




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class        __snake_case  :
    lowerCAmelCase__				=     42
    lowerCAmelCase__				=     42






class        __snake_case  :



    def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int      )  ->    Optional[Any]:





        '''simple docstring'''




        _lowerCAmelCase    :    Tuple										=      [[] for _ in range(A_      )]
        _lowerCAmelCase    :    List[str]										=      size



    def __getitem__(      self      :		Optional[int]		,	_UpperCAmelCase      :		int      )  ->    Iterator[Edge]:





        '''simple docstring'''




        return iter(self._graph[vertex]      )



    @property
    def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Dict:





        '''simple docstring'''




        return self._size



    def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    str:





        '''simple docstring'''




        if weight not in (0, 1):
            raise ValueError("""Edge weight must be either 0 or 1."""      )

        if to_vertex < 0 or to_vertex >= self.size:
            raise ValueError("""Vertex indexes must be in [0; size)."""      )

        self._graph[from_vertex].append(Edge(A_		,	A_      )      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    int | None:





        '''simple docstring'''




        _lowerCAmelCase    :    Optional[Any]										=      deque([start_vertex]      )
        _lowerCAmelCase    :    Optional[Any]										=      [None] * self.size
        _lowerCAmelCase    :    List[str]										=      0

        while queue:
            _lowerCAmelCase    :    Union[str, Any]										=      queue.popleft()
            _lowerCAmelCase    :    Union[str, Any]										=      distances[current_vertex]
            if current_distance is None:
                continue

            for edge in self[current_vertex]:
                _lowerCAmelCase    :    List[Any]										=      current_distance + edge.weight
                _lowerCAmelCase    :    List[Any]										=      distances[edge.destination_vertex]
                if (
                    isinstance(A_		,	A_      )
                    and new_distance >= dest_vertex_distance
                ):
                    continue
                _lowerCAmelCase    :    Tuple										=      new_distance
                if edge.weight == 0:
                    queue.appendleft(edge.destination_vertex      )
                else:
                    queue.append(edge.destination_vertex      )

        if distances[finish_vertex] is None:
            raise ValueError("""No path from start_vertex to finish_vertex."""      )

        return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																new_scheduler.set_timesteps(_UpperCAmelCase      )
																# copy over dummy past residuals
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

												# copy over dummy past residuals (must be after setting timesteps)
												_lowerCAmelCase    :    str										=      dummy_past_residuals[:]

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

																# copy over dummy past residual (must be after setting timesteps)
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    int										=      self.get_scheduler_config(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

												if num_inference_steps is not None and hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																scheduler.set_timesteps(_UpperCAmelCase      )
												elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