import ast
import io
import os
from copy import deepcopy
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Set, Union

import cv2
import numpy as np
import pyarrow.parquet as pq
import torch
from arpeggio import Chord, DataloaderArgs, create_dataloader, register_transform
from arpeggio.chord import Chord
from arpeggio.tuners import Qwen3VLTransform
from arpeggio.utils.conversation_utils import (
    chatml_input_ids_to_labels,
    handle_message_extra_fields,
    sharegpt_to_hf_format,
)
from arpeggio.utils.qwen_vl_utils import get_mrope_index_qwen3_vl
from decord import VideoReader, cpu
from PIL import Image
from qwen_vl_utils import fetch_image, fetch_video, process_vision_info
from qwen_vl_utils.vision_process import smart_resize
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from transformers import AutoTokenizer


DISABLE_CV2_PATCH = os.getenv("DISABLE_CV2_PATCH", "0") == "1"

# ==================== OpenCV Resize Monkey Patch ====================
# Patch PIL.Image.resize to use OpenCV

_original_pil_resize = Image.Image.resize

_PIL_TO_CV2_INTERP = {
    Image.Resampling.NEAREST: cv2.INTER_NEAREST,
    Image.Resampling.BILINEAR: cv2.INTER_LINEAR,
    Image.Resampling.BICUBIC: cv2.INTER_CUBIC,
    Image.Resampling.LANCZOS: cv2.INTER_LANCZOS4,
    # Compat with older PIL versions
    0: cv2.INTER_NEAREST,
    2: cv2.INTER_LINEAR,
    3: cv2.INTER_CUBIC,
    1: cv2.INTER_LANCZOS4,
}


def _cv2_resize(self, size, resample=None, box=None, reducing_gap=None):
    """OpenCV-accelerated PIL Image.resize()."""
    # Fall back to original for box parameter (crop + resize)
    if box is not None:
        return _original_pil_resize(self, size, resample=resample, box=box, reducing_gap=reducing_gap)

    if resample is None:
        resample = Image.Resampling.BICUBIC
    cv2_interp = _PIL_TO_CV2_INTERP.get(resample, cv2.INTER_LINEAR)

    target_w, target_h = size
    # Use INTER_AREA for downsampling (better quality)
    if target_w < self.width or target_h < self.height:
        cv2_interp = cv2.INTER_AREA

    img_array = np.asarray(self)
    resized = cv2.resize(img_array, (target_w, target_h), interpolation=cv2_interp)
    return Image.fromarray(resized)


def enable_cv2_resize():
    """Enable OpenCV-accelerated resize."""
    Image.Image.resize = _cv2_resize


def disable_cv2_resize():
    """Restore original PIL resize."""
    Image.Image.resize = _original_pil_resize


# Enable by default
if not DISABLE_CV2_PATCH:
    enable_cv2_resize()


@dataclass
class VideoData:
    """Processed video data with metadata."""

    video: Union[torch.Tensor, List[Image.Image]]  # video tensor or PIL frames
    metadata: dict  # contains fps, total_num_frames, frames_indices
    sample_fps: float


class ThothVLTransform(Qwen3VLTransform):
    """Qwen3-VL transform that supports Thoth/msswift-style data formats.

    Supports:
    - content_split: pretrain text data
    - caption: image captioning data
    - conversations/messages: multi-turn dialogue
    - Various image formats: bytes, path, dict, PIL.Image
    - Various video formats: frames, video bytes, gif
    - Nested conversations
    """

