# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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.

import os
import re
from typing import List, Union, Optional
import copy
import datasets
from collections import defaultdict

import torch
import numpy as np
from torch.utils.data import Dataset
from transformers import PreTrainedTokenizer, ProcessorMixin
from omegaconf import ListConfig, DictConfig

from verl.utils.model import compute_position_id_with_mask
import verl.utils.torch_functional as verl_F


def collate_fn(data_list: list[dict]) -> dict:
    tensors = defaultdict(list)
    non_tensors = defaultdict(list)

    for data in data_list:
        for key, val in data.items():
            if isinstance(val, torch.Tensor):
                tensors[key].append(val)
            else:
                non_tensors[key].append(val)

    for key, val in tensors.items():
        tensors[key] = torch.stack(val, dim=0)

    for key, val in non_tensors.items():
        non_tensors[key] = np.array(val, dtype=object)

    return {**tensors, **non_tensors}


class RLHFDataset(Dataset):
    """
    We assume the dataset contains a column that contains prompts and other information
    """

    def __init__(
        self,
        data_files: Union[str, List[str]],
        tokenizer: PreTrainedTokenizer,
        config: DictConfig,
        processor: Optional[ProcessorMixin] = None,
    ):
        if not isinstance(data_files, (List, ListConfig)):
            data_files = [data_files]

        self.data_files = copy.deepcopy(data_files)
        self.original_data_files = copy.deepcopy(data_files)  # use for resume
        self.tokenizer = tokenizer
        self.processor = processor
        self.config = config

        self.cache_dir = os.path.expanduser(config.get("cache_dir", "~/.cache/verl/rlhf"))
        self.prompt_key = config.get("prompt_key", "prompt")
        self.image_key = config.get("image_key", "images")
        self.video_key = config.get("video_key", "videos")
        self.max_prompt_length = config.get("max_prompt_length", 1024)

        self.return_raw_chat = config.get('return_raw_chat', False)
        self.truncation = config.get('truncation', 'error')
        self.filter_overlong_prompts = config.get("filter_overlong_prompts", True)

        self.num_workers = config.get("filter_overlong_prompts_workers", max(1, os.cpu_count() // 4))
        self.num_workers = min(self.num_workers, os.cpu_count())

        # whether to store the dataset in state_dict()
        # default not store
        self.serialize_dataset = False
        self._download()
        self._read_files_and_tokenize()

    def _download(self, use_origin_parquet=False):
        from verl.utils.fs import copy_to_local
        data_files = self.data_files if not use_origin_parquet else self.original_data_files
        for i, parquet_file in enumerate(data_files):
            self.data_files[i] = copy_to_local(src=parquet_file, cache_dir=self.cache_dir)

    def _read_files_and_tokenize(self):
        dataframes = []
        for parquet_file in self.data_files:
            # read parquet files and cache
            dataframe = datasets.load_dataset("parquet", data_files=parquet_file)["train"]
            dataframes.append(dataframe)
        self.dataframe: datasets.Dataset = datasets.concatenate_datasets(dataframes)

        print(f'dataset len: {len(self.dataframe)}')

        # filter out too long prompts
        if self.filter_overlong_prompts:
            tokenizer = self.tokenizer
            prompt_key = self.prompt_key
            self.dataframe = self.dataframe.filter(
                lambda doc: len(tokenizer.apply_chat_template(doc[prompt_key], add_generation_prompt=True)
                               ) <= self.max_prompt_length,
                num_proc=self.num_workers,
                desc=f"Filtering prompts longer than {self.max_prompt_length} tokens")

            print(f'filter dataset len: {len(self.dataframe)}')

    def resume_dataset_state(self):
        self.serialize_dataset = False if hasattr(self, 'original_data_files') else True
        # resume dataframe if not it's serialized in data.pt
        if not self.serialize_dataset:
            self._download(use_origin_parquet=True)  # download and resume from original parquet files
            self._read_files_and_tokenize()
        else:
            print(r'old dataloader ckpt file is used, please train from scratch for better ckpt performance')

    def __len__(self):
        return len(self.dataframe)

    def _build_messages(self, example: dict):
        messages: list = example.pop(self.prompt_key)

