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    ):
						lowercase					 =     (DPMSolverSDEScheduler,)
						lowercase					 =     10

						def        __a		(	self				:      Tuple							,							**__lowerCamelCase				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		{
									    '''num_train_timesteps''': 11_00,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''noise_sampler_seed''': 0,
									}

									config.update(**__lowerCAmelCase   )
									return config

						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCAmelCase   )

						def        __a		(	self				:      int   )							->		Optional[int]:


									'''simple docstring'''

									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,							[0.0002, 0.002, 0.02]   ):
												self.check_over_configs(beta_start=__lowerCAmelCase							,							beta_end=__lowerCAmelCase   )

						def        __a		(	self				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

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

						def        __a		(	self				:      Any   )							->		Dict:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCAmelCase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCAmelCase   )

									for i, t in enumerate(scheduler.timesteps   ):
												lowercase							=		scheduler.scale_model_input(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		model(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCAmelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCAmelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875   ) < 1E-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406   ) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652   ) < 1E-3
									else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3

						def        __a		(	self				:      Optional[int]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config(prediction_type='''v_prediction'''   )
									lowercase							=		scheduler_class(**__lowerCAmelCase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCAmelCase   )

									for i, t in enumerate(scheduler.timesteps   ):
												lowercase							=		scheduler.scale_model_input(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		model(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCAmelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCAmelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453   ) < 1E-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703   ) < 1E-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297   ) < 1E-3
									else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125   ) < 1E-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621   ) < 1E-3

						def        __a		(	self				:      Tuple   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCAmelCase   )

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

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter.to(__lowerCAmelCase   ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												lowercase							=		scheduler.scale_model_input(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		model(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCAmelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCAmelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938   ) < 1E-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312   ) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771   ) < 1E-3
									else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3



						def        __a		(	self				:      Tuple   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCAmelCase							,							use_karras_sigmas=__lowerCAmelCase   )

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

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter.to(__lowerCAmelCase   ) * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCAmelCase   )

									for t in scheduler.timesteps:
												lowercase							=		scheduler.scale_model_input(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		model(__lowerCAmelCase							,							__lowerCAmelCase   )

												lowercase							=		scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCAmelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCAmelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
									else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2






from __future__ import annotations

from collections.abc import MutableSequence






class      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      MutableSequence[float]   )							->		None:


									'''simple docstring'''

									if len(__lowerCamelCase   ) != degree + 1:
												raise ValueError(
												    '''The number of coefficients should be equal to the degree + 1.'''   )

									lowercase							=		list(__lowerCamelCase   )
									lowercase							=		degree

						def __add__(	self				:      Any							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									if self.degree > polynomial_a.degree:
												lowercase							=		self.coefficients[:]
												for i in range(polynomial_a.degree + 1   ):
															coefficients[i] += polynomial_a.coefficients[i]
												return Polynomial(self.degree							,							__lowerCamelCase   )
									else:
												lowercase							=		polynomial_a.coefficients[:]
												for i in range(self.degree + 1   ):
															coefficients[i] += self.coefficients[i]
												return Polynomial(polynomial_a.degree							,							__lowerCamelCase   )

						def __sub__(	self				:      str							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									return self + polynomial_a * Polynomial(0							,							[-1]   )

						def __neg__(	self				:      str   )							->		Polynomial:


									'''simple docstring'''

									return Polynomial(self.degree							,							[-c for c in self.coefficients]   )

						def __mul__(	self				:      List[str]							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + polynomial_a.degree + 1)
									for i in range(self.degree + 1   ):
												for j in range(polynomial_a.degree + 1   ):
															coefficients[i + j] += (
															    self.coefficients[i] * polynomial_a.coefficients[j]
															)

									return Polynomial(self.degree + polynomial_a.degree							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      int | float   )							->		int | float:


									'''simple docstring'''

									lowercase							=		0
									for i in range(self.degree + 1   ):
												result += self.coefficients[i] * (substitution**i)
									return result

						def __str__(	self				:      str   )							->		str:


									'''simple docstring'''

									lowercase							=		''''''
									for i in range(self.degree							,							-1							,							-1   ):
												if self.coefficients[i] == 0:
															continue
												elif self.coefficients[i] > 0:
															if polynomial:
																		polynomial += " + "
												else:
															polynomial += " - "

												if i == 0:
															polynomial += str(abs(self.coefficients[i]   )   )
												elif i == 1:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x"
												else:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x^" + str(__lowerCamelCase   )

									return polynomial

						def __repr__(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									return self.__str__()

						def        __a		(	self				:      Union[str, Any]   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * self.degree
									for i in range(self.degree   ):
												lowercase							=		self.coefficients[i + 1] * (i + 1)
									return Polynomial(self.degree - 1							,							__lowerCamelCase   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int | float = 0   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + 2)
									lowercase							=		constant
									for i in range(self.degree + 1   ):
												lowercase							=		self.coefficients[i] / (i + 1)
									return Polynomial(self.degree + 1							,							__lowerCamelCase   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return False

									if self.degree != polynomial_a.degree:
												return False

									for i in range(self.degree + 1   ):
												if self.coefficients[i] != polynomial_a.coefficients[i]:
															return False

									return True



						def __ne__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									return not self.__eq__(__lowerCamelCase   )