    # ==================== Image Processing ====================

    def _read_image(
        self,
        image: Union[bytes, str, Image.Image, dict, None],
        to_pil: bool = False,
    ) -> Optional[Image.Image]:
        """Convert various image formats to PIL Image."""
        if image is None:
            return None

        if isinstance(image, Image.Image):
            return image

        if isinstance(image, bytes):
            return Image.open(io.BytesIO(image))

        if isinstance(image, str):
            # Path to image - will be handled by fetch_image later
            return image

        if isinstance(image, dict):
            if image.get("bytes") is not None:
                img_bytes = image["bytes"]
                if isinstance(img_bytes, Image.Image):
                    return img_bytes
                return Image.open(io.BytesIO(img_bytes))
            elif image.get("path") is not None:
                return image["path"]
            return None

        if isinstance(image, (np.float64, np.float32)):
            return None

        raise ValueError(f"Unsupported image type: {type(image)}")

    def _process_images(self, item: dict) -> Optional[List]:
        """Extract and process images from item."""
        raw_images = None

        if "jpg" in item:
            raw_images = [item.pop("jpg")]
        elif "image" in item:
            image_data = item.pop("image")
            if image_data is not None and not isinstance(image_data, (np.float64, np.float32)):
                raw_images = [image_data]
        elif "images" in item:
            raw_images = item.pop("images")
            if isinstance(raw_images, np.ndarray):
                raw_images = raw_images.tolist()
            elif isinstance(raw_images, dict):
                raw_images = [raw_images]

        if not raw_images:
            return None

        images = []
        for raw in raw_images:
            img = self._read_image(raw, to_pil=True)
            if img is None:
                continue

            if isinstance(img, str):
                # Path - use fetch_image
                ele = {"type": "image", "image": img}
                self._patch_visual_element(ele)
                img = fetch_image(ele, image_patch_size=self._patch_size)

            images.append(img)

        return images if images else None

    # ==================== Video Processing ====================

    def _read_gif(self, gif_bytes: bytes) -> List[Image.Image]:
        """Extract frames from a GIF."""
        if not isinstance(gif_bytes, bytes):
            raise TypeError(f"Unsupported gif type: {type(gif_bytes)}")

        frames = []
        with io.BytesIO(gif_bytes) as byte_stream:
            with Image.open(byte_stream) as img:
                try:
                    while True:
                        frames.append(img.copy())
                        img.seek(img.tell() + 1)
                except EOFError:
                    pass
        return frames

    def _decode_video_bytes(self, video_bytes: bytes) -> tuple[torch.Tensor, dict, float]:
        """Decode video from bytes using decord.

        Returns (video_tensor, metadata, sample_fps)
        """
        vr = VideoReader(io.BytesIO(video_bytes), ctx=cpu(0), num_threads=1)

        total_frames = len(vr)
        fps = vr.get_avg_fps()

        max_num_frames = self._video_ele_kwargs.get("max_frames") or int(os.environ.get("FPS_MAX_FRAMES", 32))

        if total_frames <= max_num_frames:
            indices = list(range(total_frames))
        else:
            indices = np.linspace(0, total_frames - 1, max_num_frames, dtype=int).tolist()

        # numpy array: [N, H, W, C]
        frames = vr.get_batch(indices).asnumpy()

        #  [N, C, H, W]
        video_tensor = torch.from_numpy(frames).permute(0, 3, 1, 2)

        sample_fps = len(indices) / total_frames * fps if total_frames > 0 else 1.0

        metadata = {
            "fps": fps,
            "total_num_frames": total_frames,
            "frames_indices": torch.tensor(indices),
        }

        return video_tensor, metadata, sample_fps

    def _process_videos(self, item: dict) -> Optional[List[VideoData]]:
        """Extract and process videos from item.

        Returns a list of VideoData, each containing video tensor/frames and metadata.

        Supported formats:
            - frames: List[bytes] - Each bytes is an image (frame)
            - video: bytes - Raw mp4 video bytes
            - video: str - Video file path
            - gif: bytes - GIF animation bytes
            - videos: List[...] - Multiple videos in various formats
        """
        max_num_frames = int(os.environ.get("FPS_MAX_FRAMES", 16))
        raw_videos = None

        if "frames" in item:
            raw_frames = item.pop("frames")
            frames = []
            for frame in raw_frames:
                img = self._read_image(frame)
                if img is not None and isinstance(img, Image.Image):
                    frames.append(img)

            if len(frames) > max_num_frames:
                indices = np.linspace(0, len(frames) - 1, max_num_frames, dtype=int)
                frames = [frames[i] for i in indices]

