from __future__ import annotations

import os
import random
from collections import defaultdict
from importlib.resources import files

import jieba
import torch
from pypinyin import Style, lazy_pinyin
from torch.nn.utils.rnn import pad_sequence

# seed everything

def seed_everything(seed=0):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# helpers

def exists(v):
    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)
    return seq[None, :] < t[:, None]

def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask

def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
    lengths = (frac_lengths * seq_len).long()
    max_start = seq_len - lengths

    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)

def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)

    return num / den.clamp(min=1.0)

# simple utf-8 tokenizer, since paper went character based
def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text

# char tokenizer, based on custom dataset's extracted .txt file
def list_str_to_idx(
    text: list[str] | list[list[str]],
    vocab_char_map: dict[str, int],  # {char: idx}
    padding_value=-1,
) -> int["b nt"]:  # noqa: F722
    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
    return text

# Get tokenizer

def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
    """
    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
                - "char" for char-wise tokenizer, need .txt vocab_file
                - "byte" for utf-8 tokenizer
                - "custom" if you're directly passing in a path to the vocab.txt you want to use
    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
                - if use "char", derived from unfiltered character & symbol counts of custom dataset
                - if use "byte", set to 256 (unicode byte range)
    """
    if tokenizer in ["pinyin", "char"]:
        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
        with open(tokenizer_path, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)
        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"

    elif tokenizer == "byte":
        vocab_char_map = None
        vocab_size = 256

    elif tokenizer == "custom":
        with open(dataset_name, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)

    return vocab_char_map, vocab_size

# convert char to pinyin

def convert_char_to_pinyin(text_list, polyphone=True):
    if jieba.dt.initialized is False:
        jieba.default_logger.setLevel(50)  # CRITICAL
        jieba.initialize()

    final_text_list = []
    custom_trans = str.maketrans(
        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
    )  # add custom trans here, to address oov

    def is_chinese(c):
        return (
            "\u3100" <= c <= "\u9fff"  # common chinese characters
        )

    for text in text_list:
        char_list = []
        text = text.translate(custom_trans)
        for seg in jieba.cut(text):
            seg_byte_len = len(bytes(seg, "UTF-8"))
            if seg_byte_len == len(seg):  # if pure alphabets and symbols
                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
                    char_list.append(" ")
                char_list.extend(seg)
            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
                for i, c in enumerate(seg):
                    if is_chinese(c):
                        char_list.append(" ")
                    char_list.append(seg_[i])
            else:  # if mixed characters, alphabets and symbols
                for c in seg:
                    if ord(c) < 256:
                        char_list.extend(c)
                    elif is_chinese(c):
                        char_list.append(" ")
                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
                    else:
                        char_list.append(c)
        final_text_list.append(char_list)

    return final_text_list

# filter func for dirty data with many repetitions

def repetition_found(text, length=2, tolerance=10):
    pattern_count = defaultdict(int)
    for i in range(len(text) - length + 1):
        pattern = text[i : i + length]
        pattern_count[pattern] += 1
    for pattern, count in pattern_count.items():
        if count > tolerance:
            return True
    return False

# get the empirically pruned step for sampling

def get_epss_timesteps(n, device, dtype):
    dt = 1 / 32
    predefined_timesteps = {
        5: [0, 2, 4, 8, 16, 32],
        6: [0, 2, 4, 6, 8, 16, 32],
        7: [0, 2, 4, 6, 8, 16, 24, 32],
        10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
        12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
        16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
    }
    t = predefined_timesteps.get(n, [])
    if not t:
        return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
    return dt * torch.tensor(t, device=device, dtype=dtype)


from __future__ import annotations

import os
import random
from collections import defaultdict
from importlib.resources import files

import jieba
import torch
from pypinyin import Style, lazy_pinyin
from torch.nn.utils.rnn import pad_sequence

# seed everything

def seed_everything(seed=0):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# helpers

def exists(v):
    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:


exists

from pypinyin import Style, lazy_pinyin
from torch.nn.utils.rnn import pad_sequence

# seed everything

def seed_everything(seed=0):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# helpers

def exists(v):
    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821


default

# seed everything

def seed_everything(seed=0):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# helpers

def exists(v):
    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)


    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# helpers

def exists(v):
    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)
    return seq[None, :] < t[:, None]

def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask


    return v is not None

def default(v, d):
    return v if exists(v) else d

def is_package_available(package_name: str) -> bool:
    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)
    return seq[None, :] < t[:, None]

def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask

def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
    lengths = (frac_lengths * seq_len).long()
    max_start = seq_len - lengths

    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)


    try:
        import importlib

        package_exists = importlib.util.find_spec(package_name) is not None
        return package_exists
    except Exception:
        return False

# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)
    return seq[None, :] < t[:, None]

def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask

def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
    lengths = (frac_lengths * seq_len).long()
    max_start = seq_len - lengths

    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)

def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)


# tensor helpers

def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
    if not exists(length):
        length = t.amax()

    seq = torch.arange(length, device=t.device)
    return seq[None, :] < t[:, None]

def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask

def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
    lengths = (frac_lengths * seq_len).long()
    max_start = seq_len - lengths

    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)

def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)

    return num / den.clamp(min=1.0)

# simple utf-8 tokenizer, since paper went character based
def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text


def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
    max_seq_len = seq_len.max().item()
    seq = torch.arange(max_seq_len, device=start.device).long()
    start_mask = seq[None, :] >= start[:, None]
    end_mask = seq[None, :] < end[:, None]
    return start_mask & end_mask

def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
    lengths = (frac_lengths * seq_len).long()
    max_start = seq_len - lengths