        if self.image_key in example or self.video_key in example:
            for message in messages:
                content = message["content"]
                content_list = []
                for segment in re.split("(<image>|<video>)", content):
                    if segment == "<image>":
                        content_list.append({"type": "image"})
                    elif segment == "<video>":
                        content_list.append({"type": "video"})
                    else:
                        content_list.append({"type": "text", "text": segment})

                message["content"] = content_list

        return messages

    def __getitem__(self, item):
        """
        Note that we also return the raw_input_ids so that it can be combined with other chat template
        """
        row_dict: dict = self.dataframe[item]
        messages = self._build_messages(row_dict)
        model_inputs = {}

        if self.processor is not None:
            from verl.utils.dataset.vision_utils import process_image, process_video

            raw_prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
            multi_modal_data = {}

            images = None
            if self.image_key in row_dict:
                images = [process_image(image) for image in row_dict.pop(self.image_key)]
                multi_modal_data["image"] = images

            videos = None
            if self.video_key in row_dict:
                videos = [process_video(video) for video in row_dict.pop(self.video_key)]
                multi_modal_data["video"] = [video.numpy() for video in videos]

            model_inputs = self.processor(text=[raw_prompt], images=images, videos=videos, return_tensors="pt")

            input_ids = model_inputs.pop("input_ids")
            attention_mask = model_inputs.pop("attention_mask")

            if "second_per_grid_ts" in model_inputs:
                model_inputs.pop("second_per_grid_ts")

            # There's a trap here, multi_modal_inputs has to be a dict, not BatchFeature
            row_dict["multi_modal_data"] = multi_modal_data
            row_dict["multi_modal_inputs"] = dict(model_inputs)

            # second_per_grid_ts isn't used for training, just for mrope
            row_dict["multi_modal_inputs"].pop("second_per_grid_ts", None)

        else:
            raw_prompt = self.tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
            model_inputs = self.tokenizer(raw_prompt, return_tensors='pt', add_special_tokens=False)
            input_ids = model_inputs.pop('input_ids')
            attention_mask = model_inputs.pop('attention_mask')

        input_ids, attention_mask = verl_F.postprocess_data(input_ids=input_ids,
                                                            attention_mask=attention_mask,
                                                            max_length=self.max_prompt_length,
                                                            pad_token_id=self.tokenizer.pad_token_id,
                                                            left_pad=True,
                                                            truncation=self.truncation)

        if self.processor is not None and self.processor.image_processor.__class__.__name__ == "Qwen2VLImageProcessor":
            from verl.models.transformers.qwen2_vl import get_rope_index

            position_ids = [
                get_rope_index(
                    self.processor,
                    input_ids=input_ids[0],
                    image_grid_thw=model_inputs.get("image_grid_thw"),
                    video_grid_thw=model_inputs.get("video_grid_thw"),
                    second_per_grid_ts=model_inputs.get("second_per_grid_ts"),
                    attention_mask=attention_mask[0],
                )
            ]  # (1, 3, seq_len)

        else:
            position_ids = compute_position_id_with_mask(attention_mask)

        row_dict['input_ids'] = input_ids[0]
        row_dict['attention_mask'] = attention_mask[0]
        row_dict['position_ids'] = position_ids[0]

        raw_prompt_ids = self.tokenizer.encode(raw_prompt, add_special_tokens=False)
        if len(raw_prompt_ids) > self.max_prompt_length:
            if self.truncation == "left":
                raw_prompt_ids = raw_prompt_ids[-self.max_prompt_length:]
            elif self.truncation == "right":
                raw_prompt_ids = raw_prompt_ids[:self.max_prompt_length]
            elif self.truncation == "error":
                raise RuntimeError(f"Prompt length {len(raw_prompt_ids)} is longer than {self.max_prompt_length}.")

        row_dict['raw_prompt_ids'] = raw_prompt_ids
        # encode prompts without chat template
        if self.return_raw_chat:
            row_dict['raw_prompt'] = messages

        # add index for each prompt
        index = row_dict.get("extra_info", {}).get("index", 0)
        row_dict["index"] = index

        return row_dict

    def __getstate__(self):
        if not self.serialize_dataset:
            state = self.__dict__.copy()

            if 'dataframe' in state:
                del state['dataframe']
            return state

        return self.__dict__.copy()