






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


UpperCamelCase__  :					Any  						=    get_logger(__name__)





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_ = None,     )      ->     int:


	"""simple docstring"""



	for i, state in enumerate(lowerCAmelCase__     ):
		a				=	f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
		a				=	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__     ):
		a				=	opt.state_dict()
		a				=	f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
		a				=	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__     ):
		a				=	scheduler.state_dict()
		a				=	f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
		a				=	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:
		a				=	scaler.state_dict()
		a				=	os.path.join(lowerCAmelCase__,     lowerCAmelCase__     )
		torch.save(lowerCAmelCase__,     lowerCAmelCase__     )
		logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""     )
	# Random number generator states
	a				=	{}
	a				=	f"""{RNG_STATE_NAME}_{process_index}.pkl"""
	a				=	random.getstate()
	a				=	np.random.get_state()
	a				=	torch.get_rng_state()
	if is_xpu_available():
		a				=	torch.xpu.get_rng_state_all()
	else:
		a				=	torch.cuda.get_rng_state_all()
	if is_tpu_available():
		a				=	xm.get_rng_state()
	a				=	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__  (      snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_,     snake_case_=None,     snake_case_=None,     **snake_case_,     )      ->     int:


	"""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:
		a				=	'''cpu'''
	elif map_location == "on_device":
		a				=	PartialState().device
	# Model states
	for i, model in enumerate(lowerCAmelCase__     ):
		a				=	f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
		a				=	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__     ):
		a				=	f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
		a				=	os.path.join(lowerCAmelCase__,     lowerCAmelCase__     )
		a				=	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__     ):
		a				=	f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
		a				=	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:
		a				=	os.path.join(lowerCAmelCase__,     lowerCAmelCase__     )
		scaler.load_state_dict(torch.load(lowerCAmelCase__     )     )
		logger.info('''GradScaler state loaded successfully'''     )

	# Random states
	try:
		a				=	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__  (      snake_case_,     snake_case_,     snake_case_ = 0     )      ->     int:


	"""simple docstring"""



	a				=	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__  (      snake_case_,     snake_case_,     snake_case_ = 0     )      ->     str:


	"""simple docstring"""



	a				=	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 ..utils import DummyObject, requires_backends
class 				a_ (						metaclass=SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					['tensorflow_text']




 def __init__(			self					:  Tuple			,    *lowercase__					:  str			,    **lowercase__					:  Union[str, Any]):







        '''simple docstring'''



        requires_backends(self			,    ['tensorflow_text'])