            if frames:
                raw_videos = [frames]
        elif "video" in item:
            raw_videos = [item.pop("video")]
        elif "gif" in item:
            raw_videos = [self._read_gif(item.pop("gif"))]
        elif "videos" in item:
            raw_videos = item.pop("videos")
            if isinstance(raw_videos, np.ndarray):
                raw_videos = raw_videos.tolist()
                if raw_videos and isinstance(raw_videos[0], np.ndarray):
                    raw_videos = [v.tolist() for v in raw_videos]

        if not raw_videos:
            return None

        results = []

        try:
            for raw in raw_videos:
                if isinstance(raw, list) and raw and isinstance(raw[0], Image.Image):
                    # List of PIL Images (frames)
                    num_frames = len(raw)
                    metadata = {
                        "fps": 1.0,
                        "total_num_frames": num_frames,
                        "frames_indices": torch.arange(num_frames),
                    }
                    results.append(VideoData(video=raw, metadata=metadata, sample_fps=1.0))

                elif isinstance(raw, list) and raw and isinstance(raw[0], bytes):
                    total_frames = len(raw)

                    if total_frames > max_num_frames:
                        indices = np.linspace(0, total_frames - 1, max_num_frames, dtype=int)
                    else:
                        indices = range(total_frames)

                    frames = []
                    for i in indices:
                        img = self._read_image(raw[i])
                        if img is not None and isinstance(img, Image.Image):
                            frames.append(img)

                    if frames:
                        num_frames = len(frames)
                        metadata = {
                            "fps": 1.0,
                            "total_num_frames": total_frames,
                            "frames_indices": torch.tensor(
                                list(indices) if total_frames > max_num_frames else list(range(num_frames))
                            ),
                        }
                        results.append(VideoData(video=frames, metadata=metadata, sample_fps=1.0))

                elif isinstance(raw, bytes):
                    # Video bytes - use decord
                    video_tensor, metadata, sample_fps = self._decode_video_bytes(raw)
                    results.append(VideoData(video=video_tensor, metadata=metadata, sample_fps=sample_fps))

                elif isinstance(raw, str):
                    # Video file path
                    ele = {"type": "video", "video": raw}
                    self._patch_visual_element(ele)
                    result, sample_fps = fetch_video(
                        ele,
                        image_patch_size=self._patch_size,
                        return_video_sample_fps=True,
                        return_video_metadata=True,
                    )

                    if isinstance(result, tuple):
                        video_tensor, metadata = result
                    else:
                        video_tensor = result
                        num_frames = video_tensor.shape[0]
                        metadata = {
                            "fps": sample_fps,
                            "total_num_frames": num_frames,
                            "frames_indices": torch.arange(num_frames),
                        }
                    results.append(VideoData(video=video_tensor, metadata=metadata, sample_fps=sample_fps))

                elif isinstance(raw, torch.Tensor):
                    num_frames = raw.shape[0]
                    metadata = {
                        "fps": 1.0,
                        "total_num_frames": num_frames,
                        "frames_indices": torch.arange(num_frames),
                    }
                    results.append(VideoData(video=raw, metadata=metadata, sample_fps=1.0))
        except Exception as e:
            print(f"_process_videos error: {e}")
            raise e

        return results if results else None

    # ==================== Conversation Processing ====================

    def _normalize_message_format(self, messages: List[Dict]) -> List[Dict]:
        """Convert from/value format to role/content format and filter system messages.

        Handles:
        - from: human/user -> role: user
        - from: gpt/assistant -> role: assistant
        - from: system -> filtered out
        - value -> content
        - Dirty data fix: if assistant message contains <image>/<video>, swap roles
        """
        if isinstance(messages, np.ndarray):
            messages = messages.tolist()
        elif isinstance(messages, str):
            messages = ast.literal_eval(messages)

        normalized = []
        for msg in messages:
            # Skip system messages
            if msg.get("from") == "system" or msg.get("role") == "system":
                continue

            new_msg = {}

            # Handle role/from
            if "role" in msg:
                new_msg["role"] = msg["role"]
            elif "from" in msg:
                from_value = msg["from"]
                if from_value in ("human", "user"):
                    new_msg["role"] = "user"
                elif from_value in ("gpt", "assistant"):
                    new_msg["role"] = "assistant"
                else:
                    new_msg["role"] = from_value

