




"""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


__A							=     get_logger(__name__)



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



def 					UpperCamelCase__       (						lowercase__					:       Any    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       Union[str, Any]    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       str    ,       lowercase__					:       Tuple=None    ,       lowercase__					:       List[Any]=None    ,       **lowercase__					:       List[Any]    ,       ):
		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						:  Optional[int]        =			"cpu"
		elif map_location == "on_device":
				snake_case						:  Union[str, Any]        =			PartialState().device
		# Model states
		for i, model in enumerate(lowercase__				):
				snake_case						:  Union[str, Any]        =			F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
				snake_case						:  List[Any]        =			os.path.join(lowercase__    ,       lowercase__				)
				models[i].load_state_dict(torch.load(lowercase__    ,       map_location=lowercase__				)    ,       **lowercase__				)
		logger.info("All model weights loaded successfully"				)

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

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

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

		# Random states
		try:
				snake_case						:  Dict        =			torch.load(os.path.join(lowercase__    ,       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__       (						lowercase__					:       Optional[int]    ,       lowercase__					:       Any    ,       lowercase__					:       int = 0				):
		snake_case						:  List[Any]        =			Path(lowercase__				) / F'''custom_checkpoint_{index}.pkl'''
		logger.info(F'''Saving the state of {get_pretty_name(lowercase__				)} to {save_location}'''				)
		torch.save(obj.state_dict()    ,       lowercase__				)



def 					UpperCamelCase__       (						lowercase__					:       Tuple    ,       lowercase__					:       str    ,       lowercase__					:       int = 0				):
		snake_case						:  Dict        =			F'''{path}/custom_checkpoint_{index}.pkl'''
		logger.info(F'''Loading the state of {get_pretty_name(lowercase__				)} from {load_location}'''				)
		obj.load_state_dict(torch.load(lowercase__    ,       map_location="cpu"				)				)







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
							from doctest import testmod

							testmod()

