from dataclasses import dataclass
import json
import os
from typing import Optional, Tuple, Union
from copy import deepcopy

import torch
import torch.nn as nn
from transformers import (
    CLIPTextModel,
    CLIPTokenizer,
    AutoTokenizer,
    AutoModel,
    CLIPConfig,
    LlamaForCausalLM,
    LlamaConfig,
)
from transformers.utils import ModelOutput
from transformers.models.llama import LlamaModel
from safetensors.torch import load_file
from accelerate import init_empty_weights

import logging

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


CLIP_L_HUGGINGFACE_MODEL_ID = "openai/clip-vit-large-patch14"
LLAVA_HUGGINGFACE_MODEL_ID = "xtuner/llava-llama-3-8b-v1_1-transformers"

CLIP_CONFIG = {
    "_name_or_path": "clip-vit-large-patch14/",
    "architectures": ["CLIPModel"],
    "initializer_factor": 1.0,
    "logit_scale_init_value": 2.6592,
    "model_type": "clip",
    "projection_dim": 768,
    #   "text_config": {
    "_name_or_path": "",
    "add_cross_attention": False,
    "architectures": None,
    "attention_dropout": 0.0,
    "bad_words_ids": None,
    "bos_token_id": 0,
    "chunk_size_feed_forward": 0,
    "cross_attention_hidden_size": None,
    "decoder_start_token_id": None,
    "diversity_penalty": 0.0,
    "do_sample": False,
    "dropout": 0.0,
    "early_stopping": False,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 2,
    "finetuning_task": None,
    "forced_bos_token_id": None,
    "forced_eos_token_id": None,
    "hidden_act": "quick_gelu",
    "hidden_size": 768,
    "id2label": {"0": "LABEL_0", "1": "LABEL_1"},
    "initializer_factor": 1.0,
    "initializer_range": 0.02,
    "intermediate_size": 3072,
    "is_decoder": False,
    "is_encoder_decoder": False,
    "label2id": {"LABEL_0": 0, "LABEL_1": 1},
    "layer_norm_eps": 1e-05,
    "length_penalty": 1.0,
    "max_length": 20,
    "max_position_embeddings": 77,
    "min_length": 0,
    "model_type": "clip_text_model",
    "no_repeat_ngram_size": 0,
    "num_attention_heads": 12,
    "num_beam_groups": 1,
    "num_beams": 1,
    "num_hidden_layers": 12,
    "num_return_sequences": 1,
    "output_attentions": False,
    "output_hidden_states": False,
    "output_scores": False,
    "pad_token_id": 1,
    "prefix": None,
    "problem_type": None,
    "projection_dim": 768,
    "pruned_heads": {},
    "remove_invalid_values": False,
    "repetition_penalty": 1.0,
    "return_dict": True,
    "return_dict_in_generate": False,
    "sep_token_id": None,
    "task_specific_params": None,
    "temperature": 1.0,
    "tie_encoder_decoder": False,
    "tie_word_embeddings": True,
    "tokenizer_class": None,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": None,
    "torchscript": False,
    "transformers_version": "4.16.0.dev0",
    "use_bfloat16": False,
    "vocab_size": 49408,
    #   },
    #   "text_config_dict": {
    "hidden_size": 768,
    "intermediate_size": 3072,
    "num_attention_heads": 12,
    "num_hidden_layers": 12,
    "projection_dim": 768,
    #   },
    #   "torch_dtype": "float32",
    #   "transformers_version": null
}

LLAMA_CONFIG = {
    "architectures": ["LlamaForCausalLM"],
    "attention_bias": False,
    "attention_dropout": 0.0,
    "bos_token_id": 128000,
    "eos_token_id": 128001,
    "head_dim": 128,
    "hidden_act": "silu",
    "hidden_size": 4096,
    "initializer_range": 0.02,
    "intermediate_size": 14336,
    "max_position_embeddings": 8192,
    "mlp_bias": False,
    "model_type": "llama",
    "num_attention_heads": 32,
    "num_hidden_layers": 32,
    "num_key_value_heads": 8,
    "pretraining_tp": 1,
    "rms_norm_eps": 1e-05,
    "rope_scaling": None,
    "rope_theta": 500000.0,
    "tie_word_embeddings": False,
    "torch_dtype": "float16",
    "transformers_version": "4.46.3",
    "use_cache": True,
    "vocab_size": 128320,
}

