




from __future__ import annotations

from typing import Generic, TypeVar

_snake_case										=       TypeVar("T")







class 						lowercase							(   Generic[T]		):

     def __init__(  self		,     _a				)						->							None:
           _A							:     List[str]			=						data
           _A							:     int			=						self
           _A							:     Any			=						0







class 						lowercase							(   Generic[T]		):

     def __init__(  self				)						->							None:
           # map from node name to the node object
           _A							:     dict[T, DisjointSetTreeNode[T]]			=						{}

     def 					a__	(  self		,     _a				)						->							None:
           # create a new set with x as its member
           _A							:     Union[str, Any]			=						DisjointSetTreeNode(_a				)

     def 					a__	(  self		,     _a				)						->							DisjointSetTreeNode[T]:
           # find the set x belongs to (with path-compression)
           _A							:     Any			=						self.map[data]
           if elem_ref != elem_ref.parent:
                 _A							:     Optional[int]			=						self.find_set(elem_ref.parent.data				)
           return elem_ref.parent

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           # helper function for union operation
           if nodea.rank > nodea.rank:
                 _A							:     List[str]			=						nodea
           else:
                 _A							:     str			=						nodea
                 if nodea.rank == nodea.rank:
                       nodea.rank += 1



     def 					a__	(  self		,     _a		,     _a				)						->							None:
           # merge 2 disjoint sets
           self.link(self.find_set(_a				)		,     self.find_set(_a				)				)







class 						lowercase							(   Generic[T]		):

     def __init__(  self				)						->							None:
           # connections: map from the node to the neighbouring nodes (with weights)
           _A							:     dict[T, dict[T, int]]			=						{}

     def 					a__	(  self		,     _a				)						->							None:
           # add a node ONLY if its not present in the graph
           if node not in self.connections:
                 _A							:     Dict			=						{}

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							None:
           # add an edge with the given weight
           self.add_node(_a				)
           self.add_node(_a				)
           _A							:     Optional[int]			=						weight
           _A							:     Tuple			=						weight



     def 					a__	(  self				)						->							GraphUndirectedWeighted[T]:
           _A							:     Dict			=						[]
           _A							:     Dict			=						set()
           for start in self.connections:
                 for end in self.connections[start]:
                       if (start, end) not in seen:
                             seen.add((end, start)				)
                             edges.append((start, end, self.connections[start][end])				)
           edges.sort(key=lambda _a				: x[2]				)

           # creating the disjoint set
           _A							:     Any			=						DisjointSetTree[T]()
           for node in self.connections:
                 disjoint_set.make_set(_a				)

           # MST generation
           _A							:     Optional[int]			=						0
           _A							:     List[Any]			=						0
           _A							:     Dict			=						GraphUndirectedWeighted[T]()
           while num_edges < len(self.connections				) - 1:
                 _A			,   _A			,   _A							:     Tuple			=						edges[index]
                 index += 1
                 _A							:     Optional[Any]			=						disjoint_set.find_set(_a				)
                 _A							:     List[str]			=						disjoint_set.find_set(_a				)
                 if parent_u != parent_v:
                       num_edges += 1
                       graph.add_edge(_a		,     _a		,     _a				)
                       disjoint_set.union(_a		,     _a				)
           return graph







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							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     str			=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """noise_sampler_seed""": 0,
           }

           config.update(**_a				)
           return config

     def 					a__	(  self				)						->							Tuple:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,     [0.0002, 0.002, 0.02]				):
                 self.check_over_configs(beta_start=_a		,     beta_end=_a				)

     def 					a__	(  self				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

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

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

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

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

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

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

           for t in scheduler.timesteps:
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