    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)

def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)

    return num / den.clamp(min=1.0)

# simple utf-8 tokenizer, since paper went character based
def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text

# char tokenizer, based on custom dataset's extracted .txt file
def list_str_to_idx(
    text: list[str] | list[list[str]],
    vocab_char_map: dict[str, int],  # {char: idx}
    padding_value=-1,
) -> int["b nt"]:  # noqa: F722
    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
    return text


    rand = torch.rand_like(frac_lengths)
    start = (max_start * rand).long().clamp(min=0)
    end = start + lengths

    return mask_from_start_end_indices(seq_len, start, end)

def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)

    return num / den.clamp(min=1.0)

# simple utf-8 tokenizer, since paper went character based
def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text

# char tokenizer, based on custom dataset's extracted .txt file
def list_str_to_idx(
    text: list[str] | list[list[str]],
    vocab_char_map: dict[str, int],  # {char: idx}
    padding_value=-1,
) -> int["b nt"]:  # noqa: F722
    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
    return text

# Get tokenizer

def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
    """
    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
                - "char" for char-wise tokenizer, need .txt vocab_file


def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
    if not exists(mask):
        return t.mean(dim=1)

    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
    num = t.sum(dim=1)
    den = mask.float().sum(dim=1)

    return num / den.clamp(min=1.0)

# simple utf-8 tokenizer, since paper went character based
def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text

# char tokenizer, based on custom dataset's extracted .txt file
def list_str_to_idx(
    text: list[str] | list[list[str]],
    vocab_char_map: dict[str, int],  # {char: idx}
    padding_value=-1,
) -> int["b nt"]:  # noqa: F722
    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
    return text

# Get tokenizer

def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
    """
    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
                - "char" for char-wise tokenizer, need .txt vocab_file
                - "byte" for utf-8 tokenizer
                - "custom" if you're directly passing in a path to the vocab.txt you want to use
    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
                - if use "char", derived from unfiltered character & symbol counts of custom dataset
                - if use "byte", set to 256 (unicode byte range)
    """
    if tokenizer in ["pinyin", "char"]:
        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
        with open(tokenizer_path, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):


def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
    return text

# char tokenizer, based on custom dataset's extracted .txt file
def list_str_to_idx(
    text: list[str] | list[list[str]],
    vocab_char_map: dict[str, int],  # {char: idx}
    padding_value=-1,
) -> int["b nt"]:  # noqa: F722
    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
    return text

# Get tokenizer

def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
    """
    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
                - "char" for char-wise tokenizer, need .txt vocab_file
                - "byte" for utf-8 tokenizer
                - "custom" if you're directly passing in a path to the vocab.txt you want to use
    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
                - if use "char", derived from unfiltered character & symbol counts of custom dataset
                - if use "byte", set to 256 (unicode byte range)
    """
    if tokenizer in ["pinyin", "char"]:
        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
        with open(tokenizer_path, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)
        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"

    elif tokenizer == "byte":
        vocab_char_map = None
        vocab_size = 256

    elif tokenizer == "custom":
        with open(dataset_name, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)

    return vocab_char_map, vocab_size

# convert char to pinyin

def convert_char_to_pinyin(text_list, polyphone=True):
    if jieba.dt.initialized is False:
        jieba.default_logger.setLevel(50)  # CRITICAL
        jieba.initialize()

    final_text_list = []
    custom_trans = str.maketrans(
        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
    )  # add custom trans here, to address oov

    def is_chinese(c):
        return (
            "\u3100" <= c <= "\u9fff"  # common chinese characters
        )


        vocab_size = len(vocab_char_map)
        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"

    elif tokenizer == "byte":
        vocab_char_map = None
        vocab_size = 256

    elif tokenizer == "custom":
        with open(dataset_name, "r", encoding="utf-8") as f:
            vocab_char_map = {}
            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)

    return vocab_char_map, vocab_size

# convert char to pinyin

def convert_char_to_pinyin(text_list, polyphone=True):
    if jieba.dt.initialized is False:
        jieba.default_logger.setLevel(50)  # CRITICAL
        jieba.initialize()

    final_text_list = []
    custom_trans = str.maketrans(
        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
    )  # add custom trans here, to address oov

    def is_chinese(c):
        return (
            "\u3100" <= c <= "\u9fff"  # common chinese characters
        )

    for text in text_list:
        char_list = []
        text = text.translate(custom_trans)
        for seg in jieba.cut(text):
            seg_byte_len = len(bytes(seg, "UTF-8"))
            if seg_byte_len == len(seg):  # if pure alphabets and symbols
                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
                    char_list.append(" ")
                char_list.extend(seg)
            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
                for i, c in enumerate(seg):
                    if is_chinese(c):
                        char_list.append(" ")
                    char_list.append(seg_[i])
            else:  # if mixed characters, alphabets and symbols
                for c in seg:
                    if ord(c) < 256:
                        char_list.extend(c)
                    elif is_chinese(c):
                        char_list.append(" ")
                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
                    else:
                        char_list.append(c)
        final_text_list.append(char_list)

    return final_text_list

# filter func for dirty data with many repetitions

def repetition_found(text, length=2, tolerance=10):
    pattern_count = defaultdict(int)
    for i in range(len(text) - length + 1):
        pattern = text[i : i + length]
        pattern_count[pattern] += 1
    for pattern, count in pattern_count.items():
        if count > tolerance:
            return True
    return False

