





from abc import ABC, abstractmethod
from typing import List, Optional

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				List[Any]				):





         """simple docstring"""



         self.test()


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       0
         __snake_case								=       False
         while not completed:
            if counter == 1:
               self.reset()
            __snake_case								=       self.advance()
            if not self.does_advance(a__				):
               raise Exception(
                   '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''				)

            __snake_case	,     __snake_case	,     __snake_case								=       self.update(a__				)
            counter += 1

            if counter > 1_0000:
               raise Exception('''update() does not fulfill the constraint.'''				)

         if self.remaining() != 0:
            raise Exception('''Custom Constraint is not defined correctly.'''				)


      @abstractmethod
      def        a			(self	:				Tuple				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)


      @abstractmethod
      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)


      @abstractmethod
      def        a			(self	:				List[str]    ,      a__	:				int				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)


      @abstractmethod
      def        a			(self	:				int				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)


      @abstractmethod
      def        a			(self	:				List[str]				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)


      @abstractmethod
      def        a			(self	:				str    ,      a__	:				List[Any]=False				):





         """simple docstring"""



         raise NotImplementedError(
             f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""				)






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				Union[str, Any]    ,      a__	:				List[int]				):





         """simple docstring"""



         super(a__    ,      self				).__init__()

         if not isinstance(a__    ,      a__				) or len(a__				) == 0:
            raise ValueError(f"""`token_ids` has to be a non-empty list, but is {token_ids}."""				)
         if any((not isinstance(a__    ,      a__				) or token_id < 0) for token_id in token_ids				):
            raise ValueError(f"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."""				)

         __snake_case								=       token_ids

         __snake_case								=       len(self.token_ids				)
         __snake_case								=       -1  # the index of the currently fulfilled step
         __snake_case								=       False


      def        a			(self	:				Dict				):





         """simple docstring"""



         if self.completed:
            return None
         return self.token_ids[self.fulfilled_idx + 1]


      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         if not isinstance(a__    ,      a__				):
            raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(a__				)}"""				)

         if self.completed:
            return False

         return token_id == self.token_ids[self.fulfilled_idx + 1]


      def        a			(self	:				Tuple    ,      a__	:				int				):





         """simple docstring"""



         if not isinstance(a__    ,      a__				):
            raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(a__				)}"""				)

         __snake_case								=       False
         __snake_case								=       False
         __snake_case								=       False

         if self.does_advance(a__				):
            self.fulfilled_idx += 1
            __snake_case								=       True
            if self.fulfilled_idx == (self.seqlen - 1):
               __snake_case								=       True
            __snake_case								=       completed
         else:
            # failed to make progress.
            __snake_case								=       True
            self.reset()
         return stepped, completed, reset


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       False
         __snake_case								=       0


      def        a			(self	:				Dict				):





         """simple docstring"""



         return self.seqlen - (self.fulfilled_idx + 1)


      def        a			(self	:				str    ,      a__	:				Optional[Any]=False				):





         """simple docstring"""



         __snake_case								=       PhrasalConstraint(self.token_ids				)

         if stateful:
            __snake_case								=       self.seqlen
            __snake_case								=       self.fulfilled_idx
            __snake_case								=       self.completed

         return new_constraint






class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				int    ,      a__	:				List[List[int]]    ,      a__	:				Optional[int]=True				):





         """simple docstring"""



         __snake_case								=       max([len(a__				) for one in nested_token_ids]				)

         __snake_case								=       {}
         for token_ids in nested_token_ids:
            __snake_case								=       root
            for tidx, token_id in enumerate(a__				):
               if token_id not in level:
                  __snake_case								=       {}

               __snake_case								=       level[token_id]

         if no_subsets and self.has_subsets(a__    ,      a__				):
            raise ValueError(
                '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
                f""" {nested_token_ids}."""				)

         __snake_case								=       root


      def        a			(self	:				str    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.trie

         for current_token in current_seq:
            __snake_case								=       start[current_token]