# When using decoder-only models, we must provide a prompt template to instruct the text encoder
# on how to generate the text.
# --------------------------------------------------------------------
PROMPT_TEMPLATE_ENCODE = (
    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
    "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
)
PROMPT_TEMPLATE_ENCODE_VIDEO = (
    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
    "1. The main content and theme of the video."
    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
    "4. background environment, light, style and atmosphere."
    "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
)

NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"

PROMPT_TEMPLATE = {
    "dit-llm-encode": {
        "template": PROMPT_TEMPLATE_ENCODE,
        "crop_start": 36,
    },
    "dit-llm-encode-video": {
        "template": PROMPT_TEMPLATE_ENCODE_VIDEO,
        "crop_start": 95,
    },
}


def use_default(value, default):
    return value if value is not None else default


def load_clip_l(text_encoder_path: str, dtype: Optional[Union[str, torch.dtype]] = None):
    if os.path.isdir(text_encoder_path):
        # load from directory, configs are in the directory
        text_encoder = CLIPTextModel.from_pretrained(text_encoder_path, torch_dtype=dtype)
    else:
        # load from file, we create the model with the appropriate config
        config = CLIPConfig(**CLIP_CONFIG)
        with init_empty_weights():
            text_encoder = CLIPTextModel._from_config(config, torch_dtype=dtype)

        state_dict = load_file(text_encoder_path)

        text_encoder.load_state_dict(state_dict, strict=True, assign=True)
    # if dtype is not None:
    #     text_encoder.to(dtype=dtype)

    return text_encoder


def load_clip_l_tokenizer(tokenizer_path: str):
    if os.path.isdir(tokenizer_path):
        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
    else:
        # load from Hugging Face
        logger.info(f"Loading tokenizer from Hugging Face: {CLIP_L_HUGGINGFACE_MODEL_ID}")
        tokenizer = CLIPTokenizer.from_pretrained(CLIP_L_HUGGINGFACE_MODEL_ID, max_length=77)

    return tokenizer


def load_llm(text_encoder_path: str, dtype: Optional[Union[str, torch.dtype]] = None):
    if os.path.isdir(text_encoder_path):
        # load from directory, configs are in the directory
        text_encoder = AutoModel.from_pretrained(text_encoder_path, low_cpu_mem_usage=True, torch_dtype=dtype)
    else:
        # load from file, we create the model with the appropriate config
        config = LlamaConfig(**LLAMA_CONFIG)
        with init_empty_weights():
            text_encoder = LlamaForCausalLM._from_config(config, torch_dtype=dtype)

        state_dict = load_file(text_encoder_path)

        # support weights from ComfyUI
        if "tokenizer" in state_dict:
            state_dict.pop("tokenizer")

        text_encoder.load_state_dict(state_dict, strict=True, assign=True)

    return text_encoder


def load_llm_tokenizer(tokenizer_path: str, padding_side="right"):
    if os.path.isdir(tokenizer_path):
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
    else:
        # load from Hugging Face
        logger.info(f"Loading tokenizer from Hugging Face: {LLAVA_HUGGINGFACE_MODEL_ID}")
        tokenizer = AutoTokenizer.from_pretrained(LLAVA_HUGGINGFACE_MODEL_ID, padding_side=padding_side)

    return tokenizer


def load_text_encoder(
    text_encoder_type: str,
    text_encoder_path: str,
    text_encoder_dtype: Optional[Union[str, torch.dtype]] = None,
):
    logger.info(f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}")

    # reduce peak memory usage by specifying the dtype of the model
    dtype = text_encoder_dtype
    if text_encoder_type == "clipL":
        text_encoder = load_clip_l(text_encoder_path, dtype=dtype)
        text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
    elif text_encoder_type == "llm":
        text_encoder = load_llm(text_encoder_path, dtype=dtype)
        if hasattr(text_encoder, "norm"):
            text_encoder.final_layer_norm = text_encoder.norm  # by from_pretrained
        else:
            text_encoder.final_layer_norm = text_encoder.model.norm  # by _from_config
    else:
        raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
    # from_pretrained will ensure that the model is in eval mode.