# get the empirically pruned step for sampling

def get_epss_timesteps(n, device, dtype):


                    if is_chinese(c):
                        char_list.append(" ")
                    char_list.append(seg_[i])
            else:  # if mixed characters, alphabets and symbols
                for c in seg:
                    if ord(c) < 256:
                        char_list.extend(c)
                    elif is_chinese(c):
                        char_list.append(" ")
                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
                    else:
                        char_list.append(c)
        final_text_list.append(char_list)

    return final_text_list

# filter func for dirty data with many repetitions

def repetition_found(text, length=2, tolerance=10):
    pattern_count = defaultdict(int)
    for i in range(len(text) - length + 1):
        pattern = text[i : i + length]
        pattern_count[pattern] += 1
    for pattern, count in pattern_count.items():
        if count > tolerance:
            return True
    return False

# get the empirically pruned step for sampling

def get_epss_timesteps(n, device, dtype):
    dt = 1 / 32
    predefined_timesteps = {
        5: [0, 2, 4, 8, 16, 32],
        6: [0, 2, 4, 6, 8, 16, 32],
        7: [0, 2, 4, 6, 8, 16, 24, 32],
        10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
        12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
        16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
    }
    t = predefined_timesteps.get(n, [])
    if not t:
        return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
    return dt * torch.tensor(t, device=device, dtype=dtype)


    return final_text_list

# filter func for dirty data with many repetitions

def repetition_found(text, length=2, tolerance=10):
    pattern_count = defaultdict(int)
    for i in range(len(text) - length + 1):
        pattern = text[i : i + length]
        pattern_count[pattern] += 1
    for pattern, count in pattern_count.items():
        if count > tolerance:
            return True
    return False

# get the empirically pruned step for sampling

def get_epss_timesteps(n, device, dtype):
    dt = 1 / 32
    predefined_timesteps = {
        5: [0, 2, 4, 8, 16, 32],
        6: [0, 2, 4, 6, 8, 16, 32],
        7: [0, 2, 4, 6, 8, 16, 24, 32],
        10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
        12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
        16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
    }
    t = predefined_timesteps.get(n, [])
    if not t:
        return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
    return dt * torch.tensor(t, device=device, dtype=dtype)


            for i, char in enumerate(f):
                vocab_char_map[char[:-1]] = i
        vocab_size = len(vocab_char_map)

    return vocab_char_map, vocab_size

# convert char to pinyin

def convert_char_to_pinyin(text_list, polyphone=True):
    if jieba.dt.initialized is False:
        jieba.default_logger.setLevel(50)  # CRITICAL
        jieba.initialize()

    final_text_list = []
    custom_trans = str.maketrans(
        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
    )  # add custom trans here, to address oov

    def is_chinese(c):
        return (
            "\u3100" <= c <= "\u9fff"  # common chinese characters
        )

    for text in text_list:
        char_list = []
        text = text.translate(custom_trans)
        for seg in jieba.cut(text):
            seg_byte_len = len(bytes(seg, "UTF-8"))
            if seg_byte_len == len(seg):  # if pure alphabets and symbols
                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
                    char_list.append(" ")
                char_list.extend(seg)
            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
                for i, c in enumerate(seg):
                    if is_chinese(c):
                        char_list.append(" ")
                    char_list.append(seg_[i])
            else:  # if mixed characters, alphabets and symbols
                for c in seg:
                    if ord(c) < 256:
                        char_list.extend(c)


"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""
# flake8: noqa

from __future__ import annotations

import math
from typing import Optional

import torch
import torch.nn.functional as F
import torchaudio
from librosa.filters import mel as librosa_mel_fn
from torch import nn
from x_transformers.x_transformers import apply_rotary_pos_emb

from f5_tts.model_new.utils import is_package_available

# raw wav to mel spec

mel_basis_cache = {}
hann_window_cache = {}

def get_bigvgan_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
    fmin=0,
    fmax=None,
    center=False,
):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
    device = waveform.device
    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"

    if key not in mel_basis_cache:
        mel = librosa_mel_fn(sr=target_sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=fmin, fmax=fmax)
        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)  # TODO: why they need .float()?
        hann_window_cache[key] = torch.hann_window(win_length).to(device)

    mel_basis = mel_basis_cache[key]
    hann_window = hann_window_cache[key]

    padding = (n_fft - hop_length) // 2
    waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)

    spec = torch.stft(
        waveform,
        n_fft,
        hop_length=hop_length,
        win_length=win_length,
        window=hann_window,
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=True,
    )
    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)

    mel_spec = torch.matmul(mel_basis, spec)
    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))

    return mel_spec

def get_vocos_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
):
    mel_stft = torchaudio.transforms.MelSpectrogram(
        sample_rate=target_sample_rate,
        n_fft=n_fft,
        win_length=win_length,
        hop_length=hop_length,
        n_mels=n_mel_channels,
        power=1,
        center=True,
        normalized=False,
        norm=None,
    ).to(waveform.device)
    if len(waveform.shape) == 3:
        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'

    assert len(waveform.shape) == 2

    mel = mel_stft(waveform)
    mel = mel.clamp(min=1e-5).log()
    return mel

class MelSpec(nn.Module):
    def __init__(
        self,
        n_fft=1024,
        hop_length=256,
        win_length=1024,
        n_mel_channels=100,
        target_sample_rate=24_000,
        mel_spec_type="vocos",
    ):
        super().__init__()
        assert mel_spec_type in ["vocos", "bigvgan"], print("We only support two extract mel backend: vocos or bigvgan")

