# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import dataclasses
from typing import Literal
from accelerate import Accelerator
from transformers import HfArgumentParser
from PIL import Image
import json
import openai

from uno.flux.pipeline import UNOPipeline, preprocess_ref
from uno.utils.prompt_enhancer import enhance_prompt_with_chatgpt

openai.api_key = os.getenv("OPENAI_API_KEY")

def horizontal_concat(images):
    widths, heights = zip(*(img.size for img in images))
    total_width = sum(widths)
    max_height = max(heights)
    new_im = Image.new('RGB', (total_width, max_height))
    x_offset = 0
    for img in images:
        new_im.paste(img, (x_offset, 0))
        x_offset += img.size[0]
    return new_im

@dataclasses.dataclass
class InferenceArgs:
    prompt: str | None = None
    image_paths: list[str] | None = None
    eval_json_path: str | None = None
    offload: bool = False
    num_images_per_prompt: int = 1
    model_type: Literal["flux-dev", "flux-dev-fp8", "flux-schnell"] = "flux-dev"
    width: int = 512
    height: int = 512
    ref_size: int = -1
    num_steps: int = 25
    guidance: float = 4
    seed: int = 3407
    save_path: str = "output/inference"
    only_lora: bool = True
    concat_refs: bool = False
    lora_rank: int = 512
    data_resolution: int = 512
    pe: Literal['d', 'h', 'w', 'o'] = 'd'

def main(args: InferenceArgs):
    accelerator = Accelerator()
    pipeline = UNOPipeline(
        args.model_type,
        accelerator.device,
        args.offload,
        only_lora=args.only_lora,
        lora_rank=args.lora_rank
    )

    assert args.prompt is not None or args.eval_json_path is not None, \
        "Please provide either prompt or eval_json_path"

    if args.eval_json_path:
        with open(args.eval_json_path, "rt") as f:
            data_dicts = json.load(f)
        data_root = os.path.dirname(args.eval_json_path)
    else:
        data_root = "./"
        data_dicts = [{"prompt": args.prompt, "image_paths": args.image_paths}]

    for i, data_dict in enumerate(data_dicts):
        try:
            ref_imgs = [
                Image.open(os.path.join(data_root, img_path))
                for img_path in data_dict["image_paths"]
            ]
        except Exception as e:
            print(f"❌ [ERROR] Failed to load reference images: {e}")
            continue

        if args.ref_size == -1:
            args.ref_size = 512 if len(ref_imgs) == 1 else 320
        ref_imgs = [preprocess_ref(img, args.ref_size) for img in ref_imgs]

        print(f"\n🔧 [DEBUG] Enhancing prompt: '{data_dict['prompt']}'")
        enhanced_prompts = enhance_prompt_with_chatgpt(
            user_prompt=data_dict["prompt"],
            num_prompts=args.num_images_per_prompt,
            reference_images=ref_imgs
        )

        # Pad if needed
        while len(enhanced_prompts) < args.num_images_per_prompt:
            print(f"⚠️ [DEBUG] Padding prompts: returning user prompt as fallback.")
            enhanced_prompts.append(data_dict["prompt"])

        for j in range(args.num_images_per_prompt):
            if (i * args.num_images_per_prompt + j) % accelerator.num_processes != accelerator.process_index:
                continue

            prompt_j = enhanced_prompts[j]
            print(f"\n--- Generating image [{i}_{j}] ---")
            print(f"Enhanced Prompt: {prompt_j}")
            print(f"Image paths: {data_dict['image_paths']}")
            print(f"Seed: {args.seed + j}")
            print(f"Resolution: {args.width}x{args.height}")
            print("------------------------------")

            try:
                image_gen = pipeline(
                    prompt=prompt_j,
                    width=args.width,
                    height=args.height,
                    guidance=args.guidance,
                    num_steps=args.num_steps,
                    seed=args.seed + j,
                    ref_imgs=ref_imgs,
                    pe=args.pe,
                )

                if args.concat_refs:
                    image_gen = horizontal_concat([image_gen, *ref_imgs])

                os.makedirs(args.save_path, exist_ok=True)
                image_gen.save(os.path.join(args.save_path, f"{i}_{j}.png"))

                # Save generation context
                args_dict = vars(args)
                args_dict['prompt'] = prompt_j
                args_dict['image_paths'] = data_dict["image_paths"]
                with open(os.path.join(args.save_path, f"{i}_{j}.json"), 'w') as f:
                    json.dump(args_dict, f, indent=4)

            except Exception as e:
                print(f"❌ [ERROR] Failed to generate or save image {i}_{j}: {e}")

if __name__ == "__main__":
    parser = HfArgumentParser([InferenceArgs])
    args = parser.parse_args_into_dataclasses()[0]
    main(args)