         __snake_case								=       list(start.keys()				)

         return next_tokens


      def        a			(self	:				Tuple    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       self.next_tokens(a__				)

         return len(a__				) == 0


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       list(root.values()				)
         if len(a__				) == 0:
            return 1
         else:
            return sum([self.count_leaves(a__				) for nn in next_nodes]				)


      def        a			(self	:				List[Any]    ,      a__	:				int    ,      a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.count_leaves(a__				)
         return len(a__				) != leaf_count






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      a__	:				List[List[int]]				):





         """simple docstring"""



         super(a__    ,      self				).__init__()

         if not isinstance(a__    ,      a__				) or len(a__				) == 0:
            raise ValueError(f"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."""				)
         if any(not isinstance(a__    ,      a__				) for token_ids in nested_token_ids				):
            raise ValueError(f"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."""				)
         if any(
             any((not isinstance(a__    ,      a__				) or token_id < 0) for token_id in token_ids				)
             for token_ids in nested_token_ids				):
            raise ValueError(
                f"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."""				)

         __snake_case								=       DisjunctiveTrie(a__				)
         __snake_case								=       nested_token_ids

         __snake_case								=       self.trie.max_height
         __snake_case								=       []
         __snake_case								=       False


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.trie.next_tokens(self.current_seq				)

         if len(a__				) == 0:
            return None
         else:
            return token_list


      def        a			(self	:				List[str]    ,      a__	:				int				):





         """simple docstring"""



         if not isinstance(a__    ,      a__				):
            raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(a__				)}"""				)

         __snake_case								=       self.trie.next_tokens(self.current_seq				)

         return token_id in next_tokens


      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         if not isinstance(a__    ,      a__				):
            raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(a__				)}"""				)

         __snake_case								=       False
         __snake_case								=       False
         __snake_case								=       False

         if self.does_advance(a__				):
            self.current_seq.append(a__				)
            __snake_case								=       True
         else:
            __snake_case								=       True
            self.reset()

         __snake_case								=       self.trie.reached_leaf(self.current_seq				)
         __snake_case								=       completed

         return stepped, completed, reset


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       False
         __snake_case								=       []


      def        a			(self	:				str				):





         """simple docstring"""



         if self.completed:
            # since this can be completed without reaching max height
            return 0
         else:
            return self.seqlen - len(self.current_seq				)


      def        a			(self	:				Optional[Any]    ,      a__	:				Tuple=False				):





         """simple docstring"""



         __snake_case								=       DisjunctiveConstraint(self.token_ids				)

         if stateful:
            __snake_case								=       self.seqlen
            __snake_case								=       self.current_seq
            __snake_case								=       self.completed

         return new_constraint






class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Any    ,      a__	:				List[Constraint]				):





         """simple docstring"""



         __snake_case								=       constraints

         # max # of steps required to fulfill a given constraint
         __snake_case								=       max([c.seqlen for c in constraints]				)
         __snake_case								=       len(a__				)
         __snake_case								=       False

         self.init_state()


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       []
         __snake_case								=       None
         __snake_case								=       [constraint.copy(stateful=a__				) for constraint in self.constraints]


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       0
         if self.inprogress_constraint:
            # extra points for having a constraint mid-fulfilled
            add += self.max_seqlen - self.inprogress_constraint.remaining()

         return (len(self.complete_constraints				) * self.max_seqlen) + add


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       []
         if self.inprogress_constraint is None:
            for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
               __snake_case								=       constraint.advance()
               if isinstance(a__    ,      a__				):
                  token_list.append(a__				)
               elif isinstance(a__    ,      a__				):
                  token_list.extend(a__				)
         else:
            __snake_case								=       self.inprogress_constraint.advance()
            if isinstance(a__    ,      a__				):
               token_list.append(a__				)
            elif isinstance(a__    ,      a__				):
               token_list.extend(a__				)

         if len(a__				) == 0:
            return None
         else:
            return token_list


      def        a			(self	:				Dict    ,      a__	:				Optional[List[int]]				):





         """simple docstring"""



         self.init_state()

         if token_ids is not None:
            for token in token_ids:
               # completes or steps **one** constraint
               __snake_case	,     __snake_case								=       self.add(a__				)

