from dataclasses import dataclass
from typing import Optional, Tuple
from copy import deepcopy

import torch
import torch.nn as nn
from transformers import CLIPTextModel, CLIPTokenizer, AutoTokenizer, AutoModel
from transformers.utils import ModelOutput

from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH
from ..constants import PRECISION_TO_TYPE


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


def load_text_encoder(
    text_encoder_type,
    text_encoder_precision=None,
    text_encoder_path=None,
    logger=None,
    device=None,
):
    if text_encoder_path is None:
        text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type]
    if logger is not None:
        logger.info(
            f"Loading text encoder model ({text_encoder_type}) from: {text_encoder_path}"
        )

    if text_encoder_type == "clipL":
        text_encoder = CLIPTextModel.from_pretrained(text_encoder_path)
        text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
    elif text_encoder_type == "llm":
        text_encoder = AutoModel.from_pretrained(
            text_encoder_path, low_cpu_mem_usage=True
        )
        text_encoder.final_layer_norm = text_encoder.norm
    else:
        raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
    # from_pretrained will ensure that the model is in eval mode.

    if text_encoder_precision is not None:
        text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision])

    text_encoder.requires_grad_(False)

    if logger is not None:
        logger.info(f"Text encoder to dtype: {text_encoder.dtype}")

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

    return text_encoder, text_encoder_path


def load_tokenizer(
    tokenizer_type, tokenizer_path=None, padding_side="right", logger=None
):
    if tokenizer_path is None:
        tokenizer_path = TOKENIZER_PATH[tokenizer_type]
    if logger is not None:
        logger.info(f"Loading tokenizer ({tokenizer_type}) from: {tokenizer_path}")

    if tokenizer_type == "clipL":
        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
    elif tokenizer_type == "llm":
        tokenizer = AutoTokenizer.from_pretrained(
            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_precision: Optional[str] = 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,
        logger=None,
        device=None,
    ):
        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.logger = logger

        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_precision=self.precision,
            text_encoder_path=self.model_path,
            logger=self.logger,
            device=device,
        )
        self.dtype = self.model.dtype
        self.device = self.model.device

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

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

    @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,
        )
