import math
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F

from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
from ...modeling_outputs import BaseModelOutput, Wav2Vec2BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from ..wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2FeatureProjection,
    Wav2Vec2FeedForward,
    Wav2Vec2ForAudioFrameClassification,
    Wav2Vec2ForCTC,
    Wav2Vec2ForSequenceClassification,
    Wav2Vec2ForXVector,
    Wav2Vec2Model,
    Wav2Vec2PositionalConvEmbedding,
    Wav2Vec2PreTrainedModel,
)
from .configuration_wavlm import WavLMConfig


logger = logging.get_logger(__name__)


class WavLMPositionalConvEmbedding(Wav2Vec2PositionalConvEmbedding):
    pass


class WavLMFeatureProjection(Wav2Vec2FeatureProjection):
    pass


class WavLMAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        num_buckets: int = 320,
        max_distance: int = 800,
        has_relative_position_bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5

        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.out_proj = nn.Linear(embed_dim, embed_dim)

        self.num_buckets = num_buckets
        self.max_distance = max_distance

        self.gru_rel_pos_const = nn.Parameter(torch.ones(1, self.num_heads, 1, 1))
        self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8)

        if has_relative_position_bias:
            self.rel_attn_embed = nn.Embedding(self.num_buckets, self.num_heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_bias: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        index=0,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Attention layer with relative attention"""
        bsz, tgt_len, _ = hidden_states.size()

        # first pass of attention layer creates position bias
        if position_bias is None:
            position_bias = self.compute_bias(tgt_len, tgt_len)
            position_bias = (
                position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, tgt_len)
            )

        # Compute relative position bias:
        # 1) get reshape hidden_states
        gated_hidden_states = hidden_states.view(hidden_states.shape[:-1] + (self.num_heads, -1))
        gated_hidden_states = gated_hidden_states.permute(0, 2, 1, 3)

        # 2) project hidden states
        relative_position_proj = self.gru_rel_pos_linear(gated_hidden_states)
        relative_position_proj = relative_position_proj.view(gated_hidden_states.shape[:-1] + (2, 4)).sum(-1)

        # 3) compute gate for position bias from projected hidden states
        gate_a, gate_b = torch.sigmoid(relative_position_proj).chunk(2, dim=-1)
        gate_output = gate_a * (gate_b * self.gru_rel_pos_const - 1.0) + 2.0

        # 4) apply gate to position bias to compute gated position_bias
        gated_position_bias = gate_output.view(bsz * self.num_heads, -1, 1) * position_bias
        gated_position_bias = gated_position_bias.view((-1, tgt_len, tgt_len))

        attn_output, attn_weights = self.torch_multi_head_self_attention(
            hidden_states, attention_mask, gated_position_bias, output_attentions
        )

        return attn_output, attn_weights, position_bias

    def torch_multi_head_self_attention(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: Union[torch.LongTensor, torch.BoolTensor],
        gated_position_bias: torch.FloatTensor,
        output_attentions: bool,
    ) -> (torch.FloatTensor, torch.FloatTensor):
        """simple wrapper around torch's multi_head_attention_forward function"""
        # self-attention assumes q = k = v
        query = key = value = hidden_states.transpose(0, 1)
        key_padding_mask = attention_mask.ne(1) if attention_mask is not None else None

        # disable bias and add_zero_attn
        bias_k = bias_v = None
        add_zero_attn = False

        # PyTorch 1.3.0 has F.multi_head_attention_forward defined
        # so no problem with backwards compatibility
        attn_output, attn_weights = F.multi_head_attention_forward(
            query,
            key,
            value,
            self.embed_dim,
            self.num_heads,
            torch.empty([0]),
            torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
            bias_k,
            bias_v,
            add_zero_attn,
            self.dropout,
            self.out_proj.weight,
            self.out_proj.bias,
            self.training,
            key_padding_mask,
            output_attentions,
            gated_position_bias,
            use_separate_proj_weight=True,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
        )

