



'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    (HeunDiscreteScheduler,)
 UpperCAmelCase		      =    10







 def _snake_case					(				self    ,**a_				)						->  Any:
        _UpperCAmelCase    :		int     	=					{
            """num_train_timesteps""": 1_100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**a_				)
        return config







 def _snake_case					(				self				)						->  Dict:
        for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,[0.0002, 0.002, 0.02]				):
               self.check_over_configs(beta_start=a_    ,beta_end=a_				)







 def _snake_case					(				self				)						->  Tuple:
        for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=a_				)







 def _snake_case					(				self				)						->  str:
        for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=a_    ,clip_sample=a_				)







 def _snake_case					(				self				)						->  str:
        for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=a_				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Any     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		str     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		Optional[int]     	=					scheduler_class(**a_				)

        scheduler.set_timesteps(self.num_inference_steps				)

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

        for i, t in enumerate(scheduler.timesteps				):
               _UpperCAmelCase    :		Optional[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		int     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		Any     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		List[str]     	=					output.prev_sample

        _UpperCAmelCase    :		Dict     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Optional[int]     	=					torch.mean(torch.abs(a_				)				)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		List[Any]     	=					self.get_scheduler_config(prediction_type="""v_prediction"""				)
        _UpperCAmelCase    :		List[str]     	=					scheduler_class(**a_				)

        scheduler.set_timesteps(self.num_inference_steps				)

        _UpperCAmelCase    :		List[Any]     	=					self.dummy_model()
        _UpperCAmelCase    :		List[Any]     	=					self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCAmelCase    :		Optional[int]     	=					sample.to(a_				)

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

               _UpperCAmelCase    :		int     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		str     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		int     	=					output.prev_sample

        _UpperCAmelCase    :		str     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Optional[int]     	=					torch.mean(torch.abs(a_				)				)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7				) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0				) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		Union[str, Any]     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		Optional[int]     	=					scheduler_class(**a_				)

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

        _UpperCAmelCase    :		Any     	=					self.dummy_model()
        _UpperCAmelCase    :		Any     	=					self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
               _UpperCAmelCase    :		List[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		Tuple     	=					model(a_    ,a_				)

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

        _UpperCAmelCase    :		int     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		Any     	=					torch.mean(torch.abs(a_				)				)

        if str(a_				).startswith("""cpu"""				):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3
        elif str(a_				).startswith("""mps"""				):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002				) < 1E-2
        else:
               # CUDA
               assert abs(result_sum.item() - 0.1233				) < 1E-2
               assert abs(result_mean.item() - 0.0002				) < 1E-3







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Dict     	=					self.scheduler_classes[0]
        _UpperCAmelCase    :		Any     	=					self.get_scheduler_config()
        _UpperCAmelCase    :		str     	=					scheduler_class(**a_    ,use_karras_sigmas=a_				)

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

        _UpperCAmelCase    :		int     	=					self.dummy_model()
        _UpperCAmelCase    :		Union[str, Any]     	=					self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma
        _UpperCAmelCase    :		List[str]     	=					sample.to(a_				)

        for t in scheduler.timesteps:
               _UpperCAmelCase    :		Optional[Any]     	=					scheduler.scale_model_input(a_    ,a_				)

               _UpperCAmelCase    :		Optional[Any]     	=					model(a_    ,a_				)

               _UpperCAmelCase    :		Tuple     	=					scheduler.step(a_    ,a_    ,a_				)
               _UpperCAmelCase    :		Optional[int]     	=					output.prev_sample

        _UpperCAmelCase    :		Tuple     	=					torch.sum(torch.abs(a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					torch.mean(torch.abs(a_				)				)

        assert abs(result_sum.item() - 0.0_0015				) < 1E-2
        assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7				) < 1E-2







from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




