'''simple docstring'''

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


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


      UpperCAmelCase_      :      int      							=   (DDPMParallelScheduler,)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowerCamelCase_							)
         return config




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         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=lowerCamelCase_					,beta_end=lowerCamelCase_							)




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



         '''simple docstring'''



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




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



         '''simple docstring'''



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




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



         '''simple docstring'''



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




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



         '''simple docstring'''



         self.check_over_configs(thresholding=lowerCamelCase_							)
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=lowerCamelCase_					,prediction_type=lowerCamelCase_					,sample_max_value=lowerCamelCase_					,)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Any   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				List[str]   				=  scheduler_class(**lowerCamelCase_							)

         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 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Any   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				int   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  len(lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.dummy_model()
         UpperCAmelCase__							:				Optional[Any]   				=  self.dummy_sample_deter
         UpperCAmelCase__							:				int   				=  self.dummy_sample_deter + 0.1
         UpperCAmelCase__							:				Union[str, Any]   				=  self.dummy_sample_deter - 0.1

         UpperCAmelCase__							:				Tuple   				=  samplea.shape[0]
         UpperCAmelCase__							:				List[Any]   				=  torch.stack([samplea, samplea, samplea]					,dim=0							)
         UpperCAmelCase__							:				Optional[Any]   				=  torch.arange(lowerCamelCase_							)[0:3, None].repeat(1					,lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  model(samples.flatten(0					,1							)					,timesteps.flatten(0					,1							)							)
         UpperCAmelCase__							:				Tuple   				=  scheduler.batch_step_no_noise(lowerCamelCase_					,timesteps.flatten(0					,1							)					,samples.flatten(0					,1							)							)

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

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




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				List[Any]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Dict   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  len(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  self.dummy_model()
         UpperCAmelCase__							:				int   				=  self.dummy_sample_deter
         UpperCAmelCase__							:				Tuple   				=  torch.manual_seed(0							)

         for t in reversed(range(lowerCamelCase_							)							):
            # 1. predict noise residual
            UpperCAmelCase__							:				Optional[Any]   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase__							:				int   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,generator=lowerCamelCase_							).prev_sample

            UpperCAmelCase__							:				List[str]   				=  pred_prev_sample

         UpperCAmelCase__							:				Optional[int]   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Optional[Any]   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Dict   				=  self.get_scheduler_config(prediction_type='''v_prediction'''							)
         UpperCAmelCase__							:				int   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  len(lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_model()
         UpperCAmelCase__							:				List[str]   				=  self.dummy_sample_deter
         UpperCAmelCase__							:				Optional[Any]   				=  torch.manual_seed(0							)

         for t in reversed(range(lowerCamelCase_							)							):
            # 1. predict noise residual
            UpperCAmelCase__							:				List[Any]   				=  model(lowerCamelCase_					,lowerCamelCase_							)

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase__							:				Optional[int]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,generator=lowerCamelCase_							).prev_sample

            UpperCAmelCase__							:				str   				=  pred_prev_sample

         UpperCAmelCase__							:				Optional[int]   				=  torch.sum(torch.abs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				Any   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Any   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[Any]   				=  [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  scheduler.timesteps

         for i, timestep in enumerate(lowerCamelCase_							):
            if i == len(lowerCamelCase_							) - 1:
               UpperCAmelCase__							:				Tuple   				=  -1
            else:
               UpperCAmelCase__							:				Dict   				=  timesteps[i + 1]

            UpperCAmelCase__							:				str   				=  scheduler.previous_timestep(lowerCamelCase_							)
            UpperCAmelCase__							:				int   				=  prev_t.item()

            self.assertEqual(lowerCamelCase_					,lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Optional[int]   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[Any]   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  [100, 87, 50, 51, 0]

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				str   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  [100, 87, 50, 1, 0]
         UpperCAmelCase__							:				int   				=  len(lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				Dict   				=  self.get_scheduler_config()
         UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  [scheduler.config.num_train_timesteps]

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





'''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__)