        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.target_sample_rate = target_sample_rate

        if mel_spec_type == "vocos":
            self.extractor = get_vocos_mel_spectrogram
        elif mel_spec_type == "bigvgan":
            self.extractor = get_bigvgan_mel_spectrogram

        self.register_buffer("dummy", torch.tensor(0), persistent=False)

    def forward(self, wav):
        if self.dummy.device != wav.device:
            self.to(wav.device)

        mel = self.extractor(
            waveform=wav,
            n_fft=self.n_fft,
            n_mel_channels=self.n_mel_channels,
            target_sample_rate=self.target_sample_rate,
            hop_length=self.hop_length,
            win_length=self.win_length,
        )

        return mel

# sinusoidal position embedding

class SinusPositionEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb

# convolutional position embedding

class ConvPositionEmbedding(nn.Module):
    def __init__(self, dim, kernel_size=31, groups=16):
        super().__init__()
        assert kernel_size % 2 != 0
        self.conv1d = nn.Sequential(
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
        )

    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.0)

        x = x.permute(0, 2, 1)
        x = self.conv1d(x)
        out = x.permute(0, 2, 1)

        if mask is not None:
            out = out.masked_fill(~mask, 0.0)

        return out

# rotary positional embedding related

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.0):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
    pos = (
        start.unsqueeze(1)
        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
    )
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos

# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x

# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108

class ConvNeXtV2Block(nn.Module):
    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        dilation: int = 1,
    ):
        super().__init__()
        padding = (dilation * (7 - 1)) // 2
        self.dwconv = nn.Conv1d(
            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
        )  # depthwise conv
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.grn = GRN(intermediate_dim)
        self.pwconv2 = nn.Linear(intermediate_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = x.transpose(1, 2)  # b n d -> b d n
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # b d n -> b n d
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        return residual + x

# RMSNorm

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4

    def forward(self, x):
        if self.native_rms_norm:
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
        else:
            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
            x = x * torch.rsqrt(variance + self.eps)
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = x * self.weight

        return x

# AdaLayerNorm
# return with modulated x for attn input, and params for later mlp modulation

class AdaLayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb=None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

# AdaLayerNorm for final layer
# return only with modulated x for attn input, cuz no more mlp modulation

class AdaLayerNorm_Final(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x

# FeedForward

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.ff(x)

# Attention with possible joint part
# modified from diffusers/src/diffusers/models/attention_processor.py

class Attention(nn.Module):
    def __init__(
        self,
        processor: JointAttnProcessor | AttnProcessor,
        dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        context_dim: Optional[int] = None,  # if not None -> joint attention
        context_pre_only: bool = False,
        qk_norm: Optional[str] = None,
    ):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.processor = processor

        self.dim = dim
        self.heads = heads
        self.inner_dim = dim_head * heads
        self.dropout = dropout

        self.context_dim = context_dim
        self.context_pre_only = context_pre_only

        self.to_q = nn.Linear(dim, self.inner_dim)
        self.to_k = nn.Linear(dim, self.inner_dim)
        self.to_v = nn.Linear(dim, self.inner_dim)

        if qk_norm is None:
            self.q_norm = None
            self.k_norm = None
        elif qk_norm == "rms_norm":
            self.q_norm = RMSNorm(dim_head, eps=1e-6)
            self.k_norm = RMSNorm(dim_head, eps=1e-6)
        else:
            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")

        if self.context_dim is not None:
            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
            if qk_norm is None:
                self.c_q_norm = None
                self.c_k_norm = None
            elif qk_norm == "rms_norm":
                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)

        self.to_out = nn.ModuleList([])
        self.to_out.append(nn.Linear(self.inner_dim, dim))
        self.to_out.append(nn.Dropout(dropout))

        if self.context_dim is not None and not self.context_pre_only:
            self.to_out_c = nn.Linear(self.inner_dim, context_dim)

    def forward(
        self,
        x: float["b n d"],  # noised input x
        c: float["b n d"] = None,  # context c
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.Tensor:
        if c is not None:
            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
        else:
            return self.processor(self, x, mask=mask, rope=rope)

# Attention processor

if is_package_available("flash_attn"):
    from flash_attn.bert_padding import pad_input, unpad_input
    from flash_attn import flash_attn_varlen_func, flash_attn_func

class AttnProcessor:
    def __init__(
        self,
        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
        attn_backend: str = "torch",  # "torch" or "flash_attn"
        attn_mask_enabled: bool = True,
    ):
        if attn_backend == "flash_attn":
            assert is_package_available("flash_attn"), "Please install flash-attn first."

        self.pe_attn_head = pe_attn_head
        self.attn_backend = attn_backend
        self.attn_mask_enabled = attn_mask_enabled

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding
    ) -> torch.FloatTensor:
        batch_size = x.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)

        # apply rotary position embedding
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)

            if self.pe_attn_head is not None:
                pn = self.pe_attn_head
                query[:, :pn, :, :] = apply_rotary_pos_emb(query[:, :pn, :, :], freqs, q_xpos_scale)
                key[:, :pn, :, :] = apply_rotary_pos_emb(key[:, :pn, :, :], freqs, k_xpos_scale)
            else:
                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)

        if self.attn_backend == "torch":
            # mask. e.g. inference got a batch with different target durations, mask out the padding
            if self.attn_mask_enabled and mask is not None:
                attn_mask = mask
                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
                attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
            else:
                attn_mask = None
            x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
            x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        elif self.attn_backend == "flash_attn":
            query = query.transpose(1, 2)  # [b, h, n, d] -> [b, n, h, d]
            key = key.transpose(1, 2)
            value = value.transpose(1, 2)
            if self.attn_mask_enabled and mask is not None:
                query, indices, q_cu_seqlens, q_max_seqlen_in_batch, _ = unpad_input(query, mask)
                key, _, k_cu_seqlens, k_max_seqlen_in_batch, _ = unpad_input(key, mask)
                value, _, _, _, _ = unpad_input(value, mask)
                x = flash_attn_varlen_func(
                    query,
                    key,
                    value,
                    q_cu_seqlens,
                    k_cu_seqlens,
                    q_max_seqlen_in_batch,
                    k_max_seqlen_in_batch,
                )
                x = pad_input(x, indices, batch_size, q_max_seqlen_in_batch)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)
            else:
                x = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)

        x = x.to(query.dtype)

