# Copyright (c) ModelScope Contributors. All rights reserved.
import os
import random
import torch
from PIL import Image
from typing import Any, Dict, List

from swift.utils import get_device
from ..base import Template
from ..constant import LLMTemplateType, MLLMTemplateType
from ..register import register_template
from ..template_inputs import StdTemplateInputs
from ..template_meta import TemplateMeta
from ..utils import findall
from .utils import DEFAULT_SYSTEM, EmptyTemplateMeta


class Emu3GenTemplate(Template):

    NULL_PROMPT_PROB = 0.1
    COOKBOOK_SIZE = 32768
    CFG_SCALE = os.environ.get('CFG_SCALE', 3.0)
    GENERATION_RATIO = os.environ.get('GENERATION_RATIO', '1:1')
    NEGATIVE_PROMPT = os.environ.get(
        'NEGATIVE_PROMPT',
        'lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, '
        'worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry.')

    def init_processor(self, processor) -> None:
        if processor is None:
            return
        super().init_processor(processor)
        self.bov = self.processor.tokenizer.encode(self.processor.visual_template[0].format(token_id=0))[0]
        self.eov = self.processor.tokenizer.encode(self.processor.visual_template[0].format(token_id=self.COOKBOOK_SIZE
                                                                                            - 1))[0]
        self.h, self.w = self.processor.calculate_generate_size(self.GENERATION_RATIO, self.processor.image_area,
                                                                self.processor.vision_tokenizer.spatial_scale_factor)
        self.skip_prompt = False
        self.apply_loss_on_only_vision = True

    def _encode(self, inputs: StdTemplateInputs) -> Dict[str, Any]:
        if self.is_training:
            p_prob = random.random()
            if p_prob < self.NULL_PROMPT_PROB:
                prompt = ''
            else:
                prompt = inputs.to_history()['response']
            image = self.smart_resize(inputs.images[0].convert('RGB'))
            with torch.no_grad():
                image = self.processor.image_processor(
                    image, return_tensors='pt')['pixel_values'].to(device=self.processor.vision_tokenizer.device)
                image_token_ids = self.processor.vision_tokenizer.encode(image).squeeze(0)
            encoded = self._process_prompt_train(prompt, image_token_ids)
        else:
            prompt = inputs.to_history()['query']
            encoded = self._process_prompt_test(prompt)
            encoded = {key: encoded[key][0] for key in encoded.keys()}  # [1, L] -> [L]

        return encoded

    def _process_prompt_train(self, raw_prompt, image_token_ids):
        image_prompt = self.format_image_prompt(image_token_ids)
        prompt = self.tokenizer.bos_token + raw_prompt + image_prompt
        sample = self.tokenizer(prompt, padding='max_length', return_token_type_ids=False)
        labels = torch.tensor(sample['input_ids'])
        if self.apply_loss_on_only_vision:
            labels = torch.where(torch.logical_and(labels >= self.bov, labels <= self.eov), labels, -100)
        sample['labels'] = labels.tolist()
        return sample

    def _process_prompt_test(self, raw_prompt):
        # for supporting multi inputs, use list instead of single string
        if isinstance(raw_prompt, str):
            raw_prompt = [raw_prompt]
        prompt_list = []
        size_list = []
        for text_prompt in raw_prompt:
            prompt = self.processor.tokenizer.bos_token
            image_prompt = (
                self.processor.tokenizer.boi_token + self.processor.prefix_template.format(H=self.h, W=self.w)
                + self.processor.tokenizer.img_token)
            prompt += (text_prompt + image_prompt)
            prompt_list.append(prompt)
            size_list.append([self.h, self.w])
        prompt_list = self.tokenizer(prompt_list, padding='longest', return_token_type_ids=False)
        return prompt_list

    def prepare_for_output(self, output: str) -> str:
        return output

    def prepare_generate_kwargs(self, generate_kwargs: Dict[str, Any], *, model=None) -> Dict[str, Any]:
        from transformers import (LogitsProcessorList, PrefixConstrainedLogitsProcessor,
                                  UnbatchedClassifierFreeGuidanceLogitsProcessor)

