




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   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__ = None,    )						->   List[str]:
							# Model states
							for i, state in enumerate(snake_case__							):
														__UpperCAmelCase						:			Optional[int]         =   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Dict         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Model weights saved in {output_model_file}'''							)
							# Optimizer states
							for i, opt in enumerate(snake_case__							):
														__UpperCAmelCase						:			Any         =   opt.state_dict()
														__UpperCAmelCase						:			Union[str, Any]         =   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Optional[int]         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Optimizer state saved in {output_optimizer_file}'''							)
							# Scheduler states
							for i, scheduler in enumerate(snake_case__							):
														__UpperCAmelCase						:			List[Any]         =   scheduler.state_dict()
														__UpperCAmelCase						:			Any         =   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Optional[Any]         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Scheduler state saved in {output_scheduler_file}'''							)
							# GradScaler state
							if scaler is not None:
														__UpperCAmelCase						:			List[Any]         =   scaler.state_dict()
														__UpperCAmelCase						:			int         =   os.path.join(snake_case__,    snake_case__							)
														torch.save(snake_case__,    snake_case__							)
														logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''							)
							# Random number generator states
							__UpperCAmelCase						:			Optional[int]         =   {}
							__UpperCAmelCase						:			int         =   f'''{RNG_STATE_NAME}_{process_index}.pkl'''
							__UpperCAmelCase						:			int         =   random.getstate()
							__UpperCAmelCase						:			Tuple         =   np.random.get_state()
							__UpperCAmelCase						:			Any         =   torch.get_rng_state()
							if is_xpu_available():
														__UpperCAmelCase						:			Union[str, Any]         =   torch.xpu.get_rng_state_all()
							else:
														__UpperCAmelCase						:			List[str]         =   torch.cuda.get_rng_state_all()
							if is_tpu_available():
														__UpperCAmelCase						:			Tuple         =   xm.get_rng_state()
							__UpperCAmelCase						:			List[Any]         =   os.path.join(snake_case__,    snake_case__							)
							torch.save(snake_case__,    snake_case__							)
							logger.info(f'''Random states saved in {output_states_file}'''							)
							return output_dir




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

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

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

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

							# Random states
							try:
														__UpperCAmelCase						:			List[Any]         =   torch.load(os.path.join(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   (			snake_case__,    snake_case__,    snake_case__ = 0							)						->   Any:
							__UpperCAmelCase						:			Any         =   Path(snake_case__							) / f'''custom_checkpoint_{index}.pkl'''
							logger.info(f'''Saving the state of {get_pretty_name(snake_case__							)} to {save_location}'''							)
							torch.save(obj.state_dict(),    snake_case__							)







def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__ = 0							)						->   Union[str, Any]:
							__UpperCAmelCase						:			Union[str, Any]         =   f'''{path}/custom_checkpoint_{index}.pkl'''
							logger.info(f'''Loading the state of {get_pretty_name(snake_case__							)} from {load_location}'''							)
							obj.load_state_dict(torch.load(snake_case__,    map_location="cpu"							)							)





def   _UpperCamelCase   (			snake_case__							)						->   list:
							__UpperCAmelCase						:			Optional[int]         =   1.3
							__UpperCAmelCase						:			Any         =   len(snake_case__							)
							__UpperCAmelCase						:			Optional[Any]         =   False

							while not completed:
														# Update the gap value for a next comb
														__UpperCAmelCase						:			List[str]         =   int(gap / shrink_factor							)
														if gap <= 1:
																					__UpperCAmelCase						:			Dict         =   True

														__UpperCAmelCase						:			List[Any]         =   0
														while index + gap < len(snake_case__							):
																					if data[index] > data[index + gap]:
																												# Swap values
																												__UpperCAmelCase    ,	__UpperCAmelCase						:			Tuple         =   data[index + gap], data[index]
																												__UpperCAmelCase						:			List[str]         =   False
																					index += 1

							return data


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_snake_case			=						input('''Enter numbers separated by a comma:\n''').strip()
	_snake_case			=						[int(item) for item in user_input.split(''',''')]
	print(comb_sort(unsorted))