    if dtype is not None:
        text_encoder = text_encoder.to(dtype=dtype)

    text_encoder.requires_grad_(False)

    logger.info(f"Text encoder to dtype: {text_encoder.dtype}")
    return text_encoder, text_encoder_path


def load_tokenizer(tokenizer_type, tokenizer_path=None, padding_side="right"):
    logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")

    if tokenizer_type == "clipL":
        tokenizer = load_clip_l_tokenizer(tokenizer_path)
    elif tokenizer_type == "llm":
        tokenizer = load_llm_tokenizer(tokenizer_path, padding_side=padding_side)
    else:
        raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")

    return tokenizer, tokenizer_path


@dataclass
class TextEncoderModelOutput(ModelOutput):
    """
    Base class for model's outputs that also contains a pooling of the last hidden states.

    Args:
        hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
        hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        text_outputs (`list`, *optional*, returned when `return_texts=True` is passed):
            List of decoded texts.
    """

    hidden_state: torch.FloatTensor = None
    attention_mask: Optional[torch.LongTensor] = None
    hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None
    text_outputs: Optional[list] = None


class TextEncoder(nn.Module):
    def __init__(
        self,
        text_encoder_type: str,
        max_length: int,
        text_encoder_dtype: Optional[Union[str, torch.dtype]] = None,
        text_encoder_path: Optional[str] = None,
        tokenizer_type: Optional[str] = None,
        tokenizer_path: Optional[str] = None,
        output_key: Optional[str] = None,
        use_attention_mask: bool = True,
        input_max_length: Optional[int] = None,
        prompt_template: Optional[dict] = None,
        prompt_template_video: Optional[dict] = None,
        hidden_state_skip_layer: Optional[int] = None,
        apply_final_norm: bool = False,
        reproduce: bool = False,
    ):
        super().__init__()
        self.text_encoder_type = text_encoder_type
        self.max_length = max_length
        # self.precision = text_encoder_precision
        self.model_path = text_encoder_path
        self.tokenizer_type = tokenizer_type if tokenizer_type is not None else text_encoder_type
        self.tokenizer_path = tokenizer_path if tokenizer_path is not None else text_encoder_path
        self.use_attention_mask = use_attention_mask
        if prompt_template_video is not None:
            assert use_attention_mask is True, "Attention mask is True required when training videos."
        self.input_max_length = input_max_length if input_max_length is not None else max_length
        self.prompt_template = prompt_template
        self.prompt_template_video = prompt_template_video
        self.hidden_state_skip_layer = hidden_state_skip_layer
        self.apply_final_norm = apply_final_norm
        self.reproduce = reproduce

        self.use_template = self.prompt_template is not None
        if self.use_template:
            assert (
                isinstance(self.prompt_template, dict) and "template" in self.prompt_template
            ), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}"
            assert "{}" in str(self.prompt_template["template"]), (
                "`prompt_template['template']` must contain a placeholder `{}` for the input text, "
                f"got {self.prompt_template['template']}"
            )

        self.use_video_template = self.prompt_template_video is not None
        if self.use_video_template:
            if self.prompt_template_video is not None:
                assert (
                    isinstance(self.prompt_template_video, dict) and "template" in self.prompt_template_video
                ), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}"
            assert "{}" in str(self.prompt_template_video["template"]), (
                "`prompt_template_video['template']` must contain a placeholder `{}` for the input text, "
                f"got {self.prompt_template_video['template']}"
            )

        if "t5" in text_encoder_type:
            self.output_key = output_key or "last_hidden_state"
        elif "clip" in text_encoder_type:
            self.output_key = output_key or "pooler_output"
        elif "llm" in text_encoder_type or "glm" in text_encoder_type:
            self.output_key = output_key or "last_hidden_state"
        else:
            raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")

        self.model, self.model_path = load_text_encoder(
            text_encoder_type=self.text_encoder_type, text_encoder_path=self.model_path, text_encoder_dtype=text_encoder_dtype
        )
        self.dtype = self.model.dtype

        self.tokenizer, self.tokenizer_path = load_tokenizer(
            tokenizer_type=self.tokenizer_type, tokenizer_path=self.tokenizer_path, padding_side="right"
        )

    def __repr__(self):
        return f"{self.text_encoder_type} ({self.precision} - {self.model_path})"

    @property
    def device(self):
        return self.model.device

    @staticmethod
    def apply_text_to_template(text, template, prevent_empty_text=True):
        """
        Apply text to template.

