
"""simple docstring"""




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


snake_case					  =						get_logger(__name__)

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=None,	SCREAMING_SNAKE_CASE_=None,	**SCREAMING_SNAKE_CASE_,	):
					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:
										SCREAMING_SNAKE_CASE           =      'cpu'
					elif map_location == "on_device":
										SCREAMING_SNAKE_CASE           =      PartialState().device
					# Model states
					for i, model in enumerate(SCREAMING_SNAKE_CASE_     ):
										SCREAMING_SNAKE_CASE           =      f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
										models[i].load_state_dict(torch.load(SCREAMING_SNAKE_CASE_,	map_location=SCREAMING_SNAKE_CASE_     ),	**SCREAMING_SNAKE_CASE_     )
					logger.info('All model weights loaded successfully'     )

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

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

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

					# Random states
					try:
										SCREAMING_SNAKE_CASE           =      torch.load(os.path.join(SCREAMING_SNAKE_CASE_,	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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      Path(SCREAMING_SNAKE_CASE_     ) / f'''custom_checkpoint_{index}.pkl'''
					logger.info(f'''Saving the state of {get_pretty_name(SCREAMING_SNAKE_CASE_     )} to {save_location}'''     )
					torch.save(obj.state_dict(),	SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      f'''{path}/custom_checkpoint_{index}.pkl'''
					logger.info(f'''Loading the state of {get_pretty_name(SCREAMING_SNAKE_CASE_     )} from {load_location}'''     )
					obj.load_state_dict(torch.load(SCREAMING_SNAKE_CASE_,	map_location='cpu'     )     )


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
