


"""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


lowercase_							 =       get_logger(__name__)
def     UpperCAmelCase    (					_lowercase   :      str ,      _lowercase   :      List[dict] ,      _lowercase   :      list ,      _lowercase   :      list ,      _lowercase   :      int ,      _lowercase   :      GradScaler = None ,      )	->   str:


	"""simple docstring"""




	for i, state in enumerate(_lowercase  ):
		lowerCAmelCase_			       = F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
		lowerCAmelCase_			       = 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  ):
		lowerCAmelCase_			       = opt.state_dict()
		lowerCAmelCase_			       = F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
		lowerCAmelCase_			       = 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  ):
		lowerCAmelCase_			       = scheduler.state_dict()
		lowerCAmelCase_			       = F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
		lowerCAmelCase_			       = 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:
		lowerCAmelCase_			       = scaler.state_dict()
		lowerCAmelCase_			       = os.path.join(_lowercase ,      _lowercase  )
		torch.save(_lowercase ,      _lowercase  )
		logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""  )
	# Random number generator states
	lowerCAmelCase_			       = {}
	lowerCAmelCase_			       = F"""{RNG_STATE_NAME}_{process_index}.pkl"""
	lowerCAmelCase_			       = random.getstate()
	lowerCAmelCase_			       = np.random.get_state()
	lowerCAmelCase_			       = torch.get_rng_state()
	if is_xpu_available():
		lowerCAmelCase_			       = torch.xpu.get_rng_state_all()
	else:
		lowerCAmelCase_			       = torch.cuda.get_rng_state_all()
	if is_tpu_available():
		lowerCAmelCase_			       = xm.get_rng_state()
	lowerCAmelCase_			       = 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   :      Optional[int] ,      _lowercase   :      Tuple ,      _lowercase   :      Optional[int] ,      _lowercase   :      Any ,      _lowercase   :      int ,      _lowercase   :      Tuple=None ,      _lowercase   :      List[str]=None ,      **_lowercase   :      int ,      )	->   Optional[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:
		lowerCAmelCase_			       = '''cpu'''
	elif map_location == "on_device":
		lowerCAmelCase_			       = PartialState().device
	# Model states
	for i, model in enumerate(_lowercase  ):
		lowerCAmelCase_			       = F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
		lowerCAmelCase_			       = 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  ):
		lowerCAmelCase_			       = F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
		lowerCAmelCase_			       = os.path.join(_lowercase ,      _lowercase  )
		lowerCAmelCase_			       = 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  ):
		lowerCAmelCase_			       = F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
		lowerCAmelCase_			       = 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:
		lowerCAmelCase_			       = os.path.join(_lowercase ,      _lowercase  )
		scaler.load_state_dict(torch.load(_lowercase  )  )
		logger.info('''GradScaler state loaded successfully'''  )

	# Random states
	try:
		lowerCAmelCase_			       = 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   :      Any ,      _lowercase   :      Dict ,      _lowercase   :      int = 0  )	->   Any:


	"""simple docstring"""




	lowerCAmelCase_			       = 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   :      Union[str, Any] ,      _lowercase   :      List[str] ,      _lowercase   :      int = 0  )	->   List[Any]:


	"""simple docstring"""




	lowerCAmelCase_			       = 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"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase      )
					return config







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
						self.check_over_configs(beta_start=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for t in scheduler.timesteps:
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3