"""Undistilled FLUX9B with real CFG, negative prompts and reference images.

CFG batching/parallelism change execution layout, not the guidance equation.
The denoising call is derived from the pinned installed official pipeline;
only its two transformer calls are replaced. No approximation cache is used.
"""
import copy
import inspect
import json
import textwrap
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import torch
from diffusers import Flux2KleinPipeline
import diffusers.pipelines.flux2.pipeline_flux2_klein as upstream
from vivarium_prompt_templates import build_prompt

ROOT = Path(__file__).resolve().parents[1]

def _forward(model, kwargs, device):
    with torch.inference_mode(), torch.autocast(device.type, dtype=torch.bfloat16):
        return model(**kwargs)[0]

def _cfg_prediction(self, hidden, timestep, positive, negative, txt, neg_txt, img, image_tokens):
    if getattr(self,'_native_mark_graph_steps',False):
        torch.compiler.cudagraph_mark_step_begin()
    common = dict(hidden_states=hidden, timestep=timestep/1000, guidance=None, img_ids=img,
                  joint_attention_kwargs=self.attention_kwargs, return_dict=False)
    pos = dict(common, encoder_hidden_states=positive, txt_ids=txt)
    if not self.do_classifier_free_guidance:
        return self.transformer(**pos)[0][:,:image_tokens]
    neg = dict(common, encoder_hidden_states=negative, txt_ids=neg_txt)
    mode = self._native_cfg_mode
    if mode == 'batched':
        merged = dict(common, hidden_states=torch.cat([hidden,hidden],dim=0),
                      timestep=torch.cat([timestep,timestep],dim=0)/1000,
                      encoder_hidden_states=torch.cat([positive,negative],dim=0),
                      txt_ids=torch.cat([txt,neg_txt],dim=0), img_ids=torch.cat([img,img],dim=0))
        pred = self.transformer(**merged)[0][:,:image_tokens]
        positive_noise, negative_noise = pred.chunk(2,dim=0)
    elif mode == 'parallel':
        device=self._native_second_device
        neg={k:(v.to(device) if isinstance(v,torch.Tensor) else v) for k,v in neg.items()}
        future=self._native_executor.submit(_forward,self._native_negative_transformer,neg,device)
        positive_noise=self.transformer(**pos)[0][:,:image_tokens]
        negative_noise=future.result()[:,:image_tokens].to(hidden.device)
    else:
        with self.transformer.cache_context('cond'): positive_noise=self.transformer(**pos)[0][:,:image_tokens]
        with self.transformer.cache_context('uncond'): negative_noise=self.transformer(**neg)[0][:,:image_tokens]
    return negative_noise+self.guidance_scale*(positive_noise-negative_noise)

def _native_call():
    source=textwrap.dedent(inspect.getsource(inspect.unwrap(Flux2KleinPipeline.__call__)))
    source=source[source.index('def __call__('):]
    start=source.index('            with self.transformer.cache_context("cond"):')
    end=source.index('            # compute the previous noisy sample',start)
    old=source[start:end]
    assert old.count('self.transformer(')==2 and 'neg_noise_pred + guidance_scale' in old
    new='''            noise_pred = self._native_cfg_prediction(
                latent_model_input, timestep, prompt_embeds,
                negative_prompt_embeds if self.do_classifier_free_guidance else None,
                text_ids, negative_text_ids if self.do_classifier_free_guidance else None,
                latent_image_ids, latents.size(1))

'''
    namespace=dict(vars(upstream))
    exec(compile(source[:start]+new+source[end:],'<pinned-flux-native-cfg>','exec'),namespace)
    return torch.no_grad()(namespace['__call__'])

class NativeFluxPipeline(Flux2KleinPipeline):
    __call__=_native_call()
    _native_cfg_prediction=_cfg_prediction

