




'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = (LMSDiscreteScheduler,)
					lowercase__    = 1_0






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       {
							    "num_train_timesteps": 1_100,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__UpperCamelCase )
							return config






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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							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=__UpperCamelCase,  beta_end=__UpperCamelCase )






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






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






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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**__UpperCamelCase )

							scheduler.set_timesteps(self.num_inference_steps )

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

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       scheduler.scale_model_input(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(__UpperCamelCase ) )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config(prediction_type="v_prediction" )
							_snake_case	       =       scheduler_class(**__UpperCamelCase )

							scheduler.set_timesteps(self.num_inference_steps )

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

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       scheduler.scale_model_input(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(__UpperCamelCase ) )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**__UpperCamelCase )

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

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							_snake_case	       =       sample.to(__UpperCamelCase )

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       scheduler.scale_model_input(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(__UpperCamelCase ) )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**__UpperCamelCase,  use_karras_sigmas=__UpperCamelCase )

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

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter.to(__UpperCamelCase ) * scheduler.init_noise_sigma
							_snake_case	       =       sample.to(__UpperCamelCase )

							for t in scheduler.timesteps:
									_snake_case	       =       scheduler.scale_model_input(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       model(__UpperCamelCase,  __UpperCamelCase )

									_snake_case	       =       scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(__UpperCamelCase ) )
							_snake_case	       =       torch.mean(torch.abs(__UpperCamelCase ) )

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











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


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				List[dict]   , lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int   , lowerCamelCase:				GradScaler = None   , )    ->	Any:


      '''simple docstring'''




      for i, state in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = opt.state_dict()
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = scheduler.state_dict()
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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:
            lowerCamelCase__					     = scaler.state_dict()
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            torch.save(lowerCamelCase   , lowerCamelCase  )
            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(lowerCamelCase   , lowerCamelCase  )
      torch.save(lowerCamelCase   , lowerCamelCase  )
      logger.info(f"""Random states saved in {output_states_file}"""  )
      return output_dir

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Dict   , lowerCamelCase:				Union[str, Any]   , lowerCamelCase:				Any   , lowerCamelCase:				Any   , lowerCamelCase:				Any=None   , lowerCamelCase:				Dict=None   , **lowerCamelCase:				Optional[Any]   , )    ->	Any:


      '''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(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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:
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scaler.load_state_dict(torch.load(lowerCamelCase  )  )
            logger.info('GradScaler state loaded successfully'  )

      # Random states
      try:
            lowerCamelCase__					     = 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 	lowerCAmelCase_	(     lowerCamelCase:				Dict   , lowerCamelCase:				Any   , lowerCamelCase:				int = 0  )    ->	Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__					     = 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 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				int = 0  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = 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'  )  )