


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			(DPMSolverSinglestepScheduler,)
 SCREAMING_SNAKE_CASE__ =			(('num_inference_steps', 25),)




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       a		:Tuple									=			{
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
           '''sample_max_value''': 1.0,
           '''algorithm_type''': '''dpmsolver++''',
           '''solver_type''': '''midpoint''',
           '''lambda_min_clipped''': -float('''inf'''       ),
           '''variance_type''': None,
       }

       config.update(**_lowerCamelCase       )
       return config




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=0   ,    **_lowerCamelCase       ):
       a		:Any									=			dict(self.forward_default_kwargs       )
       a		:Dict									=			kwargs.pop('''num_inference_steps'''   ,    _lowerCamelCase       )
       a		:str									=			self.dummy_sample
       a		:str									=			0.1 * sample
       a		:Union[str, Any]									=			[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             a		:str									=			self.get_scheduler_config(**_lowerCamelCase       )
             a		:int									=			scheduler_class(**_lowerCamelCase       )
             scheduler.set_timesteps(_lowerCamelCase       )
             # copy over dummy past residuals
             a		:Optional[Any]									=			dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase       )
                   a		:Dict									=			scheduler_class.from_pretrained(_lowerCamelCase       )
                   new_scheduler.set_timesteps(_lowerCamelCase       )
                   # copy over dummy past residuals
                   a		:Any									=			dummy_past_residuals[: new_scheduler.config.solver_order]

             a				,							a		:str									=			sample, sample
             for t in range(_lowerCamelCase   ,    time_step + scheduler.config.solver_order + 1       ):
                   a		:Any									=			scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       ).prev_sample
                   a		:Union[str, Any]									=			new_scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       ).prev_sample

                   assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=0   ,    **_lowerCamelCase       ):
       a		:Any									=			dict(self.forward_default_kwargs       )
       a		:Dict									=			kwargs.pop('''num_inference_steps'''   ,    _lowerCamelCase       )
       a		:Optional[Any]									=			self.dummy_sample
       a		:Tuple									=			0.1 * sample
       a		:Dict									=			[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             a		:Any									=			self.get_scheduler_config()
             a		:List[str]									=			scheduler_class(**_lowerCamelCase       )
             scheduler.set_timesteps(_lowerCamelCase       )

             # copy over dummy past residuals (must be after setting timesteps)
             a		:Dict									=			dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase       )
                   a		:List[Any]									=			scheduler_class.from_pretrained(_lowerCamelCase       )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_lowerCamelCase       )

                   # copy over dummy past residual (must be after setting timesteps)
                   a		:str									=			dummy_past_residuals[: new_scheduler.config.solver_order]

             a		:Union[str, Any]									=			scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       ).prev_sample
             a		:Dict									=			new_scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       ).prev_sample

             assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=None   ,    **_lowerCamelCase       ):
       if scheduler is None:
             a		:Any									=			self.scheduler_classes[0]
             a		:Tuple									=			self.get_scheduler_config(**_lowerCamelCase       )
             a		:Optional[Any]									=			scheduler_class(**_lowerCamelCase       )

       a		:int									=			self.scheduler_classes[0]
       a		:Optional[Any]									=			self.get_scheduler_config(**_lowerCamelCase       )
       a		:Union[str, Any]									=			scheduler_class(**_lowerCamelCase       )

       a		:int									=			10
       a		:Dict									=			self.dummy_model()
       a		:Optional[int]									=			self.dummy_sample_deter
       scheduler.set_timesteps(_lowerCamelCase       )

       for i, t in enumerate(scheduler.timesteps       ):
             a		:Dict									=			model(_lowerCamelCase   ,    _lowerCamelCase       )
             a		:List[Any]									=			scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ).prev_sample

       return sample




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
       a		:Optional[int]									=			50
       a		:List[str]									=			self.dummy_model()
       a		:Any									=			self.dummy_sample_deter
       scheduler.set_timesteps(_lowerCamelCase       )

