import logging

import torch
import random

from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video


@tables.register("dataset_classes", "SenseVoiceDataset")
class SenseVoiceDataset(torch.utils.data.Dataset):
    """
    SenseVoiceDataset
    """

    def __init__(
        self,
        path,
        index_ds: str = None,
        frontend=None,
        tokenizer=None,
        int_pad_value: int = -1,
        float_pad_value: float = 0.0,
        **kwargs,
    ):
        super().__init__()
        index_ds_class = tables.index_ds_classes.get(index_ds)
        self.index_ds = index_ds_class(path, **kwargs)
        preprocessor_speech = kwargs.get("preprocessor_speech", None)
        if preprocessor_speech:
            preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
            preprocessor_speech = preprocessor_speech_class(
                **kwargs.get("preprocessor_speech_conf")
            )
        self.preprocessor_speech = preprocessor_speech
        preprocessor_text = kwargs.get("preprocessor_text", None)
        if preprocessor_text:
            preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
            preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
        self.preprocessor_text = preprocessor_text

        self.frontend = frontend
        self.fs = 16000 if frontend is None else frontend.fs
        self.data_type = "sound"
        self.tokenizer = tokenizer

        self.int_pad_value = int_pad_value
        self.float_pad_value = float_pad_value
        self.sos = kwargs.get("sos", "<|startoftranscript|>")
        self.eos = kwargs.get("eos", "<|endoftext|>")
        self.batch_size = kwargs.get("batch_size")
        self.batch_type = kwargs.get("batch_type")

    def get_source_len(self, index):
        item = self.index_ds[index]
        return self.index_ds.get_source_len(item)

    def get_target_len(self, index):
        item = self.index_ds[index]
        return self.index_ds.get_target_len(item)

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

    def __getitem__(self, index):
        item = self.index_ds[index]
        # import pdb;
        # pdb.set_trace()
        source = item["source"]
        data_src = load_audio_text_image_video(source, fs=self.fs)
        if self.preprocessor_speech:
            data_src = self.preprocessor_speech(data_src, fs=self.fs)
        speech, speech_lengths = extract_fbank(
            data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
        )  # speech: [b, T, d]
        speech = speech.permute(0, 2, 1)
        target = item["target"]
        if self.preprocessor_text:
            target = self.preprocessor_text(target)

        task = item.get("prompt", "<|ASR|>")
        text_language = item.get("text_language", "<|zh|>")

        prompt = f"{self.sos}{task}{text_language}"
        prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
        prompt_ids_len = len(prompt_ids) - 1  # [sos, task]

        target_ids = self.tokenizer.encode(target, allowed_special="all")
        target_ids_len = len(target_ids) + 1  # [lid, text]

        eos = self.tokenizer.encode(self.eos, allowed_special="all")  # [eos]

        ids = prompt_ids + target_ids + eos
        ids_lengths = len(ids)

        text = torch.tensor(ids, dtype=torch.int64)
        text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)

        target_mask = (
            [0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
        )  # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
        target_mask_lengths = len(target_mask)
        target_mask = torch.tensor(target_mask, dtype=torch.float32)
        target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
        return {
            "speech": speech[0, :, :],
            "speech_lengths": speech_lengths,
            "text": text,
            "text_lengths": text_lengths,
            "target_mask": target_mask,
        }

    def collator(self, samples: list = None):
        outputs = {}
        for sample in samples:
            for key in sample.keys():
                if key not in outputs:
                    outputs[key] = []
                outputs[key].append(sample[key])

        for key, data_list in outputs.items():
            if isinstance(data_list[0], torch.Tensor):
                if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:

                    pad_value = self.int_pad_value
                else:
                    pad_value = self.float_pad_value

                outputs[key] = torch.nn.utils.rnn.pad_sequence(
                    data_list, batch_first=True, padding_value=pad_value
                )

        if self.batch_type != "example":
            for i in range(3):
                outputs = self._filter_badcase(outputs)

        return outputs

    def _filter_badcase(self, outputs, i=0):
        b, t, _ = outputs["speech"].shape
        if b * t > self.batch_size:
            beg = torch.randint(0, 2, ()).item()
            logging.info(
                f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
            )
            for key, data_list in outputs.items():
                outputs[key] = outputs[key][beg : beg + b : 2]

            speech_lengths_max = outputs["speech_lengths_max"].max().item()
            outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
            text_lengths_max = outputs["text_lengths"].max().item()
            outputs["text"] = outputs["text"][:, :text_lengths_max]
            target_mask_lengths_max = outputs["target_mask_lengths_max"].max().item()
            outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]

        return outputs