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)

        return x

# Joint Attention processor for MM-DiT
# modified from diffusers/src/diffusers/models/attention_processor.py

class JointAttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        c: float["b nt d"] = None,  # context c, here text
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.FloatTensor:
        residual = x

        batch_size = c.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # `context` projections
        c_query = attn.to_q_c(c)
        c_key = attn.to_k_c(c)
        c_value = attn.to_v_c(c)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)
        if attn.c_q_norm is not None:
            c_query = attn.c_q_norm(c_query)
        if attn.c_k_norm is not None:
            c_key = attn.c_k_norm(c_key)

        # apply rope for context and noised input independently
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
        if c_rope is not None:
# ... truncated ...


import math
from typing import Optional

import torch
import torch.nn.functional as F
import torchaudio
from librosa.filters import mel as librosa_mel_fn
from torch import nn
from x_transformers.x_transformers import apply_rotary_pos_emb

from f5_tts.model_new.utils import is_package_available

# raw wav to mel spec

mel_basis_cache = {}
hann_window_cache = {}

def get_bigvgan_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
    fmin=0,
    fmax=None,
    center=False,
):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
    device = waveform.device
    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"

    if key not in mel_basis_cache:
        mel = librosa_mel_fn(sr=target_sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=fmin, fmax=fmax)
        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)  # TODO: why they need .float()?
        hann_window_cache[key] = torch.hann_window(win_length).to(device)

    mel_basis = mel_basis_cache[key]
    hann_window = hann_window_cache[key]

    padding = (n_fft - hop_length) // 2
    waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)

    spec = torch.stft(
        waveform,
        n_fft,
        hop_length=hop_length,
        win_length=win_length,
        window=hann_window,
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=True,
    )
    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)

    mel_spec = torch.matmul(mel_basis, spec)
    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))

    return mel_spec

def get_vocos_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
):
    mel_stft = torchaudio.transforms.MelSpectrogram(
        sample_rate=target_sample_rate,
        n_fft=n_fft,
        win_length=win_length,
        hop_length=hop_length,
        n_mels=n_mel_channels,
        power=1,
        center=True,
        normalized=False,
        norm=None,


    spec = torch.stft(
        waveform,
        n_fft,
        hop_length=hop_length,
        win_length=win_length,
        window=hann_window,
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=True,
    )
    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)

    mel_spec = torch.matmul(mel_basis, spec)
    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))

    return mel_spec

def get_vocos_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
):
    mel_stft = torchaudio.transforms.MelSpectrogram(
        sample_rate=target_sample_rate,
        n_fft=n_fft,
        win_length=win_length,
        hop_length=hop_length,
        n_mels=n_mel_channels,
        power=1,
        center=True,
        normalized=False,
        norm=None,
    ).to(waveform.device)
    if len(waveform.shape) == 3:
        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'

    assert len(waveform.shape) == 2

    mel = mel_stft(waveform)
    mel = mel.clamp(min=1e-5).log()
    return mel

class MelSpec(nn.Module):
    def __init__(
        self,
        n_fft=1024,
        hop_length=256,
        win_length=1024,
        n_mel_channels=100,
        target_sample_rate=24_000,
        mel_spec_type="vocos",
    ):
        super().__init__()
        assert mel_spec_type in ["vocos", "bigvgan"], print("We only support two extract mel backend: vocos or bigvgan")

        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.target_sample_rate = target_sample_rate


MelSpec

        sample_rate=target_sample_rate,
        n_fft=n_fft,
        win_length=win_length,
        hop_length=hop_length,
        n_mels=n_mel_channels,
        power=1,
        center=True,
        normalized=False,
        norm=None,
    ).to(waveform.device)
    if len(waveform.shape) == 3:
        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'

    assert len(waveform.shape) == 2

    mel = mel_stft(waveform)
    mel = mel.clamp(min=1e-5).log()
    return mel

class MelSpec(nn.Module):
    def __init__(
        self,
        n_fft=1024,
        hop_length=256,
        win_length=1024,
        n_mel_channels=100,
        target_sample_rate=24_000,
        mel_spec_type="vocos",
    ):
        super().__init__()
        assert mel_spec_type in ["vocos", "bigvgan"], print("We only support two extract mel backend: vocos or bigvgan")

        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.target_sample_rate = target_sample_rate

        if mel_spec_type == "vocos":
            self.extractor = get_vocos_mel_spectrogram
        elif mel_spec_type == "bigvgan":
            self.extractor = get_bigvgan_mel_spectrogram

        self.register_buffer("dummy", torch.tensor(0), persistent=False)

    def forward(self, wav):
        if self.dummy.device != wav.device:
            self.to(wav.device)

        mel = self.extractor(
            waveform=wav,
            n_fft=self.n_fft,
            n_mel_channels=self.n_mel_channels,
            target_sample_rate=self.target_sample_rate,
            hop_length=self.hop_length,
            win_length=self.win_length,
        )

        return mel

# sinusoidal position embedding

class SinusPositionEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb


SinusPositionEmbedding

    def forward(self, wav):
        if self.dummy.device != wav.device:
            self.to(wav.device)

        mel = self.extractor(
            waveform=wav,
            n_fft=self.n_fft,
            n_mel_channels=self.n_mel_channels,
            target_sample_rate=self.target_sample_rate,
            hop_length=self.hop_length,
            win_length=self.win_length,
        )

        return mel

# sinusoidal position embedding

class SinusPositionEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb

# convolutional position embedding

class ConvPositionEmbedding(nn.Module):
    def __init__(self, dim, kernel_size=31, groups=16):
        super().__init__()
        assert kernel_size % 2 != 0
        self.conv1d = nn.Sequential(
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
        )