        # [Seq_Len, Batch Size, ...] -> [Batch Size, Seq_Len, ...]
        attn_output = attn_output.transpose(0, 1)

        if attn_weights is not None:
            # IMPORTANT: Attention weights are averaged weights
            # here which should not be the case. This is an open issue
            # on PyTorch: https://github.com/pytorch/pytorch/issues/32590
            attn_weights = attn_weights[:, None].broadcast_to(
                attn_weights.shape[:1] + (self.num_heads,) + attn_weights.shape[1:]
            )

        return attn_output, attn_weights

    def compute_bias(self, query_length: int, key_length: int) -> torch.FloatTensor:
        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_positions_bucket(relative_position)
        relative_position_bucket = relative_position_bucket.to(self.rel_attn_embed.weight.device)
        values = self.rel_attn_embed(relative_position_bucket)
        values = values.permute([2, 0, 1])
        return values

    def _relative_positions_bucket(self, relative_positions: torch.FloatTensor) -> torch.FloatTensor:
        num_buckets = self.num_buckets // 2

        relative_buckets = (relative_positions > 0).to(torch.long) * num_buckets
        relative_positions = torch.abs(relative_positions)

        max_exact = num_buckets // 2
        is_small = relative_positions < max_exact

        relative_positions_if_large = torch.log(relative_positions.float() / max_exact)
        relative_positions_if_large = relative_positions_if_large / math.log(self.max_distance / max_exact)
        relative_positions_if_large = relative_positions_if_large * (num_buckets - max_exact)
        relative_position_if_large = (max_exact + relative_positions_if_large).to(torch.long)
        relative_position_if_large = torch.min(
            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
        )

        relative_buckets += torch.where(is_small, relative_positions, relative_position_if_large)
        return relative_buckets


class WavLMFeedForward(Wav2Vec2FeedForward):
    pass


class WavLMEncoderLayer(nn.Module):
    def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
        super().__init__()
        self.attention = WavLMAttention(
            embed_dim=config.hidden_size,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout,
            num_buckets=config.num_buckets,
            max_distance=config.max_bucket_distance,
            has_relative_position_bias=has_relative_position_bias,
        )
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.feed_forward = WavLMFeedForward(config)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, attention_mask=None, position_bias=None, output_attentions=False, index=0):
        attn_residual = hidden_states
        hidden_states, attn_weights, position_bias = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
            index=index,
        )
        hidden_states = self.dropout(hidden_states)
        hidden_states = attn_residual + hidden_states

        hidden_states = self.layer_norm(hidden_states)

        hidden_states = hidden_states + self.feed_forward(hidden_states)
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states, position_bias)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class WavLMEncoderLayerStableLayerNorm(nn.Module):
    def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
        super().__init__()
        self.attention = WavLMAttention(
            embed_dim=config.hidden_size,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout,
            num_buckets=config.num_buckets,
            max_distance=config.max_bucket_distance,
            has_relative_position_bias=has_relative_position_bias,
        )
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.feed_forward = WavLMFeedForward(config)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, attention_mask=None, position_bias=None, output_attentions=False):
        attn_residual = hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states, attn_weights, position_bias = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
        )
        hidden_states = self.dropout(hidden_states)
        hidden_states = attn_residual + hidden_states
        hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))

        outputs = (hidden_states, position_bias)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class WavLMEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layers = nn.ModuleList(
            [WavLMEncoderLayer(config, has_relative_position_bias=(i == 0)) for i in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if attention_mask is not None:
            # make sure padded tokens output 0
            expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
            hidden_states[~expand_attention_mask] = 0

        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
        position_bias = None

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = torch.rand([])

            skip_the_layer = self.training and i > 0 and (dropout_probability < self.config.layerdrop)
            if not skip_the_layer or synced_gpus:
                # under fsdp or deepspeed zero3 all gpus must run in sync
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        layer.__call__,
                        hidden_states,
                        attention_mask,
                        position_bias,
                        output_attentions,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states,
                        attention_mask=attention_mask,
                        position_bias=position_bias,
                        output_attentions=output_attentions,
                        index=i,
                    )

                hidden_states, position_bias = layer_outputs[:2]

            if skip_the_layer:
                layer_outputs = (None, None, None)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class WavLMEncoderStableLayerNorm(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layers = nn.ModuleList(
            [
                WavLMEncoderLayerStableLayerNorm(config, has_relative_position_bias=(i == 0))
                for i in range(config.num_hidden_layers)
            ]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if attention_mask is not None:
            # make sure padded tokens are not attended to
            expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
            hidden_states[~expand_attention_mask] = 0

        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.dropout(hidden_states)

        synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
        position_bias = None