        Args:
            text (str): Input text.
            template (str or list): Template string or list of chat conversation.
            prevent_empty_text (bool): If Ture, we will prevent the user text from being empty
                by adding a space. Defaults to True.
        """
        if isinstance(template, str):
            # Will send string to tokenizer. Used for llm
            return template.format(text)
        else:
            raise TypeError(f"Unsupported template type: {type(template)}")

    def text2tokens(self, text, data_type="image"):
        """
        Tokenize the input text.

        Args:
            text (str or list): Input text.
        """
        tokenize_input_type = "str"
        if self.use_template:
            if data_type == "image":
                prompt_template = self.prompt_template["template"]
            elif data_type == "video":
                prompt_template = self.prompt_template_video["template"]
            else:
                raise ValueError(f"Unsupported data type: {data_type}")
            if isinstance(text, (list, tuple)):
                text = [self.apply_text_to_template(one_text, prompt_template) for one_text in text]
                if isinstance(text[0], list):
                    tokenize_input_type = "list"
            elif isinstance(text, str):
                text = self.apply_text_to_template(text, prompt_template)
                if isinstance(text, list):
                    tokenize_input_type = "list"
            else:
                raise TypeError(f"Unsupported text type: {type(text)}")

        kwargs = dict(
            truncation=True,
            max_length=self.max_length,
            padding="max_length",
            return_tensors="pt",
        )
        if tokenize_input_type == "str":
            return self.tokenizer(
                text,
                return_length=False,
                return_overflowing_tokens=False,
                return_attention_mask=True,
                **kwargs,
            )
        elif tokenize_input_type == "list":
            return self.tokenizer.apply_chat_template(
                text,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                **kwargs,
            )
        else:
            raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")

    def encode(
        self,
        batch_encoding,
        use_attention_mask=None,
        output_hidden_states=False,
        do_sample=None,
        hidden_state_skip_layer=None,
        return_texts=False,
        data_type="image",
        device=None,
    ):
        """
        Args:
            batch_encoding (dict): Batch encoding from tokenizer.
            use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask.
                Defaults to None.
            output_hidden_states (bool): Whether to output hidden states. If False, return the value of
                self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer,
                output_hidden_states will be set True. Defaults to False.
            do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None.
                When self.produce is False, do_sample is set to True by default.
            hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer.
                If None, self.output_key will be used. Defaults to None.
            return_texts (bool): Whether to return the decoded texts. Defaults to False.
        """
        device = self.model.device if device is None else device
        use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
        hidden_state_skip_layer = use_default(hidden_state_skip_layer, self.hidden_state_skip_layer)
        do_sample = use_default(do_sample, not self.reproduce)
        attention_mask = batch_encoding["attention_mask"].to(device) if use_attention_mask else None
        outputs = self.model(
            input_ids=batch_encoding["input_ids"].to(device),
            attention_mask=attention_mask,
            output_hidden_states=output_hidden_states or hidden_state_skip_layer is not None,
        )
        if hidden_state_skip_layer is not None:
            last_hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
            # Real last hidden state already has layer norm applied. So here we only apply it
            # for intermediate layers.
            if hidden_state_skip_layer > 0 and self.apply_final_norm:
                last_hidden_state = self.model.final_layer_norm(last_hidden_state)
        else:
            last_hidden_state = outputs[self.output_key]

