, log_probs):
        """Get normalized probabilities (or log probs) from a net's output."""

        logits = net_output["encoder_out"]
        if log_probs:
            return utils.log_softmax(logits.float(), dim=-1)
        else:
            return utils.softmax(logits.float(), dim=-1)

    def get_logits(self, net_output):
        logits = net_output["encoder_out"]
        padding = net_output["encoder_padding_mask"]
        if padding is not None and padding.any():
            padding = padding.T
            logits[padding][..., 0] = 0
            logits[padding][..., 1:] = float("-inf")

        return logits

    def forward(self, **kwargs):
        x = self.w2v_encoder(**kwargs)
        return x


@dataclass
class HubertSeq2SeqConfig(HubertAsrConfig):
    decoder_embed_dim: int = field(
        default=768, metadata={"help": "decoder embedding dimension"}
    )
    decoder_ffn_embed_dim: int = field(
        default=3072, metadata={"help": "decoder embedding dimension for FFN"}
    )
    decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"})
    decoder_layerdrop: float = field(
        default=0.0, metadata={"help": "decoder layerdrop chance"}
    )
    decoder_attention_heads: int = field(
        default=4, metadata={"help": "num decoder attention heads"}
    )
    decoder_learned_pos: bool = field(
        default=False,
        metadata={"help": "use learned positional embeddings in the decoder"},
    )
    decoder_normalize_before: bool = field(
        default=False, metadata={"help": "apply layernorm before each decoder block"}
    )
    no_token_positional_embeddings: bool = field(
        default=False,
        metadata={
            "help": "if set, disables positional embeddings (outside self attention)"
        },
    )
    decoder_dropout: float = field(
        default=0.0, metadata={"help": "dropout probability in the decoder"}
    )
    decoder_attention_dropout: float = field(
        default=0.0,
        metadata={
            "help": "dropout probability for attention weights inside the decoder"
        },
    )
    decoder_activation_dropout: float = field(
        default=0.0,
        metadata={
            "help": "dropout probability after activation in FFN inside the decoder"
        },
    )
    max_target_positions: int = field(
        default=2048, metadata={"help": "max target positions"}
    )
    share_decoder_input_output_embed: bool = field(
        default=False, metadata={"help": "share decoder input and output embeddings"}
    )
    autoregressive: bool = II("task.autoregressive")
    seq2seq_path: str = field(
        default="",
        metadata={"help": "reset_dict"},
    )
    reset_dict: bool = field(
        default=False,
        metadata={"help": "reset_dict"},
    )


@register_model("hubert_seq2seq", dataclass=HubertSeq2SeqConfig)
class HubertSeq2SeqModel(FairseqEncoderDecoderModel):
    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)

    @classmethod
    def build_model(cls, cfg: HubertSeq2SeqConfig, task: FairseqTask):
        """Build a new model instance."""

        assert (
            cfg.autoregressive
        ), "Please set task.autoregressive=true for seq2seq asr models"

        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary

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

        decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim)

        encoder = cls.build_encoder(cfg, task)
        decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens)

        model = HubertSeq2SeqModel(encoder, decoder)

        if cfg["seq2seq_path"]:
            state = checkpoint_utils.load_checkpoint_to_cpu(cfg.seq2seq_path)
            state = state["model"]
            if cfg["reset_dict"]:
                del state["decoder.embed_out"]
                del state["decoder.embed_tokens.weight"]
            model.load_state_dict(state, strict=False)
        return model

    @classmethod
    def build_encoder(cls, cfg: HubertAsrConfig, task):
        return HubertEncoder(cfg, task)

    @classmethod
    def build_decoder(cls, cfg: HubertSeq2SeqConfig, tgt_dict, embed_tokens):
        return TransformerDecoder(cfg, tgt_dict, embed_tokens)

    def forward(self, **kwargs):
        encoder_out = self.encoder(**kwargs)
        decoder_out = self.decoder(encoder_out=encoder_out, **kwargs)
        return decoder_out

    def upgrade_state_dict_named(self, state_dict, name):
        return state_dict

    def load_state_dict(
        self,
        state_dict,
        strict=True,
        model_cfg=None,
        args: Optional[Namespace] = None,
    ):
        if model_cfg.reset_dict:
            logger.warn("Overriding loading strict state dict!")
            del state_dict["decoder.embed_out"]
            del state_dict["decoder.embed_tokens.weight"]
            return super().load_state_dict(state_dict, False, model_cfg, args)
        return super().load_state_dict(state_dict, strict, model_cfg, args)


class HubertEncoder(FairseqEncoder):
    def __init__(self, cfg: HubertAsrConfig, task):
        self.apply_mask = cfg.apply_mask

