import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import math


class PositionalEmbedding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEmbedding, self).__init__()
        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]


class TokenEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(TokenEmbedding, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_in', nonlinearity='leaky_relu')

    def forward(self, x):
        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
        return x


class FixedEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(FixedEmbedding, self).__init__()

        w = torch.zeros(c_in, d_model).float()
        w.require_grad = False

        position = torch.arange(0, c_in).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        w[:, 0::2] = torch.sin(position * div_term)
        w[:, 1::2] = torch.cos(position * div_term)

        self.emb = nn.Embedding(c_in, d_model)
        self.emb.weight = nn.Parameter(w, requires_grad=False)

    def forward(self, x):
        return self.emb(x).detach()


class TemporalEmbedding(nn.Module):
    def __init__(self, d_model, embed_type='fixed', freq='h'):
        super(TemporalEmbedding, self).__init__()

        minute_size = 4
        hour_size = 24
        weekday_size = 7
        day_size = 32
        month_size = 13

        Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
        if freq == 't':
            self.minute_embed = Embed(minute_size, d_model)
        self.hour_embed = Embed(hour_size, d_model)
        self.weekday_embed = Embed(weekday_size, d_model)
        self.day_embed = Embed(day_size, d_model)
        self.month_embed = Embed(month_size, d_model)

    def forward(self, x):
        x = x.long()
        minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
            self, 'minute_embed') else 0.
        hour_x = self.hour_embed(x[:, :, 3])
        weekday_x = self.weekday_embed(x[:, :, 2])
        day_x = self.day_embed(x[:, :, 1])
        month_x = self.month_embed(x[:, :, 0])

        return hour_x + weekday_x + day_x + month_x + minute_x


class TimeFeatureEmbedding(nn.Module):
    def __init__(self, d_model, embed_type='timeF', freq='h'):
        super(TimeFeatureEmbedding, self).__init__()

        freq_map = {'h': 4, 't': 5, 's': 6,
                    'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
        d_inp = freq_map[freq]
        self.embed = nn.Linear(d_inp, d_model, bias=False)

    def forward(self, x):
        return self.embed(x)


class SubjectEmbedding(nn.Module):
    def __init__(self, num_subjects, d_model):
        super(SubjectEmbedding, self).__init__()
        self.subject_embedding = nn.Embedding(num_subjects, d_model)
        self.shared_embedding = nn.Parameter(torch.randn(1, d_model))  # Shared token for unknown subjects
        self.mask_embedding = nn.Parameter(torch.randn(1, d_model))  # Mask token embedding

    def forward(self, subject_ids):
        if subject_ids[0] is None or torch.any(subject_ids >= self.subject_embedding.num_embeddings):
            batch_size = subject_ids.size(0)
            return self.shared_embedding.expand(batch_size, 1, -1)
        else:
            return self.subject_embedding(subject_ids).unsqueeze(1)

        
# class DataEmbedding(nn.Module):
#     def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1, num_subjects=None):
#         super(DataEmbedding, self).__init__()
#         self.value_embedding = nn.Linear(c_in, d_model)
#         self.position_embedding = PositionalEmbedding(d_model=d_model)
#         self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
#         self.dropout = nn.Dropout(p=dropout)
#         self.subject_embedding = SubjectEmbedding(num_subjects, d_model) if num_subjects is not None else None
#         self.mask_token = nn.Parameter(torch.randn(1, d_model))  # Mask token embedding

#     def forward(self, x, x_mark, subject_ids=None, mask=None):
#         if x_mark is None:
#             x = self.value_embedding(x) 
#         else:
#             x = self.value_embedding(x) + self.temporal_embedding(x_mark) + self.position_embedding(x)

#         if mask is not None:
#             x = x * (~mask.bool()) + self.mask_token * mask.float()

#         if self.subject_embedding is not None:
#             subject_emb = self.subject_embedding(subject_ids)  # (batch_size, 1, d_model)
#             x = torch.cat([subject_emb, x], dim=1)  # 在序列维度上拼接 (batch_size, seq_len + 1, d_model)

#         return self.dropout(x)

class DataEmbedding(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1, joint_train=False, num_subjects=None):
        super(DataEmbedding, self).__init__()
        if joint_train and num_subjects is not None:
            self.value_embedding = nn.ModuleDict({
                str(subject_id): nn.Linear(c_in, d_model) for subject_id in range(num_subjects)
            })
        else:
            self.value_embedding = nn.Linear(c_in, d_model)  # 如果没有指定subjects，则使用单一的value embedding

        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
        self.dropout = nn.Dropout(p=dropout)
        self.subject_embedding = SubjectEmbedding(num_subjects, d_model) if num_subjects is not None else None
        self.mask_token = nn.Parameter(torch.randn(1, d_model))  # Mask token embedding
        self.joint_train = joint_train
        
    def forward(self, x, x_mark, subject_ids=None, mask=None):
        if self.joint_train:
            # 使用针对每个subject的特定value embedding
            x = torch.stack([self.value_embedding[str(subject_id.item())](x[i]) for i, subject_id in enumerate(subject_ids)])
        else:
            x = self.value_embedding(x)

        if x_mark is not None:
            x = x + self.temporal_embedding(x_mark) + self.position_embedding(x)

        if mask is not None:
            x = x * (~mask.bool()) + self.mask_token * mask.float()

        if self.subject_embedding is not None:
            subject_emb = self.subject_embedding(subject_ids)  # (batch_size, 1, d_model)
            x = torch.cat([subject_emb, x], dim=1)  # 在序列维度上拼接 (batch_size, seq_len + 1, d_model)

        return self.dropout(x)




class DataEmbedding_inverted(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding_inverted, self).__init__()
        self.value_embedding = nn.Linear(c_in, d_model)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        x = x.permute(0, 2, 1)
        # x: [Batch Variate Time]
        if x_mark is None:
            x = self.value_embedding(x)
        else:
            x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
        # x: [Batch Variate d_model]
        return self.dropout(x)


class DataEmbedding_wo_pos(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding_wo_pos, self).__init__()

        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
                                                    freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
            d_model=d_model, embed_type=embed_type, freq=freq)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        if x_mark is None:
            x = self.value_embedding(x) + self.position_embedding(x)
        else:
            x = self.value_embedding(x) + self.temporal_embedding(x_mark)
        return self.dropout(x)


class PatchEmbedding(nn.Module):
    def __init__(self, d_model, patch_len, stride, padding, dropout):
        super(PatchEmbedding, self).__init__()
        # Patching
        self.patch_len = patch_len
        self.stride = stride
        self.padding_patch_layer = nn.ReplicationPad1d((0, padding))

        # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
        self.value_embedding = nn.Linear(patch_len, d_model, bias=False)

        # Positional embedding
        self.position_embedding = PositionalEmbedding(d_model)

        # Residual dropout
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # do patching
        n_vars = x.shape[1]
        x = self.padding_patch_layer(x)
        x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
        x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
        # Input encoding
        x = self.value_embedding(x) + self.position_embedding(x)
        return self.dropout(x), n_vars