"""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


SCREAMING_SNAKE_CASE						=    get_logger(__name__)


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,):






	"""simple docstring"""





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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,):






	"""simple docstring"""





	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:
		_lowercase			:     int      					=				'cpu'
	elif map_location == "on_device":
		_lowercase			:     Optional[int]      					=				PartialState().device
	# Model states
	for i, model in enumerate(__UpperCAmelCase      ):
		_lowercase			:     Any      					=				F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
		_lowercase			:     Any      					=				os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )
		models[i].load_state_dict(torch.load(__UpperCAmelCase  ,map_location=__UpperCAmelCase      )  ,**__UpperCAmelCase      )
	logger.info('All model weights loaded successfully'      )

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

	# Scheduler states
	for i, scheduler in enumerate(__UpperCAmelCase      ):
		_lowercase			:     Any      					=				F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
		_lowercase			:     Union[str, Any]      					=				os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )
		scheduler.load_state_dict(torch.load(__UpperCAmelCase      )      )
	logger.info('All scheduler states loaded successfully'      )

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

	# Random states
	try:
		_lowercase			:     Tuple      					=				torch.load(os.path.join(__UpperCAmelCase  ,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   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = 0      ):






	"""simple docstring"""





	_lowercase			:     Optional[int]      					=				Path(__UpperCAmelCase      ) / F'''custom_checkpoint_{index}.pkl'''
	logger.info(F'''Saving the state of {get_pretty_name(__UpperCAmelCase      )} to {save_location}'''      )
	torch.save(obj.state_dict()  ,__UpperCAmelCase      )







def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = 0      ):






	"""simple docstring"""





	_lowercase			:     Any      					=				F'''{path}/custom_checkpoint_{index}.pkl'''
	logger.info(F'''Loading the state of {get_pretty_name(__UpperCAmelCase      )} from {load_location}'''      )
	obj.load_state_dict(torch.load(__UpperCAmelCase  ,map_location='cpu'      )      )




"""simple docstring"""







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	if len(__UpperCAmelCase      ) > 1:
		_lowercase			:     Dict      					=				len(__UpperCAmelCase      ) // 2  # Finds the middle of the array
		_lowercase			:     List[str]      					=				arr[
		    :middle_length
		]  # Creates an array of the elements in the first half.
		_lowercase			:     Optional[int]      					=				arr[
		    middle_length:
		]  # Creates an array of the elements in the second half.
		_lowercase			:     Optional[Any]      					=				len(__UpperCAmelCase      )
		_lowercase			:     Optional[int]      					=				len(__UpperCAmelCase      )
		merge(__UpperCAmelCase      )  # Starts sorting the left.
		merge(__UpperCAmelCase      )  # Starts sorting the right
		_lowercase			:     Dict      					=				0  # Left Counter
		_lowercase			:     List[str]      					=				0  # Right Counter
		_lowercase			:     Optional[Any]      					=				0  # Position Counter
		while (
		    left_index < left_size and right_index < right_size
		):  # Runs until the lowers size of the left and right are sorted.
			if left_array[left_index] < right_array[right_index]:
				_lowercase			:     Optional[int]      					=				left_array[left_index]
				left_index += 1
			else:
				_lowercase			:     int      					=				right_array[right_index]
				right_index += 1
			index += 1
		while (
		    left_index < left_size
		):  # Adds the left over elements in the left half of the array
			_lowercase			:     Any      					=				left_array[left_index]
			left_index += 1
			index += 1
		while (
		    right_index < right_size
		):  # Adds the left over elements in the right half of the array
			_lowercase			:     str      					=				right_array[right_index]
			right_index += 1
			index += 1
	return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



