# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.


import copy
import numbers
from typing import Any, List, Tuple, Union

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

from modules.general.scaling import ActivationBalancer
from modules.general.scaling  import BasicNorm as _BasicNorm
  

_shape_t = Union[int, List[int], torch.Size]


class LayerNorm(nn.Module):
    __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
    normalized_shape: Tuple[int, ...]
    eps: float
    elementwise_affine: bool

    def __init__(
        self,
        normalized_shape: _shape_t,
        eps: float = 1e-5,
        elementwise_affine: bool = True,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)  
        self.normalized_shape = tuple(normalized_shape) 
        self.eps = eps
        self.elementwise_affine = elementwise_affine
        if self.elementwise_affine:
            self.weight = nn.Parameter(
                torch.empty(self.normalized_shape, **factory_kwargs)
            )
            self.bias = nn.Parameter(
                torch.empty(self.normalized_shape, **factory_kwargs)
            )
        else:
            self.register_parameter("weight", None)
            self.register_parameter("bias", None)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        if self.elementwise_affine:
            nn.init.ones_(self.weight)
            nn.init.zeros_(self.bias)

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            output = F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
            return output, embedding

        assert embedding is None
        return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)


    def extra_repr(self) -> str:
        return (
            "{normalized_shape}, eps={eps}, "
            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
        )


class AdaptiveLayerNorm(nn.Module):
    r"""Adaptive Layer Normalization"""

    def __init__(self, d_model, norm) -> None:
        super(AdaptiveLayerNorm, self).__init__()
        self.project_layer = nn.Linear(d_model, 2 * d_model)
        self.norm = norm
        self.d_model = d_model
        self.eps = self.norm.eps

    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            weight, bias = torch.split(
                self.project_layer(embedding),
                split_size_or_sections=self.d_model,
                dim=-1,
            )
            return (weight * self.norm(input) + bias, embedding)

        weight, bias = torch.split(
            self.project_layer(embedding),
            split_size_or_sections=self.d_model,
            dim=-1,
        )
        return weight * self.norm(input) + bias


class BasicNorm(_BasicNorm):
    def __init__(
        self,
        d_model: int,
        eps: float = 1e-5,
        device=None,
        dtype=None,
    ):
        super(BasicNorm, self).__init__(d_model, eps=eps)

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            return (
                super(BasicNorm, self).forward(input),
                embedding,
            )

        assert embedding is None
        return super(BasicNorm, self).forward(input)


class BalancedBasicNorm(nn.Module):
    def __init__(
        self,
        d_model: int,
        eps: float = 1e-5,
        device=None,
        dtype=None,
    ):
        super(BalancedBasicNorm, self).__init__()
        self.balancer = ActivationBalancer(
            d_model,
            channel_dim=-1,
            min_positive=0.45,
            max_positive=0.55,
            max_abs=6.0,
        )
        self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype)

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            return self.norm((self.balancer(input), embedding))

        assert embedding is None
        return self.norm(self.balancer(input))


class IdentityNorm(nn.Module):
    def __init__(
        self,
        d_model: int,
        eps: float = 1e-5,
        device=None,
        dtype=None,
    ) -> None:
        super(IdentityNorm, self).__init__()

    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
        if isinstance(input, tuple):
            return input

        assert embedding is None
        return input