






"""simple docstring"""


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Tuple			=     (KDPMaAncestralDiscreteScheduler,)
					lowercase_						:							Optional[int]			=     10
					def 					UpperCAmelCase__   (       self       :  int    ,   **UpperCAmelCase       :  Dict      )  ->					List[Any]:
											lowerCAmelCase      :Union[str, 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 					UpperCAmelCase__   (       self       :  Tuple      )  ->					List[Any]:
											for timesteps in [10, 50, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					List[str]:
											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 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					List[str]:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :Any				  = self.scheduler_classes[0]
											lowerCAmelCase      :Optional[Any]				  = self.get_scheduler_config()
											lowerCAmelCase      :int				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps      )

											lowerCAmelCase      :int				  = torch.manual_seed(0      )

											lowerCAmelCase      :Tuple				  = self.dummy_model()
											lowerCAmelCase      :Optional[Any]				  = self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase      :List[str]				  = sample.to(UpperCAmelCase      )

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

																	lowerCAmelCase      :int				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :List[Any]				  = scheduler.step(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   generator=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = output.prev_sample

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

											assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7      ) < 1e-2
											assert abs(result_mean.item() - 1_8.0_3_3_1      ) < 5e-3
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Optional[Any]:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :List[Any]				  = self.scheduler_classes[0]
											lowerCAmelCase      :Any				  = self.get_scheduler_config(prediction_type='v_prediction'      )
											lowerCAmelCase      :Tuple				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps      )

											lowerCAmelCase      :str				  = self.dummy_model()
											lowerCAmelCase      :int				  = self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase      :Any				  = sample.to(UpperCAmelCase      )

											lowerCAmelCase      :List[Any]				  = torch.manual_seed(0      )

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

																	lowerCAmelCase      :Optional[Any]				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

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

											lowerCAmelCase      :Optional[Any]				  = torch.sum(torch.abs(UpperCAmelCase      )      )
											lowerCAmelCase      :List[Any]				  = torch.mean(torch.abs(UpperCAmelCase      )      )

											assert abs(result_sum.item() - 3_2_8.9_9_7_0      ) < 1e-2
											assert abs(result_mean.item() - 0.4_2_8_4      ) < 1e-3
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											if torch_device == "mps":
																	return
											lowerCAmelCase      :Optional[int]				  = self.scheduler_classes[0]
											lowerCAmelCase      :Optional[Any]				  = self.get_scheduler_config()
											lowerCAmelCase      :Any				  = scheduler_class(**UpperCAmelCase      )

											scheduler.set_timesteps(self.num_inference_steps    ,   device=UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = torch.manual_seed(0      )

											lowerCAmelCase      :Optional[Any]				  = self.dummy_model()
											lowerCAmelCase      :Tuple				  = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																	lowerCAmelCase      :Tuple				  = scheduler.scale_model_input(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :Optional[Any]				  = model(UpperCAmelCase    ,   UpperCAmelCase      )

																	lowerCAmelCase      :List[Any]				  = scheduler.step(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   generator=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = output.prev_sample

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

											assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8      ) < 1e-1
											assert abs(result_mean.item() - 1_8.0_3_3_1      ) < 1e-3







"""simple docstring"""


from .number_theory.prime_numbers import next_prime




class 	__UpperCamelCase    :
					def __init__(       self       :  List[str]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  int | None = None    ,   UpperCAmelCase       :  float | None = None    ,   )  ->					None:
											lowerCAmelCase      :Tuple				  = size_table
											lowerCAmelCase      :List[Any]				  = [None] * self.size_table
											lowerCAmelCase      :Tuple				  = 0.7_5 if lim_charge is None else lim_charge
											lowerCAmelCase      :str				  = 1 if charge_factor is None else charge_factor
											lowerCAmelCase      :list				  = []
											lowerCAmelCase      :dict				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Union[str, Any]:
											return self._keys
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Optional[Any]:
											return sum(1 for slot in self.values if slot is not None      ) / (
											    self.size_table * self.charge_factor
											)
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  List[str]      )  ->					int:
											return key % self.size_table
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					int:
											print(f"""step {step_ord}"""      )
											print(list(range(len(self.values      )      )      )      )
											print(self.values      )
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  Dict      )  ->					Any:
											lowerCAmelCase      :List[str]				  = 1
											lowerCAmelCase      :Union[str, Any]				  = values
											for value in values:
																	self.insert_data(UpperCAmelCase      )
																	self._step_by_step(UpperCAmelCase      )
																	i += 1
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[Any]				  = data
											lowerCAmelCase      :str				  = data
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str=None      )  ->					Tuple:
											lowerCAmelCase      :Optional[Any]				  = self.hash_function(key + 1      )

											while self.values[new_key] is not None and self.values[new_key] != key:
																	if self.values.count(UpperCAmelCase      ) > 0:
																							lowerCAmelCase      :Tuple				  = self.hash_function(new_key + 1      )
																	else:
																							lowerCAmelCase      :Union[str, Any]				  = None
																							break

											return new_key
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Any:
											lowerCAmelCase      :Optional[Any]				  = [value for value in self.values if value is not None]
											lowerCAmelCase      :List[str]				  = next_prime(self.size_table    ,   factor=2      )
											self._keys.clear()
											lowerCAmelCase      :int				  = [None] * self.size_table  # hell's pointers D: don't DRY ;/
											for value in survivor_values:
																	self.insert_data(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  Tuple      )  ->					int:
											lowerCAmelCase      :Union[str, Any]				  = self.hash_function(UpperCAmelCase      )

											if self.values[key] is None:
																	self._set_value(UpperCAmelCase    ,   UpperCAmelCase      )

											elif self.values[key] == data:
																	pass

											else:
																	lowerCAmelCase      :Tuple				  = self._collision_resolution(UpperCAmelCase    ,   UpperCAmelCase      )
																	if collision_resolution is not None:
																							self._set_value(UpperCAmelCase    ,   UpperCAmelCase      )
																	else:
																							self.rehashing()
																							self.insert_data(UpperCAmelCase      )