            # Handle content/value
            if "content" in msg:
                new_msg["content"] = msg["content"]
            elif "value" in msg:
                new_msg["content"] = msg["value"]

            normalized.append(new_msg)

        # Fix dirty data: if assistant message contains <image>/<video>, swap roles
        normalized = self._fix_misplaced_roles(normalized)

        return normalized

    def _fix_misplaced_roles(self, messages: List[Dict]) -> List[Dict]:
        """Fix dirty data where assistant messages contain <image>/<video>.

        Detection pattern:
        - First message is from assistant AND contains <image>/<video>
        - Messages alternate between roles

        Fix: Swap all user <-> assistant roles
        """
        if not messages:
            return messages

        def _contains_media(content) -> bool:
            if not isinstance(content, str):
                return False
            return "<image>" in content or "<video>" in content

        # Check first message
        first_msg = messages[0]
        first_role = first_msg.get("role", "")
        first_content = first_msg.get("content", "")

        # Only fix if: first message is assistant AND contains media
        if first_role != "assistant" or not _contains_media(first_content):
            return messages

        # Additional validation: check if this looks like a swapped conversation
        # (i.e., roles alternate properly, just in wrong order)
        if len(messages) >= 2:
            second_role = messages[1].get("role", "")
            # Expected pattern after fix: user, assistant, user, assistant...
            # Current pattern (wrong): assistant, user, assistant, user...
            if second_role != "user":
                # Doesn't match expected dirty pattern, don't fix
                return messages

        # Swap all roles
        role_map = {"user": "assistant", "assistant": "user"}
        fixed = []
        for msg in messages:
            new_msg = msg.copy()
            if new_msg.get("role") in role_map:
                new_msg["role"] = role_map[new_msg["role"]]
            fixed.append(new_msg)

        return fixed

    def _replace_image_with_video_placeholder(self, messages: List[Dict]) -> List[Dict]:
        """Replace <image> placeholders with <video> when videos are present."""
        for msg in messages:
            if "value" in msg:
                msg["value"] = msg["value"].replace("<image>", "<video>")
            elif "content" in msg:
                msg["content"] = msg["content"].replace("<image>", "<video>")
        return messages

    def _is_nested_conversation(self, messages) -> bool:
        """Check if messages contain nested conversations."""
        if not messages:
            return False
        return isinstance(messages[0], (np.ndarray, list))

    # ==================== Main Preprocessing Methods ====================

    def preprocess_pretrain(self, item: dict) -> Chord:
        """Process pretrain-style data (content_split)."""
        item = dict(item)

        content = item.pop("content_split")
        messages = [{"role": "assistant", "content": content}]

        # Process any images
        images = self._process_images(item)

        text = self.processor.apply_chat_template(messages, tokenize=False)

        extra_info = item.pop("extra_info", {})
        if self.add_raw:
            extra_info["raw_messages"] = messages

        return self._make_model_inputs(
            text=text,
            images=images,
            extra_info=extra_info,
            do_resize=True,
        )

    def preprocess_caption(self, item: dict) -> Chord:
        """Process image captioning data."""
        item = dict(item)

        caption = item.pop("caption")
        messages = [
            {"role": "user", "content": "<image>"},
            {"role": "assistant", "content": caption},
        ]

        # Process images
        images = self._process_images(item)

        # Convert to HF format for apply_chat_template
        prompt = sharegpt_to_hf_format(messages)
        text = self.processor.apply_chat_template(prompt, tokenize=False)

        extra_info = item.pop("extra_info", {})
        if self.add_raw:
            extra_info["raw_messages"] = messages

        return self._make_model_inputs(
            text=text,
            images=images,
            extra_info=extra_info,
            do_resize=True,
        )

    def preprocess_conversation(self, item: dict) -> Chord:
        """Process conversation/dialogue data."""
        item = dict(item)