    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.0)


ConvPositionEmbedding

class SinusPositionEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb

# convolutional position embedding

class ConvPositionEmbedding(nn.Module):
    def __init__(self, dim, kernel_size=31, groups=16):
        super().__init__()
        assert kernel_size % 2 != 0
        self.conv1d = nn.Sequential(
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
        )

    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.0)

        x = x.permute(0, 2, 1)
        x = self.conv1d(x)
        out = x.permute(0, 2, 1)

        if mask is not None:
            out = out.masked_fill(~mask, 0.0)

        return out

# rotary positional embedding related

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.0):


        )

    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.0)

        x = x.permute(0, 2, 1)
        x = self.conv1d(x)
        out = x.permute(0, 2, 1)

        if mask is not None:
            out = out.masked_fill(~mask, 0.0)

        return out

# rotary positional embedding related

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.0):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
    pos = (
        start.unsqueeze(1)
        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
    )
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos

# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()


        return out

# rotary positional embedding related

def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.0):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
    pos = (
        start.unsqueeze(1)
        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
    )
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos

# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x

# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108


GRN

    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)

def get_pos_embed_indices(start, length, max_pos, scale=1.0):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
    pos = (
        start.unsqueeze(1)
        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
    )
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos

# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x

# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108

class ConvNeXtV2Block(nn.Module):
    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        dilation: int = 1,
    ):
        super().__init__()
        padding = (dilation * (7 - 1)) // 2
        self.dwconv = nn.Conv1d(
            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
        )  # depthwise conv
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers


# Global Response Normalization layer (Instance Normalization ?)

class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x

# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108

class ConvNeXtV2Block(nn.Module):
    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        dilation: int = 1,
    ):
        super().__init__()
        padding = (dilation * (7 - 1)) // 2
        self.dwconv = nn.Conv1d(
            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
        )  # depthwise conv
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.grn = GRN(intermediate_dim)
        self.pwconv2 = nn.Linear(intermediate_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = x.transpose(1, 2)  # b n d -> b d n
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # b d n -> b n d
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        return residual + x

# RMSNorm

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4

    def forward(self, x):
        if self.native_rms_norm:
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
        else:
            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
            x = x * torch.rsqrt(variance + self.eps)


RMSNorm

        self.act = nn.GELU()
        self.grn = GRN(intermediate_dim)
        self.pwconv2 = nn.Linear(intermediate_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = x.transpose(1, 2)  # b n d -> b d n
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # b d n -> b n d
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        return residual + x

# RMSNorm

class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4

    def forward(self, x):
        if self.native_rms_norm:
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
        else:
            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
            x = x * torch.rsqrt(variance + self.eps)
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = x * self.weight

        return x

# AdaLayerNorm
# return with modulated x for attn input, and params for later mlp modulation

class AdaLayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb=None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]


AdaLayerNorm

    def forward(self, x):
        if self.native_rms_norm:
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
        else:
            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
            x = x * torch.rsqrt(variance + self.eps)
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = x * self.weight

        return x

# AdaLayerNorm
# return with modulated x for attn input, and params for later mlp modulation

class AdaLayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb=None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

# AdaLayerNorm for final layer
# return only with modulated x for attn input, cuz no more mlp modulation

class AdaLayerNorm_Final(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]


    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb=None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

# AdaLayerNorm for final layer
# return only with modulated x for attn input, cuz no more mlp modulation

class AdaLayerNorm_Final(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x

# FeedForward

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.ff(x)

# Attention with possible joint part


FeedForward

class AdaLayerNorm_Final(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x

# FeedForward

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.ff(x)

# Attention with possible joint part
# modified from diffusers/src/diffusers/models/attention_processor.py

class Attention(nn.Module):
    def __init__(
        self,
        processor: JointAttnProcessor | AttnProcessor,
        dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        context_dim: Optional[int] = None,  # if not None -> joint attention
        context_pre_only: bool = False,
        qk_norm: Optional[str] = None,
    ):
        super().__init__()


Attention

class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.ff(x)

# Attention with possible joint part
# modified from diffusers/src/diffusers/models/attention_processor.py

class Attention(nn.Module):
    def __init__(
        self,
        processor: JointAttnProcessor | AttnProcessor,
        dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        context_dim: Optional[int] = None,  # if not None -> joint attention
        context_pre_only: bool = False,
        qk_norm: Optional[str] = None,
    ):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.processor = processor

        self.dim = dim
        self.heads = heads
        self.inner_dim = dim_head * heads
        self.dropout = dropout

        self.context_dim = context_dim
        self.context_pre_only = context_pre_only

        self.to_q = nn.Linear(dim, self.inner_dim)
        self.to_k = nn.Linear(dim, self.inner_dim)
        self.to_v = nn.Linear(dim, self.inner_dim)

        if qk_norm is None:
            self.q_norm = None
            self.k_norm = None
        elif qk_norm == "rms_norm":
            self.q_norm = RMSNorm(dim_head, eps=1e-6)
            self.k_norm = RMSNorm(dim_head, eps=1e-6)
        else:
            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")

        if self.context_dim is not None:
            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
            if qk_norm is None:
                self.c_q_norm = None
                self.c_k_norm = None
            elif qk_norm == "rms_norm":
                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)

        self.to_out = nn.ModuleList([])
        self.to_out.append(nn.Linear(self.inner_dim, dim))
        self.to_out.append(nn.Dropout(dropout))

        if self.context_dim is not None and not self.context_pre_only:
            self.to_out_c = nn.Linear(self.inner_dim, context_dim)

    def forward(
        self,
        x: float["b n d"],  # noised input x
        c: float["b n d"] = None,  # context c
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.Tensor:
        if c is not None:
            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
        else:
            return self.processor(self, x, mask=mask, rope=rope)

