import concurrent.futures 
import random
import gradio as gr
import requests
import io, base64, json
import spaces
from PIL import Image
from .models import IMAGE_GENERATION_MODELS, IMAGE_EDITION_MODELS, load_pipeline

class ModelManager:
    def __init__(self):
        self.model_ig_list = IMAGE_GENERATION_MODELS
        self.model_ie_list = IMAGE_EDITION_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=60)
    def generate_image_ig(self, prompt, model_name):
        pipe = self.load_model_pipe(model_name)
        result = pipe(prompt=prompt)
        return result

    def generate_image_ig_parallel_anony(self, prompt, model_A, model_B):
        if model_A == "" and model_B == "":
            model_names = random.sample([model for model in self.model_ig_list], 2)
        else:
            model_names = [model_A, model_B]

        results = []
        with concurrent.futures.ThreadPoolExecutor() as executor:
            future_to_result = {executor.submit(self.generate_image_ig, prompt, model): model for model in model_names}
            for future in concurrent.futures.as_completed(future_to_result):
                result = future.result()
                results.append(result)
        return results[0], results[1], model_names[0], model_names[1]

    def generate_image_ig_parallel(self, prompt, model_A, model_B):
        results = []
        model_names = [model_A, model_B]
        with concurrent.futures.ThreadPoolExecutor() as executor:
            future_to_result = {executor.submit(self.generate_image_ig, prompt, model): model for model in model_names}
            for future in concurrent.futures.as_completed(future_to_result):
                result = future.result()
                results.append(result)
        return results[0], results[1]

    @spaces.GPU(duration=150)
    def generate_image_ie(self, textbox_source, textbox_target, textbox_instruct, source_image, model_name):
        pipe = self.load_model_pipe(model_name)
        if 'PNP' in model_name:
            result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct, num_inversion_steps=100)
        elif 'Prompt2prompt' in model_name:
            result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct, num_inner_steps=5)
        else:
            result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct)
        return result

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

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