

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      (DDIMParallelScheduler,)
    __a  =      (("eta", 0.0), ("num_inference_steps", 50))
    def 						UpperCamelCase_				(  self , **lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   str=      {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''clip_sample''': True,
     }

     config.update(**lowerCAmelCase   )
     return config
    def 						UpperCamelCase_				(  self , **lowerCAmelCase   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   int=      self.get_scheduler_config(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      10, 0.0

     SCREAMING_SNAKE_CASE__:   Tuple=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   int=      self.dummy_sample_deter

     scheduler.set_timesteps(lowerCAmelCase   )

     for t in scheduler.timesteps:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      model(lowerCAmelCase , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   List[str]=      scheduler.step(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   ).prev_sample

     return sample
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for timesteps in [100, 500, 1000]:
      self.check_over_configs(num_train_timesteps=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     for steps_offset in [0, 1]:
      self.check_over_configs(steps_offset=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.get_scheduler_config(steps_offset=1   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      scheduler_class(**lowerCAmelCase   )
     scheduler.set_timesteps(5   )
     assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1]   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     for timestep_spacing in ["trailing", "leading"]:
      self.check_over_configs(timestep_spacing=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     for rescale_betas_zero_snr in [True, False]:
      self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     self.check_over_configs(thresholding=lowerCAmelCase   )
     for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(
           thresholding=lowerCAmelCase , prediction_type=lowerCAmelCase , sample_max_value=lowerCAmelCase , )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     for t in [1, 10, 49]:
      self.check_over_forward(time_step=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500]   ):
      self.check_over_forward(time_step=lowerCAmelCase , num_inference_steps=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0]   ):
      self.check_over_forward(time_step=lowerCAmelCase , eta=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Tuple=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   str=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   List[str]=      scheduler_class(**lowerCAmelCase   )

     assert torch.sum(torch.abs(scheduler._get_variance(0 , 0   ) - 0.0   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(420 , 400   ) - 0.14771   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(980 , 960   ) - 0.32460   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0 , 0   ) - 0.0   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487 , 486   ) - 0.00979   )   ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999 , 998   ) - 0.02   )   ) < 1e-5
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__:   List[str]=      scheduler_class(**lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      10, 0.0
     scheduler.set_timesteps(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      self.dummy_model()
     SCREAMING_SNAKE_CASE__:   Tuple=      self.dummy_sample_deter
     SCREAMING_SNAKE_CASE__:   Tuple=      self.dummy_sample_deter + 0.1
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_sample_deter - 0.1

     SCREAMING_SNAKE_CASE__:   List[Any]=      samplea.shape[0]
     SCREAMING_SNAKE_CASE__:   Any=      torch.stack([samplea, samplea, samplea] , dim=0   )
     SCREAMING_SNAKE_CASE__:   List[str]=      torch.arange(lowerCAmelCase   )[0:3, None].repeat(1 , lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      model(samples.flatten(0 , 1   ) , timesteps.flatten(0 , 1   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      scheduler.batch_step_no_noise(lowerCAmelCase , timesteps.flatten(0 , 1   ) , samples.flatten(0 , 1   ) , lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   int=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 1147.7904   ) < 1e-2
     assert abs(result_mean.item() - 0.4982   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Any=      self.full_loop()

     SCREAMING_SNAKE_CASE__:   Any=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   List[str]=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 172.0067   ) < 1e-2
     assert abs(result_mean.item() - 0.223967   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Any=      self.full_loop(prediction_type='''v_prediction'''   )

     SCREAMING_SNAKE_CASE__:   str=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 52.5302   ) < 1e-2
     assert abs(result_mean.item() - 0.0684   ) < 1e-3
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     # We specify different beta, so that the first alpha is 0.99
     SCREAMING_SNAKE_CASE__:   int=      self.full_loop(set_alpha_to_one=lowerCAmelCase , beta_start=0.01   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 149.8295   ) < 1e-2
     assert abs(result_mean.item() - 0.1951   ) < 1e-3



    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     # We specify different beta, so that the first alpha is 0.99
     SCREAMING_SNAKE_CASE__:   Any=      self.full_loop(set_alpha_to_one=lowerCAmelCase , beta_start=0.01   )
     SCREAMING_SNAKE_CASE__:   int=      torch.sum(torch.abs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 149.0784   ) < 1e-2
     assert abs(result_mean.item() - 0.1941   ) < 1e-3


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowercase_     :   str 				=				re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      None
    __a  =      None
    __a  =      None
    __a  =      None
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      _str_to_version_tuple(self.version_str   )
    def __repr__(  self   )       ->					Dict:
     return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return self.major, self.minor, self.patch
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return Version(lowerCAmelCase   )
     elif isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return other
     raise TypeError(f'{other} (type {type(lowerCAmelCase   )}) cannot be compared to version.'   )
    def __eq__(  self , lowerCAmelCase   )       ->					Optional[int]:
     try:
      SCREAMING_SNAKE_CASE__:   List[str]=      self._validate_operand(lowerCAmelCase   )
     except (TypeError, ValueError):
      return False
     else:
      return self.tuple == other.tuple
    def __lt__(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._validate_operand(lowerCAmelCase   )
     return self.tuple < other.tuple
    def __hash__(  self   )       ->					List[Any]:
     return hash(_version_tuple_to_str(self.tuple   )   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      {f.name for f in dataclasses.fields(cls   )}
     return cls(**{k: v for k, v in dic.items() if k in field_names}   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     return self.version_str







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   List[Any]=      _VERSION_REG.match(snake_case_      )
 if not res:
  raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
 return tuple(int(snake_case_      ) for v in [res.group('''major'''      ), res.group('''minor'''      ), res.group('''patch'''      )]      )







def 			A__      (	snake_case_ :		Union[str, Any]      ):
 return ".".join(str(snake_case_      ) for v in version_tuple      )

