import concurrent.futures 
import random
import gradio as gr
import requests, os
import io, base64, json
import spaces
import torch
from PIL import Image
from openai import OpenAI
from .models import IMAGE_GENERATION_MODELS, IMAGE_EDITION_MODELS, VIDEO_GENERATION_MODELS, load_pipeline
from .fetch_museum_results import draw_from_imagen_museum, draw2_from_imagen_museum
from serve.upload import get_random_mscoco_prompt, get_random_video_prompt, get_ssh_random_video_prompt
from serve.constants import SSH_CACHE_OPENSOURCE, SSH_CACHE_ADVANCE, SSH_CACHE_PIKA, SSH_CACHE_SORA, SSH_CACHE_IMAGE


class ModelManager:
    def __init__(self):
        self.model_ig_list = IMAGE_GENERATION_MODELS
        self.model_ie_list = IMAGE_EDITION_MODELS
        self.model_vg_list = VIDEO_GENERATION_MODELS
        self.loaded_models = {}

    def load_model_pipe(self, model_name):
        if not model_name in self.loaded_models:
            pipe = load_pipeline(model_name)
            self.loaded_models[model_name] = pipe
        else:
            pipe = self.loaded_models[model_name]
        return pipe
    
    @spaces.GPU(duration=120)
    def generate_image_ig(self, prompt, model_name):
        pipe = self.load_model_pipe(model_name)
        if 'Stable-cascade' not in model_name:
            result = pipe(prompt=prompt).images[0]
        else:
            prior, decoder = pipe
            prior.enable_model_cpu_offload()
            prior_output = prior(
                prompt=prompt,
                height=512,
                width=512,
                negative_prompt='',
                guidance_scale=4.0,
                num_images_per_prompt=1,
                num_inference_steps=20
            )
            decoder.enable_model_cpu_offload()
            result = decoder(
                image_embeddings=prior_output.image_embeddings.to(torch.float16),
                prompt=prompt,
                negative_prompt='',
                guidance_scale=0.0,
                output_type="pil",
                num_inference_steps=10
            ).images[0]
        return result

    def generate_image_ig_api(self, prompt, model_name):
        pipe = self.load_model_pipe(model_name)
        result = pipe(prompt=prompt)
        
        return result

    def generate_image_ig_museum(self, model_name):
        model_name = model_name.split('_')[1]
        result_list = draw_from_imagen_museum("t2i", model_name)
        image_link = result_list[0]
        prompt = result_list[1]

        return image_link, prompt


    def generate_image_ig_parallel_anony(self, prompt, model_A, model_B, model_C, model_D):
        if model_A == "" and model_B == "" and model_C == "" and model_D == "":
            # not_run = [11, 12, 13, 14, 15, 16, 17, 18, 19]
            # filtered_models = [model for i, model in enumerate(self.model_ig_list) if i not in not_run]
            # model_names = random.sample([model for model in filtered_models], 4)

            # model_names = random.sample([model for model in self.model_ig_list], 4)
            
            from .matchmaker import matchmaker
            model_ids = matchmaker(num_players=len(self.model_ig_list))
            print(model_ids)
            model_names = [self.model_ig_list[i] for i in model_ids]
            print(model_names)
        else:
            model_names = [model_A, model_B, model_C, model_D]


        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("huggingface")
                    else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
            results = [future.result() for future in futures]


        return results[0], results[1], results[2], results[3], \
            model_names[0], model_names[1], model_names[2], model_names[3]

    def generate_video_vg_parallel_anony(self, model_A, model_B, model_C, model_D):
        if model_A == "" and model_B == "" and model_C == "" and model_D == "":
            # model_names = random.sample([model for model in self.model_vg_list], 4)

            from .matchmaker_video import matchmaker_video
            model_ids = matchmaker_video(num_players=len(self.model_vg_list))
            print(model_ids)
            model_names = [self.model_vg_list[i] for i in model_ids]
            print(model_names)
        else:
            model_names = [model_A, model_B, model_C, model_D]
        
        root_dir = SSH_CACHE_OPENSOURCE
        for name in model_names:
            if "Runway-Gen3" in name or "Runway-Gen2" in name or "Pika-v1.0" in name:
                root_dir = SSH_CACHE_ADVANCE
            elif "Pika-beta" in name:
                root_dir = SSH_CACHE_PIKA      
            elif "Sora" in name and "OpenSora" not in name: 
                root_dir = SSH_CACHE_SORA  
        