def create_native_pipeline(kind='characters', device='cuda:0', fuse=True, qkv=True,
                           compile_mode='default', cfg_mode='batched', second_device='cuda:1',base_path=None):
    assert kind in ('characters','backgrounds') and cfg_mode in ('sequential','batched','parallel')
    torch.set_num_threads(8)
    torch.backends.cuda.matmul.allow_tf32=True
    registry=ROOT/'model-downloads.json'
    model=json.loads(registry.read_text())['FLUX.2-klein-base-9B'] if registry.is_file() else dict(repo='black-forest-labs/FLUX.2-klein-base-9B',revision='32773329fbe7e81a90ef971740e8ba4b0364ecf3',path='/nonexistent-vivarium-base-cache')
    path=Path(base_path) if base_path else Path(model['path'])
    if base_path is None and not path.is_absolute(): path=ROOT/path
    kwargs=dict(torch_dtype=torch.bfloat16)
    if path.is_dir(): kwargs['local_files_only']=True
    else: kwargs['revision']=model['revision']
    pipe=NativeFluxPipeline.from_pretrained(str(path) if path.is_dir() else model['repo'],**kwargs).to(device)
    assert not pipe.config.is_distilled, 'Only undistilled base9B is permitted.'
    pipe.set_progress_bar_config(disable=True)
    adapter=ROOT/'outputs/9B'/kind/'selected/pytorch_lora_weights.safetensors'
    if not adapter.is_file(): adapter=ROOT/'loras'/f'flux-{kind}.safetensors'
    if not adapter.is_file(): adapter=ROOT/kind/'pytorch_lora_weights.safetensors'
    if adapter.is_file():
        pipe.load_lora_weights(str(adapter.parent),weight_name=adapter.name,adapter_name='vivarium')
    else:
        pipe.load_lora_weights('laion/FLUX.2-klein-base-9B-vivarium-loras',revision='5bd654514cc35a1c0d0de878c3a514019896a44e',subfolder=kind,weight_name='pytorch_lora_weights.safetensors',adapter_name='vivarium')
    pipe.set_adapters('vivarium',adapter_weights=1.)
    if fuse:
        pipe.fuse_lora(adapter_names=['vivarium'],safe_fusing=True)
        pipe.unload_lora_weights()
    if qkv:
        assert fuse, 'Fuse adapter projections before fusing QKV.'
        pipe.transformer.fuse_qkv_projections()
    pipe.transformer.eval()
    pipe._native_cfg_mode=cfg_mode
    pipe._native_mark_graph_steps=compile_mode=='reduce-overhead'
    if pipe._native_mark_graph_steps: assert cfg_mode=='batched', 'CUDAGraph profile requires batched CFG.'
    pipe._native_kind=kind
    pipe._native_text_cache={}
    if cfg_mode=='parallel':
        second=torch.device(second_device)
        assert torch.device(device)!=second
        pipe._native_second_device=second
        pipe._native_negative_transformer=copy.deepcopy(pipe.transformer).to(second).eval()
        pipe._native_executor=ThreadPoolExecutor(max_workers=1)
    if compile_mode:
        # Repeated blocks carry distinct attention processors. Keep their guards
        # cached across both resolutions and successive category pipelines.
        torch._dynamo.config.cache_size_limit=1024
        for transformer in ([pipe.transformer,pipe._native_negative_transformer] if cfg_mode=='parallel' else [pipe.transformer]):
            transformer.compile_repeated_blocks(fullgraph=False,dynamic=compile_mode!='reduce-overhead',mode=compile_mode)
    return pipe

def encode_text(pipe,text):
    with torch.inference_mode(),torch.autocast(pipe._execution_device.type,dtype=torch.bfloat16):
        return pipe.encode_prompt(text,device=pipe._execution_device,max_sequence_length=256)[0]

def generate_native_image(pipe,prompt=None,seed=28008,steps=50,guidance=4.,negative_prompt='',image=None,
                          width=None,height=None,cache_negative=True,prompt_embeds=None,description=None):
    assert steps in (40,50), 'Production trials retain40 or50 denoising steps.'
    if prompt is not None and description is not None:
        raise ValueError('Use a full prompt or a templated description, not both.')
    if prompt is None and prompt_embeds is None: prompt=build_prompt(pipe._native_kind,description)
    kwargs=dict(width=width or (832 if pipe._native_kind=='characters' else 1376),
                height=height or (1248 if pipe._native_kind=='characters' else 768),
                num_inference_steps=steps,guidance_scale=guidance,max_sequence_length=256,
                generator=torch.Generator(pipe._execution_device).manual_seed(seed))
    if prompt_embeds is None: kwargs['prompt']=prompt
    else: kwargs['prompt_embeds']=prompt_embeds
    if image is not None: kwargs['image']=image
    with torch.inference_mode(),torch.autocast(pipe._execution_device.type,dtype=torch.bfloat16):
        if guidance>1 and (cache_negative or negative_prompt):
            if negative_prompt not in pipe._native_text_cache:
                pipe._native_text_cache[negative_prompt]=encode_text(pipe,negative_prompt)
            kwargs['negative_prompt_embeds']=pipe._native_text_cache[negative_prompt]
        return pipe(**kwargs).images

if __name__=='__main__':
    import argparse
    from PIL import Image
    p=argparse.ArgumentParser()
    p.add_argument('kind',choices=['characters','backgrounds'])
    text=p.add_mutually_exclusive_group()
    text.add_argument('--prompt',help='Complete prompt; overrides the warm-painted template.')
    text.add_argument('--description',help='Subject/location description; recommended warm-painted style is added.')
    p.add_argument('--negative-prompt',default='')
    p.add_argument('--reference',type=Path)
    p.add_argument('--steps',type=int,choices=[40,50],default=50)
    p.add_argument('--seed',type=int,default=28008)
    p.add_argument('--cfg-mode',choices=['sequential','batched','parallel'],default='batched')
    p.add_argument('--no-compile',action='store_true')
    p.add_argument('--base-path',type=Path)
    p.add_argument('--output',type=Path,default=ROOT/'native-preview.png')
    a=p.parse_args()
    pipe=create_native_pipeline(a.kind,compile_mode=None if a.no_compile else 'default',cfg_mode=a.cfg_mode,base_path=a.base_path)
    images=generate_native_image(pipe,a.prompt,a.seed,a.steps,negative_prompt=a.negative_prompt,
                                 image=Image.open(a.reference).convert('RGB') if a.reference else None,description=a.description)
    a.output.parent.mkdir(parents=True,exist_ok=True); images[0].save(a.output)
    print('Saved',a.output)