



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__	(						_snake_case       ):




    '''simple docstring'''






    __lowerCamelCase     :					List[Any]   = (DPMSolverSinglestepScheduler,)
    __lowerCamelCase     :					List[str]   = (("num_inference_steps", 25),)






    def      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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					:			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      _lowerCAmelCase   (			self						):
        pass






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






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






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






    def      _lowerCAmelCase   (			self						):
        self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
        self.check_over_configs(lambda_min_clipped=-5.1						)






    def      _lowerCAmelCase   (			self						):
        self.check_over_configs(variance_type=_lowerCamelCase						)
        self.check_over_configs(variance_type="""learned_range"""						)






    def      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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      _lowerCAmelCase   (			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