        # Get messages from either 'messages' or 'conversations' key
        if "messages" in item:
            messages = item.pop("messages")
        elif "conversations" in item:
            messages = item.pop("conversations")
        else:
            raise ValueError("No messages or conversations found in item")

        messages = self._normalize_message_format(messages)

        # Process images and videos
        images = self._process_images(item)
        video_data_list = self._process_videos(item)

        videos = None
        video_metadatas = None
        video_kwargs = {"do_sample_frames": False}

        if video_data_list:
            messages = self._replace_image_with_video_placeholder(messages)

            videos = [vd.video for vd in video_data_list]
            video_metadatas = [vd.metadata for vd in video_data_list]
            fps_list = [vd.sample_fps for vd in video_data_list]
            video_kwargs["fps"] = fps_list

        # Convert to HF format
        prompt = sharegpt_to_hf_format(messages)
        text = self.processor.apply_chat_template(prompt, tokenize=False)

        extra_info = item.pop("extra_info", {})
        if self.add_raw:
            extra_info["raw_messages"] = messages

        return self._make_model_inputs(
            text=text,
            images=images,
            videos=videos,
            video_metadatas=video_metadatas if video_metadatas else None,
            extra_info=extra_info,
            do_resize=True,
            **video_kwargs,
        )

    def _process_nested_conversations(self, item: dict) -> List[Chord]:
        """Process nested conversations into a list of Chords."""
        base_item = dict(item)

        if "messages" in base_item:
            nested_convs = base_item.pop("messages")
        else:
            nested_convs = base_item.pop("conversations")

        if isinstance(nested_convs, np.ndarray):
            nested_convs = nested_convs.tolist()

        results = []
        for single_conv in nested_convs:
            if isinstance(single_conv, np.ndarray):
                single_conv = single_conv.tolist()

            single_item = deepcopy(base_item)
            single_item["messages"] = single_conv

            chord = self.preprocess_conversation(single_item)
            results.append(chord)

        return results

    def preprocess(self, item: dict) -> Union[Chord, List[Chord]]:
        """Main preprocessing entry point.

        Please refer https://code.byted.org/Thoth/ms-swift/blob/dev.gyf.thoth_vl.hdfs_support/swift/llm/dataset/preprocessor/extra.py

        Handles various data formats from msswift:
        - content_split: pretrain text data
        - caption: image captioning data
        - messages/conversations: multi-turn dialogue (including nested)
        """
        try:
            item = dict(item)  # Shallow copy

            # Handle content_split (pretrain style)
            if "content_split" in item:
                return self.preprocess_pretrain(item)

            # Handle caption (image captioning style)
            if "caption" in item:
                return self.preprocess_caption(item)

            # Handle conversations/messages
            if "conversations" in item or "messages" in item:
                # Get the conversation data
                conv_key = "messages" if "messages" in item else "conversations"
                conv_data = item[conv_key]

                if isinstance(conv_data, np.ndarray):
                    conv_data = conv_data.tolist()
                elif isinstance(conv_data, str):
                    conv_data = ast.literal_eval(conv_data)

                # Check for nested conversations
                if self._is_nested_conversation(conv_data):
                    item[conv_key] = conv_data
                    return self._process_nested_conversations(item)

                return self.preprocess_conversation(item)

            # Fallback to parent class behavior
            if self.prompt_key in item:
                return super().preprocess_conversation(item)
            elif self.document_key in item:
                return super().preprocess_document(item)

            raise RuntimeError(f"Sample contained no usable fields: {list(item.keys())}")

        except Exception as e:
            if not self.allow_skip:
                raise e

            if self.should_use_mrope:
                position_ids = torch.zeros(3, 1, 0, dtype=torch.long)
            else:
                position_ids = torch.zeros(1, 0, dtype=torch.long)

            return {
                "input_ids": torch.zeros(1, 0, dtype=torch.long),
                "position_ids": position_ids,
                "attention_mask": torch.zeros(1, 0, dtype=torch.long),
                "labels": torch.zeros(1, 0, dtype=torch.long),
                "extra_info": [{}],
            }


register_transform("thoth_vl", ThothVLTransform)