from collections import namedtuple
from copy import copy
import random

from PIL import Image
import numpy as np

import modules.scripts as scripts
import gradio as gr

from modules import images
from modules.processing import process_images, Processed
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.sd_samplers
import modules.sd_models
import re


def apply_field(field):
    def fun(p, x, xs):
        setattr(p, field, x)

    return fun


def apply_prompt(p, x, xs):
    p.prompt = p.prompt.replace(xs[0], x)
    p.negative_prompt = p.negative_prompt.replace(xs[0], x)


samplers_dict = {}
for i, sampler in enumerate(modules.sd_samplers.samplers):
    samplers_dict[sampler.name.lower()] = i
    for alias in sampler.aliases:
        samplers_dict[alias.lower()] = i


def apply_sampler(p, x, xs):
    sampler_index = samplers_dict.get(x.lower(), None)
    if sampler_index is None:
        raise RuntimeError(f"Unknown sampler: {x}")

    p.sampler_index = sampler_index


def apply_checkpoint(p, x, xs):
    info = modules.sd_models.get_closet_checkpoint_match(x)
    assert info is not None, f'Checkpoint for {x} not found'
    modules.sd_models.reload_model_weights(shared.sd_model, info)


def format_value_add_label(p, opt, x):
    if type(x) == float:
        x = round(x, 8)

    return f"{opt.label}: {x}"


def format_value(p, opt, x):
    if type(x) == float:
        x = round(x, 8)

    return x

def do_nothing(p, x, xs):
    pass

def format_nothing(p, opt, x):
    return ""


AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])


axis_options = [
    AxisOption("Nothing", str, do_nothing, format_nothing),
    AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
    AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
    AxisOption("Prompt S/R", str, apply_prompt, format_value),
    AxisOption("Sampler", str, apply_sampler, format_value),
    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]


def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
    res = []

    ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]

    first_pocessed = None

    state.job_count = len(xs) * len(ys) * p.n_iter

    for iy, y in enumerate(ys):
        for ix, x in enumerate(xs):
            state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"

            processed = cell(x, y)
            if first_pocessed is None:
                first_pocessed = processed

            try:
              res.append(processed.images[0])
            except:
              res.append(Image.new(res[0].mode, res[0].size))

    grid = images.image_grid(res, rows=len(ys))
    if draw_legend:
        grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)

    first_pocessed.images = [grid]

    return first_pocessed


re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")

re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")

class Script(scripts.Script):
    def title(self):
        return "X/Y plot"

    def ui(self, is_img2img):
        current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]

        with gr.Row():
            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
            x_values = gr.Textbox(label="X values", visible=False, lines=1)

        with gr.Row():
            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
            y_values = gr.Textbox(label="Y values", visible=False, lines=1)
        
        draw_legend = gr.Checkbox(label='Draw legend', value=True)
        no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)

        return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]

    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
        modules.processing.fix_seed(p)
        p.batch_size = 1

        def process_axis(opt, vals):
            if opt.label == 'Nothing':
                return [0]

            valslist = [x.strip() for x in vals.split(",")]

            if opt.type == int:
                valslist_ext = []

                for val in valslist:
                    m = re_range.fullmatch(val)
                    mc = re_range_count.fullmatch(val)
                    if m is not None:

                        start = int(m.group(1))
                        end = int(m.group(2))+1
                        step = int(m.group(3)) if m.group(3) is not None else 1

                        valslist_ext += list(range(start, end, step))
                    elif mc is not None:
                        start = int(mc.group(1))
                        end   = int(mc.group(2))
                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
                        
                        valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
                    else:
                        valslist_ext.append(val)

                valslist = valslist_ext
            elif opt.type == float:
                valslist_ext = []

                for val in valslist:
                    m = re_range_float.fullmatch(val)
                    mc = re_range_count_float.fullmatch(val)
                    if m is not None:
                        start = float(m.group(1))
                        end = float(m.group(2))
                        step = float(m.group(3)) if m.group(3) is not None else 1

                        valslist_ext += np.arange(start, end + step, step).tolist()
                    elif mc is not None:
                        start = float(mc.group(1))
                        end   = float(mc.group(2))
                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
                        
                        valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
                    else:
                        valslist_ext.append(val)

                valslist = valslist_ext

            valslist = [opt.type(x) for x in valslist]

            return valslist

        x_opt = axis_options[x_type]
        xs = process_axis(x_opt, x_values)

        y_opt = axis_options[y_type]
        ys = process_axis(y_opt, y_values)

        def fix_axis_seeds(axis_opt, axis_list):
            if axis_opt.label in ["Seed","Var. seed"]:
                return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
            else:
                return axis_list

        if not no_fixed_seeds:
            xs = fix_axis_seeds(x_opt, xs)
            ys = fix_axis_seeds(y_opt, ys)

        if x_opt.label == 'Steps':
            total_steps = sum(xs) * len(ys)
        elif y_opt.label == 'Steps':
            total_steps = sum(ys) * len(xs)
        else:
            total_steps = p.steps * len(xs) * len(ys)

        print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
        shared.total_tqdm.updateTotal(total_steps * p.n_iter)

        def cell(x, y):
            pc = copy(p)
            x_opt.apply(pc, x, xs)
            y_opt.apply(pc, y, ys)

            return process_images(pc)

        if not x_opt.label == 'Nothing':
            p.extra_generation_params["XY Plot X Type"] = x_opt.label
            p.extra_generation_params["X Values"] = '{' + x_values + '}'
            if x_opt.label in ["Seed","Var. seed"] and not no_fixed_seeds:
                p.extra_generation_params["Fixed X Values"] = '{' + ", ".join([str(x) for x in xs])+ '}'

        if not y_opt.label == 'Nothing':
            p.extra_generation_params["XY Plot Y Type"] = y_opt.label
            p.extra_generation_params["Y Values"] = '{' + y_values + '}'
            if y_opt.label in ["Seed","Var. seed"] and not no_fixed_seeds:
                p.extra_generation_params["Fixed Y Values"] = '{' + ", ".join([str(y) for y in ys])+ '}'

        processed = draw_xy_grid(
            p,
            xs=xs,
            ys=ys,
            x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
            y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
            cell=cell,
            draw_legend=draw_legend
        )

        if opts.grid_save:
            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)

        # restore checkpoint in case it was changed by axes
        modules.sd_models.reload_model_weights(shared.sd_model)

        return processed