        local_dir = "./cache_video"
        prompt, results = get_ssh_random_video_prompt(root_dir, local_dir, model_names)
        cache_dir = local_dir
        # cache_dir, prompt = get_random_video_prompt(root_dir)
        # results = []
        # for name in model_names:
        #     model_source, model_name, model_type = name.split("_")
        #     # if model_name in ["Runway-Gen3", "Pika-beta", "Pika-v1.0"]:
        #     #     file_name = cache_dir.split("/")[-1]
        #     #     video_path = os.path.join(cache_dir, f'{file_name}.mp4')
        #     # else:
        #     #     video_path = os.path.join(cache_dir, f'{model_name}.mp4')
        #     video_path = os.path.join(cache_dir, f'{model_name}.mp4')
        #     print(video_path)
        #     results.append(video_path)

        # with concurrent.futures.ThreadPoolExecutor() as executor:
        #     futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("huggingface")
        #                else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
        #     results = [future.result() for future in futures]

        return results[0], results[1], results[2], results[3], \
            model_names[0], model_names[1], model_names[2], model_names[3], prompt, cache_dir

    def generate_image_ig_museum_parallel_anony(self, model_A, model_B, model_C, model_D):
        if model_A == "" and model_B == "" and model_C == "" and model_D == "":
            # model_names = random.sample([model for model in self.model_ig_list], 4)

            from .matchmaker import matchmaker
            model_ids = matchmaker(num_players=len(self.model_ig_list))
            print(model_ids)
            model_names = [self.model_ig_list[i] for i in model_ids]
            print(model_names)
        else:
            model_names = [model_A, model_B, model_C, model_D]

        prompt = get_random_mscoco_prompt()
        print(prompt)

        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("huggingface")
                       else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
            results = [future.result() for future in futures]

        return results[0], results[1], results[2], results[3], \
            model_names[0], model_names[1], model_names[2], model_names[3], prompt

    
    def generate_image_ig_parallel(self, prompt, model_A, model_B):
        model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("imagenhub")
                       else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
            results = [future.result() for future in futures]
        return results[0], results[1]

    def generate_image_ig_museum_parallel(self, model_A, model_B):
        with concurrent.futures.ThreadPoolExecutor() as executor:
            model_1 = model_A.split('_')[1]
            model_2 = model_B.split('_')[1]
            result_list = draw2_from_imagen_museum("t2i", model_1, model_2)
            image_links = result_list[0]
            prompt_list = result_list[1]
        return image_links[0], image_links[1], prompt_list[0]


    @spaces.GPU(duration=200)
    def generate_image_ie(self, textbox_source, textbox_target, textbox_instruct, source_image, model_name):
        pipe = self.load_model_pipe(model_name)
        result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct)
        return result

    def generate_image_ie_museum(self, model_name):
        model_name = model_name.split('_')[1]
        result_list = draw_from_imagen_museum("tie", model_name)
        image_links = result_list[0]
        prompt_list = result_list[1]
        # image_links = [src, model]
        # prompt_list = [source_caption, target_caption, instruction]
        return image_links[0], image_links[1], prompt_list[0], prompt_list[1], prompt_list[2]

    def generate_image_ie_parallel(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
        model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [
                executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image,
                                model) for model in model_names]
            results = [future.result() for future in futures]
        return results[0], results[1]

    def generate_image_ie_museum_parallel(self, model_A, model_B):
        model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            model_1 = model_names[0].split('_')[1]
            model_2 = model_names[1].split('_')[1]
            result_list = draw2_from_imagen_museum("tie", model_1, model_2)
            image_links = result_list[0]
            prompt_list = result_list[1]
            # image_links = [src, model_A, model_B]
            # prompt_list = [source_caption, target_caption, instruction]
        return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2]

    def generate_image_ie_parallel_anony(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
        if model_A == "" and model_B == "":
            model_names = random.sample([model for model in self.model_ie_list], 2)
        else:
            model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = [executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image, model) for model in model_names]
            results = [future.result() for future in futures]
        return results[0], results[1], model_names[0], model_names[1]

    def generate_image_ie_museum_parallel_anony(self, model_A, model_B):
        if model_A == "" and model_B == "":
            model_names = random.sample([model for model in self.model_ie_list], 2)
        else:
            model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            model_1 = model_names[0].split('_')[1]
            model_2 = model_names[1].split('_')[1]
            result_list = draw2_from_imagen_museum("tie", model_1, model_2)
            image_links = result_list[0]
            prompt_list = result_list[1]
            # image_links = [src, model_A, model_B]
            # prompt_list = [source_caption, target_caption, instruction]
        return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2], model_names[0], model_names[1]


        raise NotImplementedError