# Attention processor

if is_package_available("flash_attn"):
    from flash_attn.bert_padding import pad_input, unpad_input
    from flash_attn import flash_attn_varlen_func, flash_attn_func

class AttnProcessor:
    def __init__(
        self,
        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
        attn_backend: str = "torch",  # "torch" or "flash_attn"
        attn_mask_enabled: bool = True,
    ):
        if attn_backend == "flash_attn":
            assert is_package_available("flash_attn"), "Please install flash-attn first."

        self.pe_attn_head = pe_attn_head


AttnProcessor

        self,
        x: float["b n d"],  # noised input x
        c: float["b n d"] = None,  # context c
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.Tensor:
        if c is not None:
            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
        else:
            return self.processor(self, x, mask=mask, rope=rope)

# Attention processor

if is_package_available("flash_attn"):
    from flash_attn.bert_padding import pad_input, unpad_input
    from flash_attn import flash_attn_varlen_func, flash_attn_func

class AttnProcessor:
    def __init__(
        self,
        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
        attn_backend: str = "torch",  # "torch" or "flash_attn"
        attn_mask_enabled: bool = True,
    ):
        if attn_backend == "flash_attn":
            assert is_package_available("flash_attn"), "Please install flash-attn first."

        self.pe_attn_head = pe_attn_head
        self.attn_backend = attn_backend
        self.attn_mask_enabled = attn_mask_enabled

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding
    ) -> torch.FloatTensor:
        batch_size = x.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)

        # apply rotary position embedding
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)

            if self.pe_attn_head is not None:
                pn = self.pe_attn_head
                query[:, :pn, :, :] = apply_rotary_pos_emb(query[:, :pn, :, :], freqs, q_xpos_scale)
                key[:, :pn, :, :] = apply_rotary_pos_emb(key[:, :pn, :, :], freqs, k_xpos_scale)
            else:
                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)

        if self.attn_backend == "torch":
            # mask. e.g. inference got a batch with different target durations, mask out the padding
            if self.attn_mask_enabled and mask is not None:
                attn_mask = mask
                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
                attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
            else:
                attn_mask = None
            x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
            x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        elif self.attn_backend == "flash_attn":
            query = query.transpose(1, 2)  # [b, h, n, d] -> [b, n, h, d]
            key = key.transpose(1, 2)
            value = value.transpose(1, 2)
            if self.attn_mask_enabled and mask is not None:
                query, indices, q_cu_seqlens, q_max_seqlen_in_batch, _ = unpad_input(query, mask)
                key, _, k_cu_seqlens, k_max_seqlen_in_batch, _ = unpad_input(key, mask)
                value, _, _, _, _ = unpad_input(value, mask)
                x = flash_attn_varlen_func(
                    query,
                    key,
                    value,
                    q_cu_seqlens,
                    k_cu_seqlens,
                    q_max_seqlen_in_batch,
                    k_max_seqlen_in_batch,
                )
                x = pad_input(x, indices, batch_size, q_max_seqlen_in_batch)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)
            else:
                x = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)

        x = x.to(query.dtype)

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)

        return x

# Joint Attention processor for MM-DiT
# modified from diffusers/src/diffusers/models/attention_processor.py

class JointAttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        c: float["b nt d"] = None,  # context c, here text
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.FloatTensor:
        residual = x


JointAttnProcessor

                x = x.reshape(batch_size, -1, attn.heads * head_dim)

        x = x.to(query.dtype)

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)

        return x

# Joint Attention processor for MM-DiT
# modified from diffusers/src/diffusers/models/attention_processor.py

class JointAttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        c: float["b nt d"] = None,  # context c, here text
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.FloatTensor:
        residual = x

        batch_size = c.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # `context` projections
        c_query = attn.to_q_c(c)
        c_key = attn.to_k_c(c)
        c_value = attn.to_v_c(c)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)
        if attn.c_q_norm is not None:
            c_query = attn.c_q_norm(c_query)
        if attn.c_k_norm is not None:
            c_key = attn.c_k_norm(c_key)

        # apply rope for context and noised input independently
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
        if c_rope is not None:
            freqs, xpos_scale = c_rope
            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)

        # joint attention
        query = torch.cat([query, c_query], dim=2)
        key = torch.cat([key, c_key], dim=2)
        value = torch.cat([value, c_value], dim=2)

        # mask. e.g. inference got a batch with different target durations, mask out the padding
        if mask is not None:
            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
        else:
            attn_mask = None

        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        x = x.to(query.dtype)

        # Split the attention outputs.
        x, c = (
            x[:, : residual.shape[1]],
            x[:, residual.shape[1] :],
        )

