



'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  SCREAMING_SNAKE_CASE (  a__		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (DDPMParallelScheduler,)






    def  A       (  self      :					Dict  ,						**UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  {
           'num_train_timesteps': 1_0_0_0,
           'beta_start': 0.0_0_0_1,
           'beta_end': 0.0_2,
           'beta_schedule': 'linear',
           'variance_type': 'fixed_small',
           'clip_sample': True,
       }

       config.update(**SCREAMING_SNAKE_CASE_ )
       return config






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,						[0.0_0_2, 0.0_2, 0.2, 2] ):
          self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_  ,						beta_end=SCREAMING_SNAKE_CASE_ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







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






    def  A       (  self      :					int ):


       """simple docstring"""







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






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







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






    def  A       (  self      :					str ):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       for t in [0, 5_0_0, 9_9_9]:
          self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_0_9_7_9 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.0_2 ) ) < 1E-5






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  len(SCREAMING_SNAKE_CASE_ )

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

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

       UpperCamelCase					=  model(samples.flatten(0  ,						1 )  ,						timesteps.flatten(0  ,						1 ) )
       UpperCamelCase					=  scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_  ,						timesteps.flatten(0  ,						1 )  ,						samples.flatten(0  ,						1 ) )

       UpperCamelCase					=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
       UpperCamelCase					=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

       assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
       assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  len(SCREAMING_SNAKE_CASE_ )

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

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

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

          UpperCamelCase					=  pred_prev_sample

       UpperCamelCase					=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
       UpperCamelCase					=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

       assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
       assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config(prediction_type='v_prediction' )
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  len(SCREAMING_SNAKE_CASE_ )

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

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

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

          UpperCamelCase					=  pred_prev_sample

       UpperCamelCase					=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
       UpperCamelCase					=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

       assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
       assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  [1_0_0, 8_7, 5_0, 1, 0]

       scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  scheduler.timesteps

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

          UpperCamelCase					=  scheduler.previous_timestep(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					=  prev_t.item()

          self.assertEqual(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  [1_0_0, 8_7, 5_0, 5_1, 0]

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






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  [1_0_0, 8_7, 5_0, 1, 0]
       UpperCamelCase					=  len(SCREAMING_SNAKE_CASE_ )

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






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**SCREAMING_SNAKE_CASE_ )

       UpperCamelCase					=  [scheduler.config.num_train_timesteps]

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






from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