        arg_overrides = {
            "dropout": cfg.dropout,
            "activation_dropout": cfg.activation_dropout,
            "dropout_input": cfg.dropout_input,
            "attention_dropout": cfg.attention_dropout,
            "mask_length": cfg.mask_length,
            "mask_prob": cfg.mask_prob,
            "mask_selection": cfg.mask_selection,
            "mask_other": cfg.mask_other,
            "no_mask_overlap": cfg.no_mask_overlap,
            "mask_channel_length": cfg.mask_channel_length,
            "mask_channel_prob": cfg.mask_channel_prob,
            "mask_channel_selection": cfg.mask_channel_selection,
            "mask_channel_other": cfg.mask_channel_other,
            "no_mask_channel_overlap": cfg.no_mask_channel_overlap,
            "encoder_layerdrop": cfg.layerdrop,
            "feature_grad_mult": cfg.feature_grad_mult,
        }

        if cfg.w2v_args is None:
            state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
            w2v_args = state.get("cfg", None)
            if w2v_args is None:
                w2v_args = convert_namespace_to_omegaconf(state["args"])
            cfg.w2v_args = w2v_args
        else:
            state = None
            w2v_args = cfg.w2v_args
            if isinstance(w2v_args, Namespace):
                cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args)

        assert cfg.normalize == w2v_args.task.normalize, (
            "Fine-tuning works best when data normalization is the same. "
            "Please check that --normalize is set or unset for "
            "both pre-training and here"
        )

        w2v_args.task.data = cfg.data
        pretrain_task = tasks.setup_task(w2v_args.task)
        if state is not None and "task_state" in state:
            # This will load the stored "dictionaries" object
            pretrain_task.load_state_dict(state["task_state"])
        else:
            pretrain_task.load_state_dict(task.state_dict())

        model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True)
        if state is not None and not cfg.no_pretrained_weights:
            # set strict=False because we omit some modules
            model.load_state_dict(state["model"], strict=False)

        model.remove_pretraining_modules()

        super().__init__(pretrain_task.source_dictionary)

        d = w2v_args.model.encoder_embed_dim

        self.w2v_model = model

        self.final_dropout = nn.Dropout(cfg.final_dropout)
        self.freeze_finetune_updates = cfg.freeze_finetune_updates
        self.num_updates = 0

        if task.target_dictionary is not None and not cfg.autoregressive:
            self.proj = Linear(d, len(task.target_dictionary))
        elif getattr(cfg, "decoder_embed_dim", d) != d:
            self.proj = Linear(d, cfg.decoder_embed_dim)
        else:
            self.proj = None

    def set_num_updates(self, num_updates):
        """Set the number of parameters updates."""
        super().set_num_updates(num_updates)
        self.num_updates = num_updates

    def forward(self, source, padding_mask, tbc=True, **kwargs):

        w2v_args = {
            "source": source,
            "padding_mask": padding_mask,
            "mask": self.apply_mask and self.training,
        }

        ft = self.freeze_finetune_updates <= self.num_updates

        with torch.no_grad() if not ft else contextlib.ExitStack():
            x, padding_mask = self.w2v_model.extract_features(**w2v_args)

            if tbc:
                # B x T x C -> T x B x C
                x = x.transpose(0, 1)

        x = self.final_dropout(x)

        if self.proj:
            x = self.proj(x)

        return {
            "encoder_out": x,  # T x B x C
            "encoder_padding_mask": padding_mask,  # B x T
            "padding_mask": padding_mask,
        }

    def reorder_encoder_out(self, encoder_out, new_order):
        if encoder_out["encoder_out"] is not None:
            encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
                1, new_order
            )
        if encoder_out["encoder_padding_mask"] is not None:
            encoder_out["encoder_padding_mask"] = encoder_out[
                "encoder_padding_mask"
            ].index_select(0, new_order)
        if encoder_out["padding_mask"] is not None:
            encoder_out["padding_mask"] = encoder_out["padding_mask"].index_select(
                0, new_order
            )
        return encoder_out

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        return None

    def upgrade_state_dict_named(self, state_dict, name):
        return state_dict


class TransformerDecoder(FairseqIncrementalDecoder):
    """
    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
    is a :class:`TransformerDecoderLayer`.

    Args:
        args (argparse.Namespace): parsed command-line arguments
        dictionary (~fairseq.data.Dictionary): decoding dictionary
        embed_tokens (torch.nn.Embedding): output embedding
        no_encoder_attn (bool, optional): whether to attend to encoder outputs
            (default: False).
    """

    def __init__(
        self,
        cfg: HubertSeq2SeqConfig,
        dictionary,
        embed_tokens,
        no_encoder_attn=False,
    ):
        super().__init__(dictionary)

        self.dropout = cfg.decoder_dropout
        self.share_input_output_embed = cfg.share_decoder_input_output_embed

        input_embed_dim = embed_tokens.embedding_dim
        embed_dim = cfg.decoder_embed_dim
        self.output_embed_dim = cfg.decoder_embed_dim

        self.layerdrop = cfg.decoder_layerdrop

        self.padding_idx = embed_tokens.padding_idx
        self.max_target_positions = cfg.max_target_positions

        self.embed_tokens = embed_tokens
        self.embed_scale = math.sqrt(embed_dim)  # todo: try with input_embed_dim

        self.project_in_dim = (
            Linear(input_embed_dim, embed_dim, bias=False)
            if embed_dim != input_embed_dim
            else None
        )

        self.embed_positions = (
            PositionalEmbedding(
                cfg.max_target_positions,
                embed_dim,
                self.padding_idx,
                learned=cfg.decoder_learned_pos,
            )
            if not cfg.no_token_positional_embeddings
            else None
        )

