



"""simple docstring"""
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_lowerCAmelCase       :List[str]	=   TypeVar('T')
_lowerCAmelCase       :str	=   TypeVar('U')
class 				_UpperCAmelCase				(					Generic[T, U] ):





     '''simple docstring'''



     def __init__(   self			,      A			,      A						)				->   str:
         _UpperCAmelCase				:    Optional[int]											=       key
         _UpperCAmelCase				:    str											=       val
         _UpperCAmelCase				:    DoubleLinkedListNode[T, U] | None											=       None
         _UpperCAmelCase				:    DoubleLinkedListNode[T, U] | None											=       None





     def __repr__(   self						)				->   str:
         return (
             f'Node: key: {self.key}, val: {self.val}, '
             f'has next: {bool(self.next						)}, has prev: {bool(self.prev						)}'
         )



class 				_UpperCAmelCase				(					Generic[T, U] ):





     '''simple docstring'''



     def __init__(   self						)				->   None:
         _UpperCAmelCase				:    DoubleLinkedListNode[T, U]											=       DoubleLinkedListNode(__A			,      __A						)
         _UpperCAmelCase				:    DoubleLinkedListNode[T, U]											=       DoubleLinkedListNode(__A			,      __A						)
         _UpperCAmelCase				:    str											=       self.rear, self.head





     def __repr__(   self						)				->   str:
         _UpperCAmelCase				:    Optional[int]											=       ['''DoubleLinkedList''']
         _UpperCAmelCase				:    List[str]											=       self.head
         while node.next is not None:
             rep.append(str(__A						)						)
             _UpperCAmelCase				:    Dict											=       node.next
         rep.append(str(self.rear						)						)
         return ",\n    ".join(__A						)





     def 					__lowerCAmelCase      (   self			,      A						)				->   None:
         _UpperCAmelCase				:    Dict											=       self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         _UpperCAmelCase				:    Any											=       node
         _UpperCAmelCase				:    Optional[int]											=       previous
         _UpperCAmelCase				:    Optional[Any]											=       node
         _UpperCAmelCase				:    Any											=       self.rear





     def 					__lowerCAmelCase      (   self			,      A						)				->   DoubleLinkedListNode[T, U] | None:

         if node.prev is None or node.next is None:
             return None

         _UpperCAmelCase				:    Tuple											=       node.next
         _UpperCAmelCase				:    Optional[int]											=       node.prev
         _UpperCAmelCase				:    int											=       None
         _UpperCAmelCase				:    Dict											=       None
         return node



class 				_UpperCAmelCase				(					Generic[T, U] ):





     '''simple docstring'''



     a__   							={}





     def __init__(   self			,      A						)				->   Union[str, Any]:
         _UpperCAmelCase				:    DoubleLinkedList[T, U]											=       DoubleLinkedList()
         _UpperCAmelCase				:    Tuple											=       capacity
         _UpperCAmelCase				:    Optional[int]											=       0
         _UpperCAmelCase				:    int											=       0
         _UpperCAmelCase				:    Optional[int]											=       0
         _UpperCAmelCase				:    dict[T, DoubleLinkedListNode[T, U]]											=       {}





     def __repr__(   self						)				->   str:
         return (
             f'CacheInfo(hits={self.hits}, misses={self.miss}, '
             f'capacity={self.capacity}, current size={self.num_keys})'
         )





     def __contains__(   self			,      A						)				->   bool:
         return key in self.cache





     def 					__lowerCAmelCase      (   self			,      A						)				->   U | None:
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
             self.hits += 1
             _UpperCAmelCase				:    DoubleLinkedListNode[T, U]											=       self.cache[key]
             _UpperCAmelCase				:    Optional[int]											=       self.list.remove(self.cache[key]						)
             assert node == value_node

             # node is guaranteed not None because it is in self.cache
             assert node is not None
             self.list.add(__A						)
             return node.val
         self.miss += 1
         return None





     def 					__lowerCAmelCase      (   self			,      A			,      A						)				->   None:

         if key not in self.cache:
             if self.num_keys >= self.capacity:
                 # delete first node (oldest) when over capacity
                 _UpperCAmelCase				:    List[str]											=       self.list.head.next

                 # guaranteed to have a non-None first node when num_keys > 0
                 # explain to type checker via assertions
                 assert first_node is not None
                 assert first_node.key is not None
                 assert (
                     self.list.remove(__A						) is not None
                 )  # node guaranteed to be in list assert node.key is not None

                 del self.cache[first_node.key]
                 self.num_keys -= 1
             _UpperCAmelCase				:    Union[str, Any]											=       DoubleLinkedListNode(__A			,      __A						)
             self.list.add(self.cache[key]						)
             self.num_keys += 1

         else:
             # bump node to the end of the list, update value
             _UpperCAmelCase				:    str											=       self.list.remove(self.cache[key]						)
             assert node is not None  # node guaranteed to be in list
             _UpperCAmelCase				:    List[str]											=       value
             self.list.add(__A						)





     @classmethod
     def 					__lowerCAmelCase      (   cls			,      A = 1_2_8						)				->   Callable[[Callable[[T], U]], Callable[..., U]]:

         def cache_decorator_inner(A						) -> Callable[..., U]:
             def cache_decorator_wrapper(*A						) -> U:
                 if func not in cls.decorator_function_to_instance_map:
                     _UpperCAmelCase				:    int											=       LRUCache(__A						)

                 _UpperCAmelCase				:    Optional[int]											=       cls.decorator_function_to_instance_map[func].get(args[0]						)
                 if result is None:
                     _UpperCAmelCase				:    str											=       func(*__A						)
                     cls.decorator_function_to_instance_map[func].put(args[0]			,      __A						)
                 return result

             def cache_info() -> LRUCache[T, U]:
                 return cls.decorator_function_to_instance_map[func]

             setattr(__A			,      '''cache_info'''			,      __A						)  # noqa: B010

             return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


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


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 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"]:
                     self.check_over_configs(beta_schedule=__A				)


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


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

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

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

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


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

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

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

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

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





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

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

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
