





'''simple docstring'''

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = (DDPMParallelScheduler,)


    def 			snake_case_(		self ,		**A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			{
            "num_train_timesteps": 1000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**UpperCamelCase__       )
        return config


    def 			snake_case_(		self       )		->   Optional[Any]:




        for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   List[str]:




        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,		[0.002, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=UpperCamelCase__ ,		beta_end=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   int:




        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   str:




        for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   Dict:




        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   List[str]:
        self.check_over_configs(thresholding=UpperCamelCase__       )
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=UpperCamelCase__ ,		prediction_type=UpperCamelCase__ ,		sample_max_value=UpperCamelCase__ ,		)


    def 			snake_case_(		self       )		->   Union[str, Any]:




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


    def 			snake_case_(		self       )		->   Union[str, Any]:




        for t in [0, 500, 999]:
            self.check_over_forward(time_step=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_0979       )       ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1e-5


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			len(UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			self.dummy_model()
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter + 0.1
        _SCREAMING_SNAKE_CASE									=			self.dummy_sample_deter - 0.1

        _SCREAMING_SNAKE_CASE									=			samplea.shape[0]
        _SCREAMING_SNAKE_CASE									=			torch.stack([samplea, samplea, samplea] ,		dim=0       )
        _SCREAMING_SNAKE_CASE									=			torch.arange(UpperCamelCase__       )[0:3, None].repeat(1 ,		UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			model(samples.flatten(0 ,		1       ) ,		timesteps.flatten(0 ,		1       )       )
        _SCREAMING_SNAKE_CASE									=			scheduler.batch_step_no_noise(UpperCamelCase__ ,		timesteps.flatten(0 ,		1       ) ,		samples.flatten(0 ,		1       )       )

        _SCREAMING_SNAKE_CASE									=			torch.sum(torch.abs(UpperCamelCase__       )       )
        _SCREAMING_SNAKE_CASE									=			torch.mean(torch.abs(UpperCamelCase__       )       )

        assert abs(result_sum.item() - 1153.1833       ) < 1e-2
        assert abs(result_mean.item() - 0.5005       ) < 1e-3


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			len(UpperCamelCase__       )

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

        for t in reversed(range(UpperCamelCase__       )       ):
            # 1. predict noise residual
            _SCREAMING_SNAKE_CASE									=			model(UpperCamelCase__ ,		UpperCamelCase__       )

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

            _SCREAMING_SNAKE_CASE									=			pred_prev_sample

        _SCREAMING_SNAKE_CASE									=			torch.sum(torch.abs(UpperCamelCase__       )       )
        _SCREAMING_SNAKE_CASE									=			torch.mean(torch.abs(UpperCamelCase__       )       )

        assert abs(result_sum.item() - 258.9606       ) < 1e-2
        assert abs(result_mean.item() - 0.3372       ) < 1e-3


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config(prediction_type="""v_prediction"""       )
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			len(UpperCamelCase__       )

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

        for t in reversed(range(UpperCamelCase__       )       ):
            # 1. predict noise residual
            _SCREAMING_SNAKE_CASE									=			model(UpperCamelCase__ ,		UpperCamelCase__       )

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

            _SCREAMING_SNAKE_CASE									=			pred_prev_sample

        _SCREAMING_SNAKE_CASE									=			torch.sum(torch.abs(UpperCamelCase__       )       )
        _SCREAMING_SNAKE_CASE									=			torch.mean(torch.abs(UpperCamelCase__       )       )

        assert abs(result_sum.item() - 202.0296       ) < 1e-2
        assert abs(result_mean.item() - 0.2631       ) < 1e-3


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			scheduler.timesteps

        for i, timestep in enumerate(UpperCamelCase__       ):
            if i == len(UpperCamelCase__       ) - 1:
                _SCREAMING_SNAKE_CASE									=			-1
            else:
                _SCREAMING_SNAKE_CASE									=			timesteps[i + 1]

            _SCREAMING_SNAKE_CASE									=			scheduler.previous_timestep(UpperCamelCase__       )
            _SCREAMING_SNAKE_CASE									=			prev_t.item()

            self.assertEqual(UpperCamelCase__ ,		UpperCamelCase__       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			[100, 87, 50, 51, 0]

        with self.assertRaises(UpperCamelCase__ ,		msg="""`custom_timesteps` must be in descending order."""       ):
            scheduler.set_timesteps(timesteps=UpperCamelCase__       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			[100, 87, 50, 1, 0]
        _SCREAMING_SNAKE_CASE									=			len(UpperCamelCase__       )

        with self.assertRaises(UpperCamelCase__ ,		msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""       ):
            scheduler.set_timesteps(num_inference_steps=UpperCamelCase__ ,		timesteps=UpperCamelCase__       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE									=			self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE									=			scheduler_class(**UpperCamelCase__       )

        _SCREAMING_SNAKE_CASE									=			[scheduler.config.num_train_timesteps]

        with self.assertRaises(
            UpperCamelCase__ ,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,		):
            scheduler.set_timesteps(timesteps=UpperCamelCase__       )







"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list )	->						int:


    """simple docstring"""





    if not grid or not grid[0]:
        raise TypeError("The grid does not contain the appropriate information" )

    for cell_n in range(1						,							len(grid[0] ) ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    snake_case    :      int      					=    grid[0]

    for row_n in range(1						,							len(lowercase ) ):
        snake_case    :      Optional[Any]      					=    grid[row_n]
        snake_case    :      List[str]      					=    fill_row(lowercase						,							lowercase )
        snake_case    :      Optional[int]      					=    grid[row_n]

    return grid[-1][-1]







def   __lowerCAmelCase						(						lowercase  :  list						,							lowercase  :  list )	->						list:


    """simple docstring"""





    current_row[0] += row_above[0]
    for cell_n in range(1						,							len(lowercase ) ):
        current_row[cell_n] += min(current_row[cell_n - 1]						,							row_above[cell_n] )

    return current_row


if __name__ == "__main__":
  import doctest

  doctest.testmod()