       # make sure that the first t is uneven
       for i, t in enumerate(scheduler.timesteps[3:]       ):
             a		:List[str]									=			model(_lowerCamelCase   ,    _lowerCamelCase       )
             a		:str									=			scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ).prev_sample

       a		:Optional[int]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.2574       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for timesteps in [25, 50, 100, 999, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       a		:Any									=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
       a		:str									=			self.full_loop(scheduler=_lowerCamelCase       )
       a		:List[str]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.2791       ) < 1e-3

       a		:str									=			DEISMultistepScheduler.from_config(scheduler.config       )
       a		:str									=			DPMSolverMultistepScheduler.from_config(scheduler.config       )
       a		:Any									=			UniPCMultistepScheduler.from_config(scheduler.config       )
       a		:Any									=			DPMSolverSinglestepScheduler.from_config(scheduler.config       )

       a		:int									=			self.full_loop(scheduler=_lowerCamelCase       )
       a		:List[Any]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.2791       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.check_over_configs(thresholding=_lowerCamelCase       )
       for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   thresholding=_lowerCamelCase   ,    prediction_type=_lowerCamelCase   ,    sample_max_value=_lowerCamelCase   ,    algorithm_type='''dpmsolver++'''   ,    solver_order=_lowerCamelCase   ,    solver_type=_lowerCamelCase   ,    )




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   solver_order=_lowerCamelCase   ,    solver_type=_lowerCamelCase   ,    prediction_type=_lowerCamelCase   ,    algorithm_type=_lowerCamelCase   ,    )
                               a		:List[Any]									=			self.full_loop(
                                   solver_order=_lowerCamelCase   ,    solver_type=_lowerCamelCase   ,    prediction_type=_lowerCamelCase   ,    algorithm_type=_lowerCamelCase   ,    )
                               assert not torch.isnan(_lowerCamelCase       ).any(), "Samples have nan numbers"




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.check_over_configs(lower_order_final=_lowerCamelCase       )
       self.check_over_configs(lower_order_final=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.check_over_configs(lambda_min_clipped=-float('''inf'''       )       )
       self.check_over_configs(lambda_min_clipped=-5.1       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.check_over_configs(variance_type=_lowerCamelCase       )
       self.check_over_configs(variance_type='''learned_range'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
             self.check_over_forward(num_inference_steps=_lowerCamelCase   ,    time_step=0       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.full_loop()
       a		:List[Any]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.2791       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.full_loop(use_karras_sigmas=_lowerCamelCase       )
       a		:List[str]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.2248       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.full_loop(prediction_type='''v_prediction'''       )
       a		:Union[str, Any]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.1453       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.full_loop(prediction_type='''v_prediction'''   ,    use_karras_sigmas=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.mean(torch.abs(_lowerCamelCase       )       )

       assert abs(result_mean.item() - 0.0649       ) < 1e-3




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.scheduler_classes[0]
       a		:Union[str, Any]									=			self.get_scheduler_config(thresholding=_lowerCamelCase   ,    dynamic_thresholding_ratio=0       )
       a		:Dict									=			scheduler_class(**_lowerCamelCase       )

       a		:Tuple									=			10
       a		:int									=			self.dummy_model()
       a		:List[str]									=			self.dummy_sample_deter.half()
       scheduler.set_timesteps(_lowerCamelCase       )

       for i, t in enumerate(scheduler.timesteps       ):
             a		:str									=			model(_lowerCamelCase   ,    _lowerCamelCase       )
             a		:int									=			scheduler.step(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ).prev_sample

       assert sample.dtype == torch.floataa









from __future__ import annotations

from typing import Any







class 						_snake_case    ( _snake_case				):
 pass







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Any									=			data
       a		:Node | None									=			None




 def __iter__(						self       ):
       a		:Dict									=			self
       a		:Optional[int]									=			[]
       while node:
             if node in visited:
                   raise ContainsLoopError
             visited.append(_lowerCamelCase       )
             yield node.data
             a		:str									=			node.next_node




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       try:
             list(self       )
             return False
       except ContainsLoopError:
             return True


if __name__ == "__main__":
     snake_case							:       int										=	Node(1)
     snake_case							:       int										=	Node(2)
     snake_case							:       Optional[int]										=	Node(3)
     snake_case							:       List[str]										=	Node(4)
     print(root_node.has_loop)  # False
     snake_case							:       Optional[Any]										=	root_node.next_node
     print(root_node.has_loop)  # True

     snake_case							:       Dict										=	Node(5)
     snake_case							:       List[Any]										=	Node(6)
     snake_case							:       Optional[int]										=	Node(5)
     snake_case							:       Union[str, Any]										=	Node(6)
     print(root_node.has_loop)  # False

     snake_case							:       Tuple										=	Node(1)
     print(root_node.has_loop)  # False





