


'''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


_lowerCAmelCase	:		Optional[int]		  =						get_logger(__name__)

def       __UpperCamelCase     (     _A     :					str       , _A     :					List[dict]       , _A     :					list       , _A     :					list       , _A     :					int       , _A     :					GradScaler = None       , )     ->   Union[str, Any]:




	"""simple docstring"""





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

def       __UpperCamelCase     (     _A     :					Any       , _A     :					List[str]       , _A     :					str       , _A     :					Union[str, Any]       , _A     :					str       , _A     :					Union[str, Any]=None       , _A     :					Optional[Any]=None       , **_A     :					Union[str, Any]       , )     ->   Dict:




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

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

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

	# GradScaler state
	if scaler is not None:
		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(_A       , _A					)
		scaler.load_state_dict(torch.load(_A					)					)
		logger.info('GradScaler state loaded successfully'					)

	# Random states
	try:
		lowerCAmelCase       :     Union[str, Any]   				=			torch.load(os.path.join(_A       , 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     (     _A     :					Any       , _A     :					Tuple       , _A     :					int = 0					)     ->   Optional[int]:




	"""simple docstring"""





	lowerCAmelCase       :     List[Any]   				=			Path(_A					) / F"custom_checkpoint_{index}.pkl"
	logger.info(F"Saving the state of {get_pretty_name(_A					)} to {save_location}"					)
	torch.save(obj.state_dict()       , _A					)






def       __UpperCamelCase     (     _A     :					List[Any]       , _A     :					Any       , _A     :					int = 0					)     ->   List[str]:




	"""simple docstring"""





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




'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax



@require_flax
class 			lowerCAmelCase					:



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Any   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     Tuple   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     int   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     List[str]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Tuple   				=			16
		lowerCAmelCase       :     Dict   				=			(16, 32)

		lowerCAmelCase       :     Dict   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     int   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			'kwargs' in inspect.signature(self.model_class.__init__     ).parameters
		lowerCAmelCase       :     Optional[Any]   				=			len(self.model_class._deprecated_kwargs     ) > 0

		if has_kwarg_in_model_class and not has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
			    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
			    ' [<deprecated_argument>]`'     )

		if not has_kwarg_in_model_class and has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
			    f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
			    ' from `_deprecated_kwargs = [<deprecated_argument>]`'     )
