# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Qwen2-VL model."""

import math
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import LayerNorm

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from .configuration_qwen2_vl import Qwen2VLVisionConfig


if is_flash_attn_available():
    from transformers.modeling_flash_attention_utils import _flash_attention_forward, flash_attn_varlen_func

if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask

    from transformers.integrations.flex_attention import make_flex_block_causal_mask


logger = logging.get_logger(__name__)


# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb_vision(
    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    orig_q_dtype = q.dtype
    orig_k_dtype = k.dtype
    q, k = q.float(), k.float()
    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    q_embed = q_embed.to(orig_q_dtype)
    k_embed = k_embed.to(orig_k_dtype)
    return q_embed, k_embed


class VisionRotaryEmbedding(nn.Module):
    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, seqlen: int) -> torch.Tensor:
        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(seq, self.inv_freq)
        return freqs


class PatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        in_channels: int = 3,
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.in_channels = in_channels
        self.embed_dim = embed_dim

        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        target_dtype = self.proj.weight.dtype
        hidden_states = hidden_states.view(
            -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
        )
        hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
        return hidden_states


class PatchMerger(nn.Module):
    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        self.ln_q = LayerNorm(context_dim, eps=1e-6)
        self.mlp = nn.Sequential(
            nn.Linear(self.hidden_size, self.hidden_size),
            nn.GELU(),
            nn.Linear(self.hidden_size, dim),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
        return x


class VisionMlp(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, hidden_act: str) -> None:
        super().__init__()
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.act = ACT2FN[hidden_act]
        self.fc2 = nn.Linear(hidden_dim, dim)

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


class VisionAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: Optional[torch.Tensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        if position_embeddings is None:
            logger.warning_once(
                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
                "removed and `position_embeddings` will be mandatory."
            )
            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        else:
            cos, sin = position_embeddings
        q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)

        attention_mask = torch.full(
            [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype
        )
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0

        q = q.transpose(0, 1)
        k = k.transpose(0, 1)
        v = v.transpose(0, 1)
        attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim)
        attn_weights = attn_weights + attention_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.transpose(0, 1)
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


class VisionFlashAttention2(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: Optional[torch.Tensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        if position_embeddings is None:
            logger.warning_once(
                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
                "removed and `position_embeddings` will be mandatory."
            )
            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        else:
            cos, sin = position_embeddings
        q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)

        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
            seq_length, -1
        )
        attn_output = self.proj(attn_output)
        return attn_output


class VisionSdpaAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 16) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: Optional[torch.Tensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
        if position_embeddings is None:
            logger.warning_once(
                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
                "removed and `position_embeddings` will be mandatory."
            )
            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        else:
            cos, sin = position_embeddings
        q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)

        attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool)
        for i in range(1, len(cu_seqlens)):
            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
        q = q.transpose(0, 1)
        k = k.transpose(0, 1)
        v = v.transpose(0, 1)
        attn_output = F.scaled_dot_product_attention(
            q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attention_mask, dropout_p=0.0
        )
        attn_output = attn_output.squeeze(0).transpose(0, 1)
        attn_output = attn_output.reshape(seq_length, -1)
        attn_output = self.proj(attn_output)
        return attn_output


QWEN2_VL_VISION_ATTENTION_CLASSES = {
    "eager": VisionAttention,
    "flash_attention_2": VisionFlashAttention2,
    "sdpa": VisionSdpaAttention,
}


class Qwen2VLVisionBlock(nn.Module):
    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
        super().__init__()
        self.norm1 = LayerNorm(config.embed_dim, eps=1e-6)
        self.norm2 = LayerNorm(config.embed_dim, eps=1e-6)
        mlp_hidden_dim = int(config.embed_dim * config.mlp_ratio)

        self.attn = QWEN2_VL_VISION_ATTENTION_CLASSES[attn_implementation](
            config.embed_dim, num_heads=config.num_heads
        )
        self.mlp = VisionMlp(dim=config.embed_dim, hidden_dim=mlp_hidden_dim, hidden_act=config.hidden_act)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        rotary_pos_emb: Optional[torch.Tensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        hidden_states = hidden_states + self.attn(
            self.norm1(hidden_states),
            cu_seqlens=cu_seqlens,
            rotary_pos_emb=rotary_pos_emb,
            position_embeddings=position_embeddings,
        )
        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
        return hidden_states

@auto_docstring
class Qwen2VLPreTrainedModel(PreTrainedModel):
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_static_cache = False  # TODO (joao): fix. torch.compile failing probably due to `cache_positions`

    def _init_weights(self, module):
        std = self.config.get_text_config().initializer_range
        if isinstance(module, (nn.Linear, nn.Conv3d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.weight.data.fill_(1.0)
            module.bias.data.zero_()

@auto_docstring
class Qwen2VisionTransformerPretrainedModel(Qwen2VLPreTrainedModel):
    config_class = Qwen2VLVisionConfig
    _no_split_modules = ["Qwen2VLVisionBlock"]

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

        self.patch_embed = PatchEmbed(
            patch_size=config.patch_size,
            temporal_patch_size=config.temporal_patch_size,
            in_channels=config.in_channels,
            embed_dim=config.embed_dim,
        )

        head_dim = config.embed_dim // config.num_heads
        self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)

        self.blocks = nn.ModuleList(
            [Qwen2VLVisionBlock(config, config._attn_implementation) for _ in range(config.depth)]
        )
        self.merger = PatchMerger(
            dim=config.hidden_size, context_dim=config.embed_dim, spatial_merge_size=config.spatial_merge_size
        )
        self.gradient_checkpointing = False

    def get_dtype(self) -> torch.dtype:
        return self.blocks[0].mlp.fc2.weight.dtype

    def get_device(self) -> torch.device:
        return self.blocks[0].mlp.fc2.weight.device

    def rot_pos_emb(self, grid_thw):
        pos_ids = []
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            hpos_ids = hpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            )
            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
            hpos_ids = hpos_ids.flatten()

            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            wpos_ids = wpos_ids.reshape(
                h // self.spatial_merge_size,
                self.spatial_merge_size,
                w // self.spatial_merge_size,
                self.spatial_merge_size,
            )
            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
            wpos_ids = wpos_ids.flatten()
            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

    @auto_docstring
    def forward(self, pixel_values: torch.Tensor, image_grid_thw: torch.Tensor) -> torch.Tensor:
        r"""
        grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
            The temporal, height and width dimensions of feature shape for each image. Each row contains [t, h, w] values.
        """
        hidden_states = self.patch_embed(pixel_values)
        rotary_pos_emb = self.rot_pos_emb(image_grid_thw)
        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
        position_embeddings = (emb.cos(), emb.sin())

        cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum(
            dim=0,
            # Select dtype based on the following factors:
            #  - FA2 requires that cu_seqlens_q must have dtype int32
            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
            # See https://github.com/huggingface/transformers/pull/34852 for more information
            dtype=image_grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
        )
        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)

        for blk in self.blocks:
            if self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    blk.__call__, hidden_states, cu_seqlens, None, position_embeddings
                )
            else:
                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)

        return self.merger(hidden_states)