




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   (UnCLIPScheduler,)
      def 			lowerCAmelCase__						(				self: List[Any]    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{
                 """num_train_timesteps""": 10_00,
                 """variance_type""": """fixed_small_log""",
                 """clip_sample""": True,
                 """clip_sample_range""": 1.0,
                 """prediction_type""": """epsilon""",
             }

             config.update(**UpperCamelCase_   )
             return config
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             for timesteps in [1, 5, 1_00, 10_00]:
                    self.check_over_configs(num_train_timesteps=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             for variance in ["fixed_small_log", "learned_range"]:
                    self.check_over_configs(variance_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Any   ):
             for clip_sample_range in [1, 5, 10, 20]:
                    self.check_over_configs(clip_sample_range=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(prediction_type=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             for time_step in [0, 5_00, 9_99]:
                    for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                           if prev_timestep is not None and prev_timestep >= time_step:
                                  continue

                           self.check_over_forward(time_step=UpperCamelCase_    ,       prev_timestep=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config(variance_type="""fixed_small_log"""   )
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.054_9625   )   ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.999_4987   )   ) < 1E-5
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config(variance_type="""learned_range"""   )
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             __lowerCamelCase   =						0.5

             assert scheduler._get_variance(1    ,       predicted_variance=UpperCamelCase_   ) - -10.171_2790 < 1E-5
             assert scheduler._get_variance(4_87    ,       predicted_variance=UpperCamelCase_   ) - -5.799_8052 < 1E-5
             assert scheduler._get_variance(9_99    ,       predicted_variance=UpperCamelCase_   ) - -0.001_0011 < 1E-5
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config()
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             __lowerCamelCase   =						scheduler.timesteps

             __lowerCamelCase   =						self.dummy_model()
             __lowerCamelCase   =						self.dummy_sample_deter
             __lowerCamelCase   =						torch.manual_seed(0   )

             for i, t in enumerate(UpperCamelCase_   ):
                    # 1. predict noise residual
                    __lowerCamelCase   =						model(UpperCamelCase_    ,       UpperCamelCase_   )

                    # 2. predict previous mean of sample x_t-1
                    __lowerCamelCase   =						scheduler.step(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       generator=UpperCamelCase_   ).prev_sample

                    __lowerCamelCase   =						pred_prev_sample

             __lowerCamelCase   =						torch.sum(torch.abs(UpperCamelCase_   )   )
             __lowerCamelCase   =						torch.mean(torch.abs(UpperCamelCase_   )   )

             assert abs(result_sum.item() - 252.268_2495   ) < 1E-2
             assert abs(result_mean.item() - 0.328_4743   ) < 1E-3
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.scheduler_classes[0]
             __lowerCamelCase   =						self.get_scheduler_config()
             __lowerCamelCase   =						scheduler_class(**UpperCamelCase_   )

             scheduler.set_timesteps(25   )

             __lowerCamelCase   =						scheduler.timesteps

             __lowerCamelCase   =						self.dummy_model()
             __lowerCamelCase   =						self.dummy_sample_deter
             __lowerCamelCase   =						torch.manual_seed(0   )

             for i, t in enumerate(UpperCamelCase_   ):
                    # 1. predict noise residual
                    __lowerCamelCase   =						model(UpperCamelCase_    ,       UpperCamelCase_   )

                    if i + 1 == timesteps.shape[0]:
                           __lowerCamelCase   =						None
                    else:
                           __lowerCamelCase   =						timesteps[i + 1]

                    # 2. predict previous mean of sample x_t-1
                    __lowerCamelCase   =						scheduler.step(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       prev_timestep=UpperCamelCase_    ,       generator=UpperCamelCase_   ).prev_sample

                    __lowerCamelCase   =						pred_prev_sample

             __lowerCamelCase   =						torch.sum(torch.abs(UpperCamelCase_   )   )
             __lowerCamelCase   =						torch.mean(torch.abs(UpperCamelCase_   )   )

             assert abs(result_sum.item() - 258.204_4983   ) < 1E-2
             assert abs(result_mean.item() - 0.336_2038   ) < 1E-3
      def 			lowerCAmelCase__						(				self: Any   ):
             pass




      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
       import torch.distributed.checkpoint as dist_cp
       from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
       from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
       from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCAmelCase_									=	get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					Union[str, Any]  ,			A__       :					str  ,			A__       :					Any  ,			A__       :					Dict  ,			A__       :					Any=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     if accelerator.process_index == 0:
                            logger.info(f'Saving model to {output_model_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Saving model to {output_model_file}'	)
                     torch.save(A__  ,			A__	)
                     logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving model to {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": state_dict}

                     dist_cp.save_state_dict(
                         state_dict=A__  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Model saved to {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Dict  ,			A__       :					int  ,			A__       :					List[str]  ,			A__       :					Any=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(A__	) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
                                       """initializing FSDP object"""	)
                            return
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                         if f'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading model from {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=A__  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			planner=DefaultLoadPlanner()  ,			)
                     __lowerCamelCase   =						state_dict["""model"""]
                     logger.info(f'Model loaded from {ckpt_dir}'	)
              model.load_state_dict(A__	)




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[str]  ,			A__       :					str  ,			A__       :					Dict  ,			A__       :					Optional[Any]  ,			A__       :					Optional[int]=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						FSDP.optim_state_dict(A__  ,			A__	)
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            __lowerCamelCase   =						(
                                f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                            logger.info(f'Saving Optimizer state to {output_optimizer_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Optimizer state saved in {output_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving Optimizer state to {ckpt_dir}'	)
                     dist_cp.save_state_dict(
                         state_dict={"""optimizer""": optim_state}  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Optimizer state saved in {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[str]  ,			A__       :					int  ,			A__       :					Any  ,			A__       :					Union[str, Any]  ,			A__       :					List[Any]=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     __lowerCamelCase   =						(
                         f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                         if f'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading Optimizer from {ckpt_dir}'	)
                     __lowerCamelCase   =						load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,			optimizer_key="""optimizer"""  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			)
                     __lowerCamelCase   =						optim_state["""optimizer"""]
                     logger.info(f'Optimizer loaded from {ckpt_dir}'	)
              __lowerCamelCase   =						FSDP.optim_state_dict_to_load(A__  ,			A__  ,			A__	)
              optimizer.load_state_dict(A__	)

