# coding=utf-8
# Adapted from
# https://github.com/THUDM/GLM-4
"""Inference-only GLM-4v model visual encoder compatible with THUDM weights."""
from argparse import Namespace
from typing import Optional

import torch
from torch import nn
from torch.nn import LayerNorm

from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)


class PatchEmbedding(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.proj = nn.Conv2d(config.in_channels,
                              config.hidden_size,
                              kernel_size=config.patch_size,
                              stride=config.patch_size)
        self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
        self.position_embedding = nn.Embedding(config.num_positions,
                                               config.hidden_size)

    def forward(self, images: torch.Tensor) -> torch.Tensor:
        """
        Parameters:
        images : torch.Tensor
            Input image tensor with shape (B, C, H, W)

        Returns:
        torch.Tensor
            Transformed tensor with shape (B, L, D)
        """
        images = images.to(self.proj.weight.device)
        x = self.proj(images)
        x = x.flatten(2).transpose(1, 2)
        cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_token, x), dim=1)
        x += self.position_embedding.weight.unsqueeze(0)
        return x


class Attention(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_heads_per_rank = config.num_heads // self.tp_size
        self.head_dim = config.hidden_size // config.num_heads
        self.scale = self.head_dim**-0.5

        self.query_key_value = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            config.num_heads,
            quant_config=quant_config,
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            quant_config=quant_config,
        )

        self.output_dropout = torch.nn.Dropout(config.dropout_prob)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, L, _ = x.shape
        qkv, _ = self.query_key_value(x)  # B, L, 3 * H * D
        q, k, v = qkv.chunk(3, dim=-1)
        q = q.reshape(B, L, self.num_heads_per_rank,
                      self.head_dim).permute(0, 2, 1, 3)  # B, H, L, D
        k = k.reshape(B, L, self.num_heads_per_rank,
                      self.head_dim).permute(0, 2, 1, 3)  # B, H, L, D
        v = v.reshape(B, L, self.num_heads_per_rank,
                      self.head_dim).permute(0, 2, 1, 3)  # B, H, L, D

        out = torch.nn.functional.scaled_dot_product_attention(q,
                                                               k,
                                                               v,
                                                               attn_mask=None,
                                                               dropout_p=0.,
                                                               is_causal=False)

        output, _ = self.dense(out.transpose(1, 2).contiguous().view(B, L, -1))
        output = self.output_dropout(output)
        return output


class MLP(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.activation_fn = get_act_fn(config.hidden_act)
        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config,
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.fc1(x)
        x = self.activation_fn(x)
        x, _ = self.fc2(x)
        return x


class TransformerLayer(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.input_layernorm = LayerNorm(config.hidden_size,
                                         eps=config.layer_norm_eps)
        self.attention = Attention(config, quant_config=quant_config)
        self.mlp = MLP(config, quant_config=quant_config)
        self.post_attention_layernorm = LayerNorm(config.hidden_size,
                                                  eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        attention_input = hidden_states
        attention_output = self.input_layernorm(
            self.attention(attention_input))
        hidden_states = attention_input + attention_output
        mlp_input = hidden_states
        mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
        output = mlp_input + mlp_output
        return output


class Transformer(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.layers = nn.ModuleList([
            TransformerLayer(config, quant_config=quant_config)
            for _ in range(config.num_hidden_layers)
        ])

    def forward(self, hidden_states):
        for layer_module in self.layers:
            hidden_states = layer_module(hidden_states)
        return hidden_states


class GLU(nn.Module):

    def __init__(
        self,
        config,
        in_features,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        """
        The original implementation is the same as:
        ```python
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.ffn_hidden_size,
            bias=False,
            quant_config=quant_config
        )

        self.gate_proj = ColumnParallelLinear(
            config.hidden_size,
            config.ffn_hidden_size,
            bias=False,
            quant_config=quant_config
        )
        ```
        ```
        gate_proj_output, _ = self.gate_proj(x)
        dense_h_to_4h_output, _ = self.dense_h_to_4h(x)
        x = torch.cat([gate_proj_output, dense_h_to_4h_output], dim=-1)
        ```

        We merge two ColumnParallelLinear into one MergedColumnParallelLinear:
        ```
        self.merged_proj = MergedColumnParallelLinear(
            config.hidden_size,
            [config.ffn_hidden_size] * 2,
            bias=False,
            quant_config=quant_config
        )
        ```
        ```
        x, _ = self.merged_proj(x)
        ```
        """
        super().__init__()
        self.linear_proj = ReplicatedLinear(in_features,
                                            config.hidden_size,
                                            bias=False,
                                            quant_config=quant_config)
        self.norm1 = nn.LayerNorm(config.hidden_size)
        self.act1 = nn.GELU()
        self.act2 = SiluAndMul()

        self.merged_proj = MergedColumnParallelLinear(
            config.hidden_size, [config.ffn_hidden_size] * 2,
            bias=False,
            quant_config=quant_config)

        self.dense_4h_to_h = RowParallelLinear(config.ffn_hidden_size,
                                               config.hidden_size,
                                               bias=False,
                                               quant_config=quant_config)

    def forward(self, x):
        x, _ = self.linear_proj(x)
        x = self.act1(self.norm1(x))
        x, _ = self.merged_proj(x)
        x = self.act2(x)
        x, _ = self.dense_4h_to_h(x)
        return x


class EVA2CLIPModel(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        vision_config = Namespace(**config.vision_config)
        self.patch_embedding = PatchEmbedding(vision_config)
        self.transformer = Transformer(vision_config,
                                       quant_config=quant_config)
        self.linear_proj = GLU(config,
                               in_features=config.hidden_size,
                               quant_config=quant_config)
        self.conv = nn.Conv2d(in_channels=vision_config.hidden_size,
                              out_channels=config.hidden_size,
                              kernel_size=2,
                              stride=2)
        self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.scaling_factor = vision_config.scaling_factor

    def forward(self, images: torch.Tensor) -> torch.Tensor:
        """
        Parameters:
        images : torch.Tensor
            Input image tensor with shape (B, C, H, W)

        Returns:
        torch.Tensor
            Transformed tensor with shape (B, L, D)
        """
        x = self.patch_embedding(images)
        x = self.transformer(x)
        x = x[:, 1:]

        b, s, h = x.shape
        grid_size = int(s**0.5)
        x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)
        x = self.conv(x)

        x = x.flatten(2).transpose(1, 2)
        x = self.linear_proj(x)
        boi = self.boi.expand(x.shape[0], -1, -1)
        eoi = self.eoi.expand(x.shape[0], -1, -1)
        x = torch.cat((boi, x, eoi), dim=1)
        x = x / self.scaling_factor
        return x