        # Remove hidden states of instruction tokens, only keep prompt tokens.
        if self.use_template:
            if data_type == "image":
                crop_start = self.prompt_template.get("crop_start", -1)
            elif data_type == "video":
                crop_start = self.prompt_template_video.get("crop_start", -1)
            else:
                raise ValueError(f"Unsupported data type: {data_type}")
            if crop_start > 0:
                last_hidden_state = last_hidden_state[:, crop_start:]
                attention_mask = attention_mask[:, crop_start:] if use_attention_mask else None

        if output_hidden_states:
            return TextEncoderModelOutput(last_hidden_state, attention_mask, outputs.hidden_states)
        return TextEncoderModelOutput(last_hidden_state, attention_mask)

    def forward(
        self,
        text,
        use_attention_mask=None,
        output_hidden_states=False,
        do_sample=False,
        hidden_state_skip_layer=None,
        return_texts=False,
    ):
        batch_encoding = self.text2tokens(text)
        return self.encode(
            batch_encoding,
            use_attention_mask=use_attention_mask,
            output_hidden_states=output_hidden_states,
            do_sample=do_sample,
            hidden_state_skip_layer=hidden_state_skip_layer,
            return_texts=return_texts,
        )


# region HunyanVideo architecture


def load_text_encoder_1(
    text_encoder_dir: str, device: torch.device, fp8_llm: bool, dtype: Optional[Union[str, torch.dtype]] = None
) -> TextEncoder:
    text_encoder_dtype = dtype or torch.float16
    text_encoder_type = "llm"
    text_len = 256
    hidden_state_skip_layer = 2
    apply_final_norm = False
    reproduce = False

    prompt_template = "dit-llm-encode"
    prompt_template = PROMPT_TEMPLATE[prompt_template]
    prompt_template_video = "dit-llm-encode-video"
    prompt_template_video = PROMPT_TEMPLATE[prompt_template_video]

    crop_start = prompt_template_video["crop_start"]  # .get("crop_start", 0)
    max_length = text_len + crop_start

    text_encoder_1 = TextEncoder(
        text_encoder_type=text_encoder_type,
        max_length=max_length,
        text_encoder_dtype=text_encoder_dtype,
        text_encoder_path=text_encoder_dir,
        tokenizer_type=text_encoder_type,
        prompt_template=prompt_template,
        prompt_template_video=prompt_template_video,
        hidden_state_skip_layer=hidden_state_skip_layer,
        apply_final_norm=apply_final_norm,
        reproduce=reproduce,
    )
    text_encoder_1.eval()

    if fp8_llm:
        org_dtype = text_encoder_1.dtype
        logger.info(f"Moving and casting text encoder to {device} and torch.float8_e4m3fn")
        text_encoder_1.to(device=device, dtype=torch.float8_e4m3fn)

        # prepare LLM for fp8
        def prepare_fp8(llama_model: LlamaModel, target_dtype):
            def forward_hook(module):
                def forward(hidden_states):
                    input_dtype = hidden_states.dtype
                    hidden_states = hidden_states.to(torch.float32)
                    variance = hidden_states.pow(2).mean(-1, keepdim=True)
                    hidden_states = hidden_states * torch.rsqrt(variance + module.variance_epsilon)
                    return module.weight.to(input_dtype) * hidden_states.to(input_dtype)

                return forward

            for module in llama_model.modules():
                if module.__class__.__name__ in ["Embedding"]:
                    # print("set", module.__class__.__name__, "to", target_dtype)
                    module.to(target_dtype)
                if module.__class__.__name__ in ["LlamaRMSNorm"]:
                    # print("set", module.__class__.__name__, "hooks")
                    module.forward = forward_hook(module)

        prepare_fp8(text_encoder_1.model, org_dtype)
    else:
        text_encoder_1.to(device=device)

    return text_encoder_1


def load_text_encoder_2(
    text_encoder_dir: str, device: torch.device, dtype: Optional[Union[str, torch.dtype]] = None
) -> TextEncoder:
    text_encoder_dtype = dtype or torch.float16
    reproduce = False

    text_encoder_2_type = "clipL"
    text_len_2 = 77

    text_encoder_2 = TextEncoder(
        text_encoder_type=text_encoder_2_type,
        max_length=text_len_2,
        text_encoder_dtype=text_encoder_dtype,
        text_encoder_path=text_encoder_dir,
        tokenizer_type=text_encoder_2_type,
        reproduce=reproduce,
    )
    text_encoder_2.eval()

    text_encoder_2.to(device=device)

    return text_encoder_2


# endregion


if __name__ == "__main__":
    import argparse
    from utils.model_utils import str_to_dtype

