from torch.nn import (
    Module,
    Embedding,
    Dropout,
    ModuleDict,
    LayerNorm,
    ModuleList,
    Linear,
    GELU,
    functional,
)
from torch.nn.init import normal_, zeros_
from dataclasses import dataclass
from rotary_embedding_torch import RotaryEmbedding
from torch import ones, cat
from torch.nn.functional import scaled_dot_product_attention
import torch.nn.functional as F
from math import sqrt

@dataclass
class NBAConfig:
    players_per_team: int = None
    player_tokens: int = None
    age_tokens: int = None
    n_layer: int = None
    n_head: int = None
    n_embd: int = None
    dropout: float = None
    seed: int = None
    bias: bool = None
    dtype: type = None
    num_labels: int = None

class SelfAttention(Module):

    def __init__(self, config):

        block_size = config.players_per_team * 2 + 1

        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = Linear(config.n_embd, 3 * config.n_embd, bias=config.bias, dtype=config.dtype)
        self.c_proj = Linear(config.n_embd, config.n_embd, bias=config.bias, dtype=config.dtype)
        self.attn_dropout = Dropout(config.dropout)
        self.resid_dropout = Dropout(config.dropout)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.dropout = config.dropout
        self.rotary_emb = RotaryEmbedding(config.n_embd)
        self.flash = hasattr(functional, 'scaled_dot_product_attention')
        if not self.flash:
            self.register_buffer("bias", ones(block_size, block_size)
                                 ).view(1, 1, block_size, block_size)

    def forward(self, x):
        B, T, C = x.size()

        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)

        q = self.rotary_emb.rotate_queries_or_keys(q)
        k = self.rotary_emb.rotate_queries_or_keys(k)

        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)

        if self.flash:
            y = scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=False)
        else:
            att = (q @ k.transpose(-2, -1)) * (1.0 / sqrt(k.size(-1)))
            att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
            att = F.softmax(att, dim=-1)
            att = self.attn_dropout(att)
            y = att @ v
        y = y.transpose(1, 2).contiguous().view(B, T, C)

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y

class MLP(Module):

    def __init__(self, config):
        super().__init__()
        self.c_fc = Linear(config.n_embd, 4 * config.n_embd, bias=config.bias, dtype=config.dtype)
        self.gelu = GELU()
        self.c_proj = Linear(4 * config.n_embd, config.n_embd, bias=config.bias, dtype=config.dtype)
        self.dropout = Dropout(config.dropout)

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x

class Block(Module):

    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias, dtype=config.dtype)
        self.attn = SelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias, dtype=config.dtype)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        return x + self.mlp(self.ln_2(x))

class NBAModel(Module):

    def __init__(self, config) -> None:
        super().__init__()

        self.config = config

        self.transformer = ModuleDict(dict(
            home_player_embeddings = Embedding(config.player_tokens, config.n_embd, dtype=config.dtype),
            away_player_embeddings = Embedding(config.player_tokens, config.n_embd, dtype=config.dtype),
            home_age_embeddings = Embedding(config.age_tokens, config.n_embd, dtype=config.dtype),
            away_age_embeddings = Embedding(config.age_tokens, config.n_embd, dtype=config.dtype),
            drop = Dropout(config.dropout),
            h = ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = LayerNorm(config.n_embd, bias=config.bias, dtype=config.dtype),
        ))

        self.head = Linear(config.n_embd, config.num_labels, dtype=config.dtype)

        self.apply(self._init_weights)
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                normal_(p, mean=0.0, std=0.02/sqrt(2 * config.n_layer))

    def _init_weights(self, module):
        if isinstance(module, Linear):
            normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                zeros_(module.bias)
        elif isinstance(module, Embedding):
            normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, **batch):
        home_player_tokens = batch['home_player_tokens']
        away_player_tokens = batch['away_player_tokens']
        home_age_tokens = batch['home_age_tokens']
        away_age_tokens = batch['away_age_tokens']

        home_player_embeddings = self.transformer.home_player_embeddings(home_player_tokens)
        away_player_embeddings = self.transformer.away_player_embeddings(away_player_tokens)

        home_age_embeddings = self.transformer.home_age_embeddings(home_age_tokens)
        away_age_embeddings = self.transformer.away_age_embeddings(away_age_tokens)

        home_emb = home_player_embeddings + home_age_embeddings
        away_emb = away_player_embeddings + away_age_embeddings

        x = cat([home_emb, away_emb], dim=1)

        x = self.transformer.drop(x)

        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)

        logits = self.head(x)
        logits = logits[:, 0]

        loss = None
        if 'home_team_won' in batch:
            loss =  F.cross_entropy(logits, batch['home_net_score_token'])
            loss = {'loss': loss}

        return logits, loss