'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   (KDPMaDiscreteScheduler,)
      UpperCAmelCase_      :      List[str]      							=   10




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**lowerCamelCase_							)
         return config




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         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=lowerCamelCase_					,beta_end=lowerCamelCase_							)




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				List[str]   				=  self.get_scheduler_config(prediction_type='''v_prediction'''							)
         UpperCAmelCase__							:				List[Any]   				=  scheduler_class(**lowerCamelCase_							)

         scheduler.set_timesteps(self.num_inference_steps							)

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

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				Any   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Any   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[str]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Tuple   				=  output.prev_sample

         UpperCAmelCase__							:				str   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Dict   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.6934e-07							) < 1e-2
            assert abs(result_mean.item() - 6.1112e-10							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 4.693428650170972e-07							) < 1e-2
            assert abs(result_mean.item() - 0.0002							) < 1e-3




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         if torch_device == "mps":
            return
         UpperCAmelCase__							:				Dict   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Union[str, Any]   				=  scheduler_class(**lowerCamelCase_							)

         scheduler.set_timesteps(self.num_inference_steps							)

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

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				Optional[int]   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[Any]   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Dict   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				int   				=  output.prev_sample

         UpperCAmelCase__							:				List[Any]   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[str]   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         if torch_device == "mps":
            return
         UpperCAmelCase__							:				Dict   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

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

         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_model()
         UpperCAmelCase__							:				Union[str, Any]   				=  self.dummy_sample_deter.to(lowerCamelCase_							) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
            UpperCAmelCase__							:				Union[str, Any]   				=  scheduler.scale_model_input(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				Any   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				str   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				List[Any]   				=  output.prev_sample

         UpperCAmelCase__							:				Dict   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Dict   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         if str(lowerCamelCase_							).startswith('''cpu'''							):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3
         else:
            # CUDA
            assert abs(result_sum.item() - 20.4125							) < 1e-2
            assert abs(result_mean.item() - 0.0266							) < 1e-3





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__     :	Union[str, Any]				=			{'tokenization_byt5': ['ByT5Tokenizer']}


if TYPE_CHECKING:
  from .tokenization_byta import ByTaTokenizer
else:
  import sys

  UpperCamelCase__     :	List[str]				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




