from collections import namedtuple
from copy import copy
from itertools import permutations, chain
import random
import csv
from io import StringIO
from PIL import Image
import numpy as np

import modules.scripts as scripts
import gradio as gr

from modules import images, hypernetwork
from modules.processing import process_images, Processed, get_correct_sampler
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)


def apply_order(p, x, xs):
    token_order = []

    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
    for token in x:
        token_order.append((p.prompt.find(token), token))

    token_order.sort(key=lambda t: t[0])

    prompt_parts = []

    # Split the prompt up, taking out the tokens
    for _, token in token_order:
        n = p.prompt.find(token)
        prompt_parts.append(p.prompt[0:n])
        p.prompt = p.prompt[n + len(token):]

    # Rebuild the prompt with the tokens in the order we want
    prompt_tmp = ""
    for idx, part in enumerate(prompt_parts):
        prompt_tmp += part
        prompt_tmp += x[idx]
    p.prompt = prompt_tmp + p.prompt
    

def build_samplers_dict(p):
    samplers_dict = {}
    for i, sampler in enumerate(get_correct_sampler(p)):
        samplers_dict[sampler.name.lower()] = i
        for alias in sampler.aliases:
            samplers_dict[alias.lower()] = i
    return samplers_dict


def apply_sampler(p, x, xs):
    sampler_index = build_samplers_dict(p).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 apply_hypernetwork(p, x, xs):
    hypernetwork.load_hypernetwork(x)


def apply_clip_skip(p, x, xs):
    opts.data["CLIP_stop_at_last_layers"] = x


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 format_value_join_list(p, opt, x):
    return ", ".join(x)


def do_nothing(p, x, xs):
    pass


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


def str_permutations(x):
    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
    return x


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("Prompt order", str_permutations, apply_order, format_value_join_list),
    AxisOption("Sampler", str, apply_sampler, format_value),
    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
    AxisOption("Hypernetwork", str, apply_hypernetwork, 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),
    AxisOption("Clip skip", int, apply_clip_skip, 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_processed = 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_processed is None:
                first_processed = 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_processed.images = [grid]

    return first_processed


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):
        if not no_fixed_seeds:
            modules.processing.fix_seed(p)

        p.batch_size = 1
        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers

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

            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]

            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
            elif opt.type == str_permutations:
                valslist = list(permutations(valslist))

            valslist = [opt.type(x) for x in valslist]
            
            # Confirm options are valid before starting
            if opt.label == "Sampler":
                samplers_dict = build_samplers_dict(p)
                for sampler_val in valslist:
                    if sampler_val.lower() not in samplers_dict.keys():
                        raise RuntimeError(f"Unknown sampler: {sampler_val}")
            elif opt.label == "Checkpoint name":
                for ckpt_val in valslist:
                    if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
                        raise RuntimeError(f"Checkpoint for {ckpt_val} not found")

            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 == '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)

        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)

        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)

        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers

        return processed