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = torch.rand([])

            skip_the_layer = self.training and i > 0 and (dropout_probability < self.config.layerdrop)
            if not skip_the_layer or synced_gpus:
                # under fsdp or deepspeed zero3 all gpus must run in sync
                # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        layer.__call__,
                        hidden_states,
                        attention_mask,
                        position_bias,
                        output_attentions,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states,
                        attention_mask=attention_mask,
                        output_attentions=output_attentions,
                        position_bias=position_bias,
                    )
                hidden_states, position_bias = layer_outputs[:2]

            if skip_the_layer:
                layer_outputs = (None, None, None)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[2],)

        hidden_states = self.layer_norm(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions
        )


class WavLMGumbelVectorQuantizer(nn.Module):
    """
    Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
    GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
    """

    def __init__(self, config):
        super().__init__()
        self.num_groups = config.num_codevector_groups
        self.num_vars = config.num_codevectors_per_group

        if config.codevector_dim % self.num_groups != 0:
            raise ValueError(
                f"`config.codevector_dim {config.codevector_dim} must be divisible"
                f" by `config.num_codevector_groups` {self.num_groups} "
                "for concatenation."
            )

        # storage for codebook variables (codewords)
        self.codevectors = nn.Parameter(
            torch.FloatTensor(1, self.num_groups * self.num_vars, config.codevector_dim // self.num_groups)
        )
        self.weight_proj = nn.Linear(config.conv_dim[-1], self.num_groups * self.num_vars)

        # can be decayed for training
        self.temperature = 2

    @staticmethod
    def _compute_perplexity(probs):
        marginal_probs = probs.mean(dim=0)
        perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
        return perplexity

    def forward(self, hidden_states):
        batch_size, sequence_length, hidden_size = hidden_states.shape

        # project to codevector dim
        hidden_states = self.weight_proj(hidden_states)
        hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1)

        if self.training:
            # sample code vector probs via gumbel in differentiateable way
            codevector_probs = nn.functional.gumbel_softmax(hidden_states.float(), tau=self.temperature, hard=True)
            codevector_probs = codevector_probs.type_as(hidden_states)

            # compute perplexity
            codevector_soft_dist = torch.softmax(
                hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1
            )
            perplexity = self._compute_perplexity(codevector_soft_dist)
        else:
            # take argmax in non-differentiable way
            # comptute hard codevector distribution (one hot)
            codevector_idx = hidden_states.argmax(dim=-1)
            codevector_probs = hidden_states.new_zeros(*hidden_states.shape).scatter_(
                -1, codevector_idx.view(-1, 1), 1.0
            )
            codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1)

            perplexity = self._compute_perplexity(codevector_probs)

        codevector_probs = codevector_probs.view(batch_size * sequence_length, -1)
        # use probs to retrieve codevectors
        codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors
        codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
        codevectors = codevectors.sum(-2).view(batch_size, sequence_length, -1)

        return codevectors, perplexity


class WavLMPreTrainedModel(PreTrainedModel, Wav2Vec2PreTrainedModel):
    config_class = WavLMConfig
    base_model_prefix = "wavlm"
    main_input_name = "input_values"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = False
    _supports_sdpa = False

    def _init_weights(self, module):
        """Initialize the weights"""
        # gumbel softmax requires special init
        if isinstance(module, WavLMGumbelVectorQuantizer):
            module.weight_proj.weight.data.normal_(mean=0.0, std=1)
            module.weight_proj.bias.data.zero_()
            nn.init.uniform_(module.codevectors)
        elif isinstance(module, WavLMPositionalConvEmbedding):
            nn.init.normal_(
                module.conv.weight,
                mean=0,
                std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)),
            )
            nn.init.constant_(module.conv.bias, 0)
        elif isinstance(module, WavLMFeatureProjection):
            k = math.sqrt(1 / module.projection.in_features)
            nn.init.uniform_(module.projection.weight, a=-k, b=k)
            nn.init.uniform_(module.projection.bias, a=-k, b=k)
        elif isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)

            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Conv1d):
            nn.init.kaiming_normal_(module.weight)

            if module.bias is not None:
                k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
                nn.init.uniform_(module.bias, a=-k, b=k)

    def _get_adapters(self):
        raise AttributeError("Not needed for WavLM")

    def init_adapter_layers(self):
        raise AttributeError("Not needed for WavLM")

    def load_adapter(self):
        raise AttributeError("Not needed for WavLM")


WavLMBaseModelOutput = Wav2Vec2BaseModelOutput


class WavLMModel(Wav2Vec2Model):
    pass


class WavLMForCTC(Wav2Vec2ForCTC):
    pass


class WavLMForSequenceClassification(Wav2Vec2ForSequenceClassification):
    pass


class WavLMForAudioFrameClassification(Wav2Vec2ForAudioFrameClassification):
    pass


class WavLMForXVector(Wav2Vec2ForXVector):
    pass


__all__ = [
    "WavLMForAudioFrameClassification",
    "WavLMForCTC",
    "WavLMForSequenceClassification",
    "WavLMForXVector",
    "WavLMModel",
    "WavLMPreTrainedModel",
]