from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from PIL import Image
from torch.nn import CrossEntropyLoss
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    Qwen2Config,
    Qwen2ForCausalLM,
    Qwen2Model,
)
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import CausalLMOutputWithPast, MoeCausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput

from ..vita_arch import VITAMetaForCausalLM, VITAMetaModel


def custom_forward(
    self,
    input_ids: torch.LongTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
    r"""
    Args:
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, Qwen2ForCausalLM

    >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

    >>> prompt = "Hey, are you conscious? Can you talk to me?"
    >>> inputs = tokenizer(prompt, return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
    ```"""

    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
    outputs = self.model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
        cache_position=cache_position,
    )

    hidden_states = outputs[0]
    logits = self.lm_head(hidden_states)
    # logits = logits.float()

    loss = None
    if labels is not None:
        # Shift so that tokens < n predict n
        shift_logits = logits[..., :-1, :].contiguous()
        shift_labels = labels[..., 1:].contiguous()
        # Flatten the tokens
        loss_fct = CrossEntropyLoss()
        shift_logits = shift_logits.view(-1, self.config.vocab_size)
        shift_labels = shift_labels.view(-1)
        # Enable model parallelism
        shift_labels = shift_labels.to(shift_logits.device)
        loss = loss_fct(shift_logits, shift_labels)

    if not return_dict:
        output = (logits,) + outputs[1:]
        return (loss,) + output if loss is not None else output

    #import pdb; pdb.set_trace()
    return CausalLMOutputWithPast(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )


Qwen2ForCausalLM.forward = custom_forward


class VITAQwen2Config(Qwen2Config):
    model_type = "vita-Qwen2"


class VITAQwen2Model(VITAMetaModel, Qwen2Model):
    config_class = VITAQwen2Config

    def __init__(self, config: Qwen2Config):
        super(VITAQwen2Model, self).__init__(config)


class VITAQwen2ForCausalLM(Qwen2ForCausalLM, VITAMetaForCausalLM):
    config_class = VITAQwen2Config

    def __init__(self, config):
        super(Qwen2ForCausalLM, self).__init__(config)
        self.model = VITAQwen2Model(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        audios: Optional[dict] = None,
        sf_masks: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels,
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids, position_ids, attention_mask, past_key_values, labels, images, audios, sf_masks
            )

        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        audios: Optional[torch.Tensor] = None,
        sf_masks: Optional[torch.Tensor] = None,
        shared_v_pid_stride: Optional[int] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")

        if images is not None or audios is not None:
            (
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                _
            ) = self.prepare_inputs_labels_for_multimodal(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
                audios,
                sf_masks,
                shared_v_pid_stride,
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)

        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        attention_mask=None,
        **kwargs,
    ):
        images = kwargs.pop("images", None)
        audios = kwargs.pop("audios", None)
        sf_masks = kwargs.pop("sf_masks", None)

        _inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            **kwargs,
        )

#        import pdb; pdb.set_trace()
        position_ids = _inputs["position_ids"]
        cache_position = _inputs["cache_position"]
        if cache_position.shape[-1] == 1 and position_ids.shape[-1] > 1:
            new_position_ids = torch.zeros((position_ids.shape[0],1), dtype=position_ids.dtype, device=position_ids.device)
            new_position_ids[:, 0] = position_ids[0,-1] + cache_position[-1] + 1 - position_ids.shape[-1]
            position_ids = new_position_ids
            _inputs["position_ids"] = position_ids
#        import pdb; pdb.set_trace()

        if images is not None:
            _inputs["images"] = images
        if audios is not None:
            _inputs["audios"] = audios
        if sf_masks is not None:
            _inputs["sf_masks"] = sf_masks
        return _inputs

    def expand2square(self, pil_img, background_color):
        width, height = pil_img.size
        if width == height:
            return pil_img
        elif width > height:
            result = Image.new(pil_img.mode, (width, width), background_color)
            result.paste(pil_img, (0, (width - height) // 2))
            return result
        else:
            result = Image.new(pil_img.mode, (height, height), background_color)
            result.paste(pil_img, ((height - width) // 2, 0))
            return result

    def process_images(self, images, model_cfg):
        vision_tower = self.get_vision_tower()
        if not vision_tower.is_loaded:
            vision_tower.load_model()
        image_processor = vision_tower.image_processor
        image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
        new_images = []
        if image_aspect_ratio == "pad":
            for image in images:
                image = self.expand2square(
                    image, tuple(int(x * 255) for x in image_processor.image_mean)
                )
                image = image_processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
                new_images.append(image)
        else:
            return image_processor(images, return_tensors="pt")["pixel_values"]
        if all(x.shape == new_images[0].shape for x in new_images):
            new_images = torch.stack(new_images, dim=0)
        return new_images


AutoConfig.register("vita-Qwen2", VITAQwen2Config)
AutoModelForCausalLM.register(VITAQwen2Config, VITAQwen2ForCausalLM)



