
import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__magic_name__           =					get_logger(__name__)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = None      ,						):
    # Model states
    for i, state in enumerate(__lowerCAmelCase							):
        snake_case__  =			F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        save(__lowerCAmelCase      ,						__lowerCAmelCase							)
        logger.info(F"""Model weights saved in {output_model_file}"""							)
    # Optimizer states
    for i, opt in enumerate(__lowerCAmelCase							):
        snake_case__  =			opt.state_dict()
        snake_case__  =			F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        save(__lowerCAmelCase      ,						__lowerCAmelCase							)
        logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
    # Scheduler states
    for i, scheduler in enumerate(__lowerCAmelCase							):
        snake_case__  =			scheduler.state_dict()
        snake_case__  =			F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        save(__lowerCAmelCase      ,						__lowerCAmelCase							)
        logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
    # GradScaler state
    if scaler is not None:
        snake_case__  =			scaler.state_dict()
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        torch.save(__lowerCAmelCase      ,						__lowerCAmelCase							)
        logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
    # Random number generator states
    snake_case__  =			{}
    snake_case__  =			F"""{RNG_STATE_NAME}_{process_index}.pkl"""
    snake_case__  =			random.getstate()
    snake_case__  =			np.random.get_state()
    snake_case__  =			torch.get_rng_state()
    if is_xpu_available():
        snake_case__  =			torch.xpu.get_rng_state_all()
    else:
        snake_case__  =			torch.cuda.get_rng_state_all()
    if is_tpu_available():
        snake_case__  =			xm.get_rng_state()
    snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
    torch.save(__lowerCAmelCase      ,						__lowerCAmelCase							)
    logger.info(F"""Random states saved in {output_states_file}"""							)
    return output_dir





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase=None      ,						__lowerCAmelCase=None      ,						**__lowerCAmelCase      ,						):
    if map_location not in [None, "cpu", "on_device"]:
        raise TypeError(
            "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"							)
    if map_location is None:
        snake_case__  =			"cpu"
    elif map_location == "on_device":
        snake_case__  =			PartialState().device
    # Model states
    for i, model in enumerate(__lowerCAmelCase							):
        snake_case__  =			F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        models[i].load_state_dict(torch.load(__lowerCAmelCase      ,						map_location=__lowerCAmelCase							)      ,						**__lowerCAmelCase							)
    logger.info("All model weights loaded successfully"							)

    # Optimizer states
    for i, opt in enumerate(__lowerCAmelCase							):
        snake_case__  =			F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        snake_case__  =			torch.load(__lowerCAmelCase      ,						map_location=__lowerCAmelCase							)
        optimizers[i].load_state_dict(__lowerCAmelCase							)
    logger.info("All optimizer states loaded successfully"							)

    # Scheduler states
    for i, scheduler in enumerate(__lowerCAmelCase							):
        snake_case__  =			F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        scheduler.load_state_dict(torch.load(__lowerCAmelCase							)							)
    logger.info("All scheduler states loaded successfully"							)

    # GradScaler state
    if scaler is not None:
        snake_case__  =			os.path.join(__lowerCAmelCase      ,						__lowerCAmelCase							)
        scaler.load_state_dict(torch.load(__lowerCAmelCase							)							)
        logger.info("GradScaler state loaded successfully"							)

    # Random states
    try:
        snake_case__  =			torch.load(os.path.join(__lowerCAmelCase      ,						F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
        random.setstate(states["random_state"]							)
        np.random.set_state(states["numpy_random_seed"]							)
        torch.set_rng_state(states["torch_manual_seed"]							)
        if is_xpu_available():
            torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]							)
        else:
            torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]							)
        if is_tpu_available():
            xm.set_rng_state(states["xm_seed"]							)
        logger.info("All random states loaded successfully"							)
    except Exception:
        logger.info("Could not load random states"							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = 0							):
    snake_case__  =			Path(__lowerCAmelCase							) / F"""custom_checkpoint_{index}.pkl"""
    logger.info(F"""Saving the state of {get_pretty_name(__lowerCAmelCase							)} to {save_location}"""							)
    torch.save(obj.state_dict()      ,						__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = 0							):
    snake_case__  =			F"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(F"""Loading the state of {get_pretty_name(__lowerCAmelCase							)} from {load_location}"""							)
    obj.load_state_dict(torch.load(__lowerCAmelCase      ,						map_location="cpu"							)							)





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			len(cells[0]							)  # 31
    snake_case__  =			[]
    for i in range(__lowerCAmelCase							):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        snake_case__  =			0 if i == 0 else cells[time][i - 1]
        snake_case__  =			0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        snake_case__  =			7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""      ,						2							)
        next_generation.append(rule[situation]							)
    return next_generation





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


if __name__ == "__main__":
     __magic_name__           =					bin(int(input('''Rule:\n''').strip()))[2:]
     __magic_name__           =					format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     __magic_name__           =					generate_image(CELLS)
     # Uncomment to save the image
     # img.save(f"rule_{rule_num}.png")
     img.show()