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    parser = argparse.ArgumentParser()
    parser.add_argument("type", type=str, help="Text Encoder type")
    parser.add_argument("path1", type=str, help="Text Encoder directory or file 1")
    parser.add_argument("path2", type=str, help="Text Encoder directory or file 2")
    parser.add_argument("--dtype", type=str, default=None, help="Data type for Text Encoder")
    args = parser.parse_args()

    dtype = str_to_dtype(args.dtype) if args.dtype is not None else torch.float16

    """
    if args.type == "clipL":
        text_encoder_1st = load_clip_l(args.path1, dtype=dtype)
        tokenizer_1st = load_clip_l_tokenizer(args.path1)
        text_encoder_2nd = load_clip_l(args.path2, dtype=dtype)
        tokenizer_2nd = load_clip_l_tokenizer(args.path2)
    elif args.type == "llm":
        text_encoder_1st = load_llm(args.path1, dtype=dtype)
        tokenizer_1st = load_llm_tokenizer(args.path1)
        text_encoder_2nd = load_llm(args.path2, dtype=dtype)
        tokenizer_2nd = load_llm_tokenizer(args.path2)

    print(f"1st Text Encoder dtype: {text_encoder_1st.dtype}")
    print(f"2nd Text Encoder dtype: {text_encoder_2nd.dtype}")

    text_encoder_1st.to(device=device)
    text_encoder_2nd.to(device=device)

    test_text = "A cat sitting on a table"
    token_ids_1st = tokenizer_1st(test_text, return_tensors="pt")["input_ids"]
    token_ids_2nd = tokenizer_2nd(test_text, return_tensors="pt")["input_ids"]
    assert torch.allclose(token_ids_1st, token_ids_2nd)
    print(f"Token IDs are the same: {token_ids_1st}")

    with torch.no_grad():
        text_encoder_1st_output = text_encoder_1st(token_ids_1st.to(device), output_hidden_states=True)
        text_encoder_2nd_output = text_encoder_2nd(token_ids_2nd.to(device), output_hidden_states=True)
    print(f"1st Text Encoder output keys: {text_encoder_1st_output.keys()}")
    print(f"2nd Text Encoder output keys: {text_encoder_2nd_output.keys()}")
    for key in text_encoder_1st_output:
        print(f"Checking output: {key}")
        assert key in text_encoder_2nd_output, f"Key {key} not in 2nd Text Encoder output"
        assert torch.allclose(text_encoder_1st_output[key], text_encoder_2nd_output[key])
        print(f"Outputs are the same: {key}")
    print("All outputs are the same.")
    """

    if args.type == "clipL":
        text_encoder_1st = load_text_encoder_2(args.path1, device, dtype)
        text_encoder_2nd = load_text_encoder_2(args.path2, device, dtype)
    elif args.type == "llm":
        text_encoder_1st = load_text_encoder_1(args.path1, device, False, dtype)
        text_encoder_2nd = load_text_encoder_1(args.path2, device, False, dtype)
    print(f"1st Text Encoder dtype: {text_encoder_1st.dtype}")
    print(f"2nd Text Encoder dtype: {text_encoder_2nd.dtype}")

    prompt = "A cat sitting on a table"
    data_type = "video"  # video only, image is not supported
    text_inputs_1st = text_encoder_1st.text2tokens(prompt, data_type=data_type)
    text_inputs_2nd = text_encoder_2nd.text2tokens(prompt, data_type=data_type)
    print(text_inputs_1st)
    assert torch.allclose(text_inputs_1st["input_ids"], text_inputs_2nd["input_ids"])

    with torch.no_grad():
        prompt_outputs_1st = text_encoder_1st.encode(text_inputs_1st, data_type=data_type)
        prompt_outputs_2nd = text_encoder_2nd.encode(text_inputs_1st, data_type=data_type)

    # prompt_outputs.hidden_state, prompt_outputs.attention_mask
    assert torch.allclose(prompt_outputs_1st.hidden_state, prompt_outputs_2nd.hidden_state)
    print("Hidden states are the same.")
    assert torch.allclose(prompt_outputs_1st.attention_mask, prompt_outputs_2nd.attention_mask)
    print("Attention masks are the same.")
    print("All outputs are the same.")
