import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				(DDIMParallelScheduler,)
    a__     :						List[Any]      =				(("""eta""", 0.0), ("""num_inference_steps""", 5_0))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Dict:
        __UpperCamelCase					:List[Any]        =		{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.00_01,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowercase)
        return config







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     int:
        __UpperCamelCase					:Tuple        =		self.scheduler_classes[0]
        __UpperCamelCase					:Optional[int]        =		self.get_scheduler_config(**__lowercase)
        __UpperCamelCase					:int        =		scheduler_class(**__lowercase)

        __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		10, 0.0

        __UpperCamelCase					:List[str]        =		self.dummy_model()
        __UpperCamelCase					:Union[str, Any]        =		self.dummy_sample_deter

        scheduler.set_timesteps(__lowercase)

        for t in scheduler.timesteps:
            __UpperCamelCase					:Optional[int]        =		model(__lowercase			,			__lowercase)
            __UpperCamelCase					:Any        =		scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase).prev_sample

        return sample







    def       UpperCamelCase__       (     self)	->     int:
        for timesteps in [100, 500, 1_000]:
            self.check_over_configs(num_train_timesteps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=__lowercase)

        __UpperCamelCase					:Optional[int]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Any        =		self.get_scheduler_config(steps_offset=1)
        __UpperCamelCase					:Union[str, Any]        =		scheduler_class(**__lowercase)
        scheduler.set_timesteps(5)
        assert torch.equal(scheduler.timesteps			,			torch.LongTensor([801, 601, 401, 201, 1]))







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,			[0.0_02, 0.02, 0.2, 2]):
            self.check_over_configs(beta_start=__lowercase			,			beta_end=__lowercase)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowercase)







    def       UpperCamelCase__       (     self)	->     int:
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        self.check_over_configs(thresholding=__lowercase)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=__lowercase			,			prediction_type=__lowercase			,			sample_max_value=__lowercase			,			)







    def       UpperCamelCase__       (     self)	->     Tuple:
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        for t, num_inference_steps in zip([1, 10, 50]			,			[10, 50, 500]):
            self.check_over_forward(time_step=__lowercase			,			num_inference_steps=__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        for t, eta in zip([1, 10, 49]			,			[0.0, 0.5, 1.0]):
            self.check_over_forward(time_step=__lowercase			,			eta=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[str]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Tuple        =		self.get_scheduler_config()
        __UpperCamelCase					:str        =		scheduler_class(**__lowercase)

        assert torch.sum(torch.abs(scheduler._get_variance(0			,			0) - 0.0)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420			,			400) - 0.1_47_71)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980			,			960) - 0.3_24_60)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0			,			0) - 0.0)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487			,			486) - 0.0_09_79)) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999			,			998) - 0.02)) < 1E-5







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[Any]        =		self.scheduler_classes[0]
        __UpperCamelCase					:Tuple        =		self.get_scheduler_config()
        __UpperCamelCase					:List[Any]        =		scheduler_class(**__lowercase)

        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		10, 0.0
        scheduler.set_timesteps(__lowercase)

        __UpperCamelCase					:List[str]        =		self.dummy_model()
        __UpperCamelCase					:List[str]        =		self.dummy_sample_deter
        __UpperCamelCase					:int        =		self.dummy_sample_deter + 0.1
        __UpperCamelCase					:Any        =		self.dummy_sample_deter - 0.1

        __UpperCamelCase					:Tuple        =		samplea.shape[0]
        __UpperCamelCase					:List[str]        =		torch.stack([samplea, samplea, samplea]			,			dim=0)
        __UpperCamelCase					:Dict        =		torch.arange(__lowercase)[0:3, None].repeat(1			,			__lowercase)

        __UpperCamelCase					:Optional[Any]        =		model(samples.flatten(0			,			1)			,			timesteps.flatten(0			,			1))
        __UpperCamelCase					:Union[str, Any]        =		scheduler.batch_step_no_noise(__lowercase			,			timesteps.flatten(0			,			1)			,			samples.flatten(0			,			1)			,			__lowercase)

        __UpperCamelCase					:Optional[Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:Optional[Any]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 11_47.79_04) < 1E-2
        assert abs(result_mean.item() - 0.49_82) < 1E-3







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		self.full_loop()

        __UpperCamelCase					:Union[str, Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:int        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 1_72.00_67) < 1E-2
        assert abs(result_mean.item() - 0.22_39_67) < 1E-3







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:str        =		self.full_loop(prediction_type='''v_prediction''')

        __UpperCamelCase					:Dict        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:str        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 52.53_02) < 1E-2
        assert abs(result_mean.item() - 0.06_84) < 1E-3







    def       UpperCamelCase__       (     self)	->     Tuple:
        # We specify different beta, so that the first alpha is 0.99
        __UpperCamelCase					:List[Any]        =		self.full_loop(set_alpha_to_one=__lowercase			,			beta_start=0.01)
        __UpperCamelCase					:Any        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:List[str]        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 1_49.82_95) < 1E-2
        assert abs(result_mean.item() - 0.19_51) < 1E-3







    def       UpperCamelCase__       (     self)	->     Dict:
        # We specify different beta, so that the first alpha is 0.99
        __UpperCamelCase					:Optional[int]        =		self.full_loop(set_alpha_to_one=__lowercase			,			beta_start=0.01)
        __UpperCamelCase					:Union[str, Any]        =		torch.sum(torch.abs(__lowercase))
        __UpperCamelCase					:Any        =		torch.mean(torch.abs(__lowercase))

        assert abs(result_sum.item() - 1_49.07_84) < 1E-2
        assert abs(result_mean.item() - 0.19_41) < 1E-3






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()





