





"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(EulerDiscreteScheduler,)
					__lowerCAmelCase					=					10







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					{
									    '''num_train_timesteps''': 1100,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**_UpperCAmelCase       )
									return config







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







					def 	_lowerCamelCase						(  self       ):
									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,     [0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
													self.check_over_configs(beta_start=_UpperCAmelCase		,     beta_end=_UpperCAmelCase       )







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







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.scheduler_classes[0]
									__a	:  str         =					self.get_scheduler_config()
									__a	:  int         =					scheduler_class(**_UpperCAmelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									__a	:  int         =					torch.manual_seed(0       )

									__a	:  Any         =					self.dummy_model()
									__a	:  Optional[int]         =					self.dummy_sample_deter * scheduler.init_noise_sigma
									__a	:  List[str]         =					sample.to(_UpperCAmelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
													__a	:  Dict         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  Optional[int]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  Optional[Any]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
													__a	:  Optional[Any]         =					output.prev_sample

									__a	:  int         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  List[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1e-2
									assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.scheduler_classes[0]
									__a	:  Union[str, Any]         =					self.get_scheduler_config(prediction_type='''v_prediction'''       )
									__a	:  List[str]         =					scheduler_class(**_UpperCAmelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									__a	:  Tuple         =					torch.manual_seed(0       )

									__a	:  str         =					self.dummy_model()
									__a	:  Dict         =					self.dummy_sample_deter * scheduler.init_noise_sigma
									__a	:  Any         =					sample.to(_UpperCAmelCase       )

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

													__a	:  Any         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  List[str]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
													__a	:  int         =					output.prev_sample

									__a	:  Tuple         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  Optional[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 0.0_0_0_2       ) < 1e-2
									assert abs(result_mean.item() - 2.2_6_7_6e-0_6       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.scheduler_classes[0]
									__a	:  List[str]         =					self.get_scheduler_config()
									__a	:  Union[str, Any]         =					scheduler_class(**_UpperCAmelCase       )

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

									__a	:  Dict         =					torch.manual_seed(0       )

									__a	:  Dict         =					self.dummy_model()
									__a	:  str         =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__a	:  Any         =					sample.to(_UpperCAmelCase       )

									for t in scheduler.timesteps:
													__a	:  Tuple         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  str         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  Optional[int]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
													__a	:  Any         =					output.prev_sample

									__a	:  List[str]         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  Any         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 1_0.0_8_0_7       ) < 1e-2
									assert abs(result_mean.item() - 0.0_1_3_1       ) < 1e-3







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.scheduler_classes[0]
									__a	:  Optional[int]         =					self.get_scheduler_config()
									__a	:  List[Any]         =					scheduler_class(**_UpperCAmelCase		,     use_karras_sigmas=_UpperCAmelCase       )

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

									__a	:  Tuple         =					torch.manual_seed(0       )

									__a	:  Optional[int]         =					self.dummy_model()
									__a	:  Any         =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__a	:  List[Any]         =					sample.to(_UpperCAmelCase       )

									for t in scheduler.timesteps:
													__a	:  List[str]         =					scheduler.scale_model_input(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  Optional[Any]         =					model(_UpperCAmelCase		,     _UpperCAmelCase       )

													__a	:  Union[str, Any]         =					scheduler.step(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     generator=_UpperCAmelCase       )
													__a	:  Optional[int]         =					output.prev_sample

									__a	:  Any         =					torch.sum(torch.abs(_UpperCAmelCase       )       )
									__a	:  List[Any]         =					torch.mean(torch.abs(_UpperCAmelCase       )       )

									assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9       ) < 1e-2
									assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1e-3






"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A	     =					TypeVar('''T''')




class 			__lowercase     (     Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase					=					42  # Cache store of keys
					__lowerCAmelCase					=					42  # References of the keys in cache
					__lowerCAmelCase					=					10  # Maximum capacity of cache







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					deque()
									__a	:  Dict         =					set()
									if not n:
													__a	:  List[Any]         =					sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''       )
									else:
													__a	:  str         =					n







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if x not in self.key_reference:
													if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																	__a	:  int         =					self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase       )
									else:
													self.dq_store.remove(_UpperCAmelCase       )

									self.dq_store.appendleft(_UpperCAmelCase       )
									self.key_reference.add(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for k in self.dq_store:
													print(_UpperCAmelCase       )







					def __repr__(  self       ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A	     =					LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"