               # the entire list of constraints are fulfilled
               if self.completed:
                  break


      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         if not isinstance(a__    ,      a__				):
            raise ValueError(f"""`token_id` should be an `int`, but is `{token_id}`."""				)

         __snake_case	,     __snake_case								=       False, False

         if self.completed:
            __snake_case								=       True
            __snake_case								=       False
            return complete, stepped

         if self.inprogress_constraint is not None:
            # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
            # job, simply update the state

            __snake_case	,     __snake_case	,     __snake_case								=       self.inprogress_constraint.update(a__				)
            if reset:
               # 1. If the next token breaks the progress, then we must restart.
               #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

               #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
               #     constraint, not the full list of constraints.

               self.pending_constraints.append(self.inprogress_constraint.copy(stateful=a__				)				)
               __snake_case								=       None

            if complete:
               # 2. If the next token completes the constraint, move it to completed list, set
               #     inprogress to None. If there are no pending constraints either, then this full list of constraints
               #     is complete.

               self.complete_constraints.append(self.inprogress_constraint				)
               __snake_case								=       None

               if len(self.pending_constraints				) == 0:
                  # we're done!
                  __snake_case								=       True

         else:
            # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
            # of constraints?

            for cidx, pending_constraint in enumerate(self.pending_constraints				):
               if pending_constraint.does_advance(a__				):
                  __snake_case	,     __snake_case	,     __snake_case								=       pending_constraint.update(a__				)

                  if not stepped:
                     raise Exception(
                         '''`constraint.update(token_id)` is not yielding incremental progress, '''
                         '''even though `constraint.does_advance(token_id)` is true.'''				)

                  if complete:
                     self.complete_constraints.append(a__				)
                     __snake_case								=       None

                  if not complete and stepped:
                     __snake_case								=       pending_constraint

                  if complete or stepped:
                     # If we made any progress at all, then it's at least not a "pending constraint".

                     __snake_case								=       (
                         self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                     )

                     if len(self.pending_constraints				) == 0 and self.inprogress_constraint is None:
                        # If there's no longer any pending after this and no inprogress either, then we must be
                        # complete.

                        __snake_case								=       True

                     break  # prevent accidentally stepping through multiple constraints with just one token.

         return complete, stepped


      def        a			(self	:				Union[str, Any]    ,      a__	:				List[str]=True				):





         """simple docstring"""



         __snake_case								=       ConstraintListState(self.constraints				)  # we actually never though self.constraints objects
         # throughout this process. So it's at initialization state.

         if stateful:
            __snake_case								=       [
                constraint.copy(stateful=a__				) for constraint in self.complete_constraints
            ]
            if self.inprogress_constraint is not None:
               __snake_case								=       self.inprogress_constraint.copy(stateful=a__				)
            __snake_case								=       [constraint.copy() for constraint in self.pending_constraints]

         return new_state








import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (DPMSolverSinglestepScheduler,)
      A_						:    Union[str, Any]							=   (('num_inference_steps', 25),)


      def        a			(self	:				Dict    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''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(**a__				)
         return config


      def        a			(self	:				str    ,      a__	:				Any=0    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)
            # copy over dummy past residuals
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

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

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         pass


      def        a			(self	:				List[Any]    ,      a__	:				Dict=0    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__				)

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

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


      def        a			(self	:				int    ,      a__	:				Tuple=None    ,      **a__	:				List[str]				):





         """simple docstring"""



         if scheduler is None:
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       50
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

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

         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_5_7_4				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



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


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3

         __snake_case								=       DEISMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       UniPCMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         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=a__    ,      prediction_type=a__    ,      sample_max_value=a__    ,      algorithm_type='''dpmsolver++'''    ,      solver_order=a__    ,      solver_type=a__    ,      )


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         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=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     __snake_case								=       self.full_loop(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



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


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.check_over_configs(variance_type=a__				)
         self.check_over_configs(variance_type='''learned_range'''				)


      def        a			(self	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.full_loop(use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_2_4_8				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_4_5_3				) < 1E-3


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''    ,      use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.0_6_4_9				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         assert sample.dtype == torch.floataa

