"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							(LMSDiscreteScheduler,)
			_UpperCamelCase     :	Optional[Any]				    =							10





			def 		lowerCamelCase				( self      ,			**snake_case_       ):







										'''simple docstring'''


										A__					:    str														= {
										    """num_train_timesteps""": 1_100,
										    """beta_start""": 0.00_01,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**snake_case_       )
										return config





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,			[0.00_02, 0.0_02, 0.02]       ):
																	self.check_over_configs(beta_start=snake_case_      ,			beta_end=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= self.scheduler_classes[0]
										A__					:    Union[str, Any]														= self.get_scheduler_config()
										A__					:    List[Any]														= scheduler_class(**snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps       )

										A__					:    Union[str, Any]														= self.dummy_model()
										A__					:    str														= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    str														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[Any]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    Optional[int]														= output.prev_sample

										A__					:    Tuple														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    str														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 10_06.3_88       ) < 1E-2
										assert abs(result_mean.item() - 1.31       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= self.scheduler_classes[0]
										A__					:    Any														= self.get_scheduler_config(prediction_type="""v_prediction"""       )
										A__					:    Optional[int]														= scheduler_class(**snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps       )

										A__					:    Union[str, Any]														= self.dummy_model()
										A__					:    Tuple														= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    Union[str, Any]														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Tuple														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    Any														= output.prev_sample

										A__					:    Union[str, Any]														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    Optional[int]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 0.00_17       ) < 1E-2
										assert abs(result_mean.item() - 2.2676E-06       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.scheduler_classes[0]
										A__					:    int														= self.get_scheduler_config()
										A__					:    str														= scheduler_class(**snake_case_       )

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

										A__					:    str														= self.dummy_model()
										A__					:    Optional[Any]														= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										A__					:    Any														= sample.to(snake_case_       )

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    List[Any]														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Optional[int]														= model(snake_case_      ,			snake_case_       )

																	A__					:    str														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    List[str]														= output.prev_sample

										A__					:    str														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    Union[str, Any]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 10_06.3_88       ) < 1E-2
										assert abs(result_mean.item() - 1.31       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= self.scheduler_classes[0]
										A__					:    str														= self.get_scheduler_config()
										A__					:    Union[str, Any]														= scheduler_class(**snake_case_      ,			use_karras_sigmas=snake_case_       )

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

										A__					:    str														= self.dummy_model()
										A__					:    Union[str, Any]														= self.dummy_sample_deter.to(snake_case_       ) * scheduler.init_noise_sigma
										A__					:    Optional[int]														= sample.to(snake_case_       )

										for t in scheduler.timesteps:
																	A__					:    Tuple														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Optional[Any]														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    int														= output.prev_sample

										A__					:    Dict														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    List[Any]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 38_12.99_27       ) < 2E-2
										assert abs(result_mean.item() - 4.96_48       ) < 1E-3






"""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


_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = None ,     ):
							# Model states
							for i, state in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= 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__					:    Tuple														= opt.state_dict()
														A__					:    Union[str, Any]														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Tuple														= 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__					:    List[str]														= scheduler.state_dict()
														A__					:    Dict														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= 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__					:    Dict														= scaler.state_dict()
														A__					:    Optional[int]														= 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__					:    Tuple														= {}
							A__					:    Any														= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
							A__					:    Any														= random.getstate()
							A__					:    Any														= np.random.get_state()
							A__					:    int														= torch.get_rng_state()
							if is_xpu_available():
														A__					:    List[str]														= torch.xpu.get_rng_state_all()
							else:
														A__					:    Union[str, Any]														= torch.cuda.get_rng_state_all()
							if is_tpu_available():
														A__					:    str														= xm.get_rng_state()
							A__					:    str														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
							torch.save(lowerCAmelCase ,     lowerCAmelCase       )
							logger.info(F'''Random states saved in {output_states_file}'''       )
							return output_dir

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							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__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= 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__					:    str														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														A__					:    List[str]														= 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__					:    Any														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= 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__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= 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"""       )       )