        # TODO: update this when transformer gets converted to dataclass configs
        transformer_cfg = copy.deepcopy(cfg)
        with open_dict(transformer_cfg):
            transformer_cfg.dropout = transformer_cfg.decoder_dropout
            transformer_cfg.attention_dropout = (
                transformer_cfg.decoder_attention_dropout
            )
            transformer_cfg.activation_dropout = (
                transformer_cfg.decoder_activation_dropout
            )

        self.layers = nn.ModuleList([])
        self.layers.extend(
            [
                TransformerDecoderLayer(transformer_cfg, no_encoder_attn)
                for _ in range(transformer_cfg.decoder_layers)
            ]
        )

        if not self.share_input_output_embed:
            self.embed_out = nn.Parameter(
                torch.Tensor(len(dictionary), self.output_embed_dim)
            )
            nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim**-0.5)

        if transformer_cfg.decoder_normalize_before:
            self.layer_norm = LayerNorm(embed_dim)
        else:
            self.layer_norm = None

    def forward(
        self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
    ):
        """
        Args:
            prev_output_tokens (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for teacher forcing
            encoder_out (Tensor, optional): output from the encoder, used for
                encoder-side attention
            incremental_state (dict): dictionary used for storing state during
                :ref:`Incremental decoding`

        Returns:
            tuple:
                - the decoder's output of shape `(batch, tgt_len, vocab)`
                - a dictionary with any model-specific outputs
        """
        if type(prev_output_tokens) == list:
            max_len = max((len(x) for x in prev_output_tokens))
            tmp = torch.zeros(
                [len(prev_output_tokens), max_len], device=prev_output_tokens[0].device
            )
            for (i, p) in enumerate(prev_output_tokens):
                tmp[i, : len(p)] = p
            prev_output_tokens = tmp
        prev_output_tokens = prev_output_tokens.long()
        x, extra = self.extract_features(
            prev_output_tokens, encoder_out, incremental_state
        )
        x = self.output_layer(x)
        return x, extra

    def extract_features(
        self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused
    ):
        """
        Similar to *forward* but only return features.

        Returns:
            tuple:
                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                - a dictionary with any model-specific outputs
        """

        # embed positions
        positions = (
            self.embed_positions(
                prev_output_tokens, incremental_state=incremental_state
            )
            if self.embed_positions is not None
            else None
        )

        if incremental_state is not None:
            prev_output_tokens = prev_output_tokens[:, -1:]
            if positions is not None:
                positions = positions[:, -1:]

        # embed tokens and positions
        x = self.embed_scale * self.embed_tokens(prev_output_tokens)

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

        if positions is not None:
            x += positions
        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)
        attn = None

        inner_states = [x]

        # decoder layers
        self_attn_padding_mask = None
        if prev_output_tokens.eq(self.padding_idx).any():
            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
        for layer in self.layers:
            dropout_probability = np.random.random()
            if not self.training or (dropout_probability > self.layerdrop):
                x, attn, _ = layer(
                    x,
                    encoder_out["encoder_out"] if encoder_out is not None else None,
                    encoder_out["padding_mask"] if encoder_out is not None else None,
                    incremental_state,
                    self_attn_mask=self.buffered_future_mask(x)
                    if incremental_state is None
                    else None,
                    self_attn_padding_mask=self_attn_padding_mask,
                )
                inner_states.append(x)

        if self.layer_norm:
            x = self.layer_norm(x)

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        return x, {"attn": attn, "inner_states": inner_states}

    def output_layer(self, features, **kwargs):
        """Project features to the vocabulary size."""
        # project back to size of vocabulary
        if self.share_input_output_embed:
            return F.linear(features, self.embed_tokens.weight)
        else:
            return F.linear(features, self.embed_out)

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        if self.embed_positions is None:
            return self.max_target_positions
        return min(self.max_target_positions, self.embed_positions.max_positions)

    def buffered_future_mask(self, tensor):
        dim = tensor.size(0)
        if (
            not hasattr(self, "_future_mask")
            or self._future_mask is None
            or self._future_mask.device != tensor.device
            or self._future_mask.size(0) < dim
        ):
            self._future_mask = torch.triu(
                utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
            )
        return self._future_mask[:dim, :dim]

    def upgrade_state_dict_named(self, state_dict, name):
        return state_dict


def Embedding(num_embeddings, embedding_dim, padding_idx):
    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
    nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)
    nn.init.constant_(m.weight[padding_idx], 0)
    return m


def Linear(in_features, out_features, bias=True):
    m = nn.Linear(in_features, out_features, bias)
    nn.init.xavier_uniform_(m.weight)
    if bias:
        nn.init.constant_(m.bias, 0.0)
    return m