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)
        if not attn.context_pre_only:
            c = attn.to_out_c(c)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)
            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)

        return x, c

# DiT Block

class DiTBlock(nn.Module):
    def __init__(
        self,
        dim,
        heads,
        dim_head,
        ff_mult=4,
        dropout=0.1,
        qk_norm=None,
        pe_attn_head=None,
        attn_backend="torch",  # "torch" or "flash_attn"
        attn_mask_enabled=True,
    ):
        super().__init__()


DiTBlock

        )

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)
        if not attn.context_pre_only:
            c = attn.to_out_c(c)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)
            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)

        return x, c

# DiT Block

class DiTBlock(nn.Module):
    def __init__(
        self,
        dim,
        heads,
        dim_head,
        ff_mult=4,
        dropout=0.1,
        qk_norm=None,
        pe_attn_head=None,
        attn_backend="torch",  # "torch" or "flash_attn"
        attn_mask_enabled=True,
    ):
        super().__init__()

        self.attn_norm = AdaLayerNorm(dim)
        self.attn = Attention(
            processor=AttnProcessor(
                pe_attn_head=pe_attn_head,
                attn_backend=attn_backend,
                attn_mask_enabled=attn_mask_enabled,
            ),
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            dropout=dropout,
            qk_norm=qk_norm,
        )

        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")

    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
        # pre-norm & modulation for attention input
        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)

        # attention
        attn_output = self.attn(x=norm, mask=mask, rope=rope)

        # process attention output for input x
        x = x + gate_msa.unsqueeze(1) * attn_output

        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
        ff_output = self.ff(norm)
        x = x + gate_mlp.unsqueeze(1) * ff_output

        return x

# MMDiT Block https://arxiv.org/abs/2403.03206

class MMDiTBlock(nn.Module):
    r"""
    modified from diffusers/src/diffusers/models/attention.py

    notes.
    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
    _x: noised input related. (right part)
    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
    """

    def __init__(
        self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_dim=None, context_pre_only=False, qk_norm=None
    ):
        super().__init__()
        if context_dim is None:


MMDiTBlock

    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
        # pre-norm & modulation for attention input
        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)

        # attention
        attn_output = self.attn(x=norm, mask=mask, rope=rope)

        # process attention output for input x
        x = x + gate_msa.unsqueeze(1) * attn_output

        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
        ff_output = self.ff(norm)
        x = x + gate_mlp.unsqueeze(1) * ff_output

        return x

# MMDiT Block https://arxiv.org/abs/2403.03206

class MMDiTBlock(nn.Module):
    r"""
    modified from diffusers/src/diffusers/models/attention.py

    notes.
    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
    _x: noised input related. (right part)
    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
    """

    def __init__(
        self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_dim=None, context_pre_only=False, qk_norm=None
    ):
        super().__init__()
        if context_dim is None:
            context_dim = dim
        self.context_pre_only = context_pre_only

        self.attn_norm_c = AdaLayerNorm_Final(context_dim) if context_pre_only else AdaLayerNorm(context_dim)
        self.attn_norm_x = AdaLayerNorm(dim)
        self.attn = Attention(
            processor=JointAttnProcessor(),
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            dropout=dropout,
            context_dim=context_dim,
            context_pre_only=context_pre_only,
            qk_norm=qk_norm,
        )

        if not context_pre_only:
            self.ff_norm_c = nn.LayerNorm(context_dim, elementwise_affine=False, eps=1e-6)
            self.ff_c = FeedForward(dim=context_dim, mult=ff_mult, dropout=dropout, approximate="tanh")
        else:
            self.ff_norm_c = None
            self.ff_c = None
        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")

    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
        # pre-norm & modulation for attention input
        if self.context_pre_only:
            norm_c = self.attn_norm_c(c, t)
        else:
            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)

        # attention
        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)

        # process attention output for context c
        if self.context_pre_only:
            c = None
        else:  # if not last layer
            c = c + c_gate_msa.unsqueeze(1) * c_attn_output

            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            c_ff_output = self.ff_c(norm_c)
            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output

        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x

# time step conditioning embedding

class TimestepEmbedding(nn.Module):
    def __init__(self, dim, freq_embed_dim=256):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))

    def forward(self, timestep: float["b"]):
        time_hidden = self.time_embed(timestep)
        time_hidden = time_hidden.to(timestep.dtype)
        time = self.time_mlp(time_hidden)  # b d
        return time


TimestepEmbedding

        else:  # if not last layer
            c = c + c_gate_msa.unsqueeze(1) * c_attn_output

            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            c_ff_output = self.ff_c(norm_c)
            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output

        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x

# time step conditioning embedding

class TimestepEmbedding(nn.Module):
    def __init__(self, dim, freq_embed_dim=256):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))

    def forward(self, timestep: float["b"]):
        time_hidden = self.time_embed(timestep)
        time_hidden = time_hidden.to(timestep.dtype)
        time = self.time_mlp(time_hidden)  # b d
        return time


            c = c + c_gate_msa.unsqueeze(1) * c_attn_output

            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            c_ff_output = self.ff_c(norm_c)
            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output

        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x

# time step conditioning embedding

class TimestepEmbedding(nn.Module):
    def __init__(self, dim, freq_embed_dim=256):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))

    def forward(self, timestep: float["b"]):
        time_hidden = self.time_embed(timestep)
        time_hidden = time_hidden.to(timestep.dtype)
        time = self.time_mlp(time_hidden)  # b d
        return time


forward

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output

        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x

# time step conditioning embedding

class TimestepEmbedding(nn.Module):
    def __init__(self, dim, freq_embed_dim=256):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))

    def forward(self, timestep: float["b"]):
        time_hidden = self.time_embed(timestep)
        time_hidden = time_hidden.to(timestep.dtype)
        time = self.time_mlp(time_hidden)  # b d
        return time