        negative_prompt = self.NEGATIVE_PROMPT
        neg_inputs = self._process_prompt_test(negative_prompt)
        neg_inputs = {key: torch.tensor(val) for key, val in neg_inputs.items()}
        batch_size = generate_kwargs['input_ids'].shape[0]
        h = torch.tensor([self.h] * batch_size)
        w = torch.tensor([self.w] * batch_size)

        constrained_fn = self.processor.build_prefix_constrained_fn(h, w)
        logits_processor = LogitsProcessorList([
            UnbatchedClassifierFreeGuidanceLogitsProcessor(
                self.CFG_SCALE,
                model,
                unconditional_ids=neg_inputs['input_ids'].to(get_device()),
            ),
            PrefixConstrainedLogitsProcessor(
                constrained_fn,
                num_beams=1,
            ),
        ])
        res = super().prepare_generate_kwargs(generate_kwargs, model=model)
        res['logits_processor'] = logits_processor
        return res

    def decode_generate_ids(self, generate_ids: List[int], **kwargs) -> Any:
        mm_list = self.processor.decode(generate_ids)
        for im in mm_list:
            if not isinstance(im, Image.Image):
                continue
            return [{'type': 'image', 'image': im}]

    def to_imgstr(self, image_tokens):
        image_token_str = [[self.processor.visual_template[0].format(token_id=token_id) for token_id in token_row]
                           for token_row in image_tokens]
        image_row_str = [''.join(token_row) for token_row in image_token_str]
        imgstr = self.tokenizer.eol_token.join(image_row_str)
        return imgstr

    def format_image_prompt(self, image_tokens):
        h, w = image_tokens.shape
        imgstr = self.to_imgstr(image_tokens)
        image_prompt = (
            self.tokenizer.boi_token + f'{h}*{w}' + self.tokenizer.img_token + imgstr + self.tokenizer.eol_token
            + self.tokenizer.eof_token + self.tokenizer.eoi_token)
        return image_prompt

    def smart_resize(self, image):
        w, h = image.size
        current_area = h * w
        target_ratio = (self.processor.image_area / current_area)**0.5
        th = int(round(h * target_ratio))
        tw = int(round(w * target_ratio))
        image = image.resize((tw, th))
        return image


register_template(EmptyTemplateMeta(
    MLLMTemplateType.emu3_gen,
    template_cls=Emu3GenTemplate,
))


class Emu3ChatTemplate(Template):
    system = 'You are a helpful assistant.'
    image_placeholder = ['<|image token|>']

    def _encode(self, inputs: StdTemplateInputs) -> Dict[str, Any]:
        encoded = super()._encode(inputs)
        # image
        images = inputs.images
        input_ids = encoded['input_ids']
        labels = encoded['labels']
        loss_scale = encoded.get('loss_scale', None)
        image_tokens = self.processor.tokenize_image(images)
        image_prompts = []
        idx_list = findall(input_ids, self.tokenizer.encode(self.image_placeholder))
        # Create image prompts
        for i in range(len(images)):
            h, w = image_tokens[i].shape
            imgstr = self.processor.to_imgstr(image_tokens[i])
            image_prompt = (
                self.tokenizer.boi_token + self.processor.prefix_template.format(H=h, W=w) + self.tokenizer.img_token
                + imgstr + self.tokenizer.eol_token + self.tokenizer.eof_token + self.tokenizer.eoi_token)
            image_prompts.append(self.tokenizer.encode(image_prompt))

        # Insert image tokens into input_ids
        input_ids, labels, loss_scale = self._extend_tokens(input_ids, labels, loss_scale, idx_list,
                                                            lambda i: image_prompts[i])
        return {'input_ids': input_ids, 'labels': labels, 'loss_scale': loss_scale}


register_template(
    TemplateMeta(
        MLLMTemplateType.emu3_chat,
        prefix=[['bos_token_id'], '{{SYSTEM}}'],
        prompt=[' User: {{QUERY}}. Assistant:'],
        chat_sep=[['eos_token_id']],
        suffix=[['eos_token_id']],
        default_system=DEFAULT_SYSTEM,
        template_cls=Emu3ChatTemplate))

register_template(
    TemplateMeta(
        LLMTemplateType.bge_reranker,
        prefix=['<s> '],
        chat_sep=[],
        prompt=['{{QUERY}}</s></s> '],
        suffix=['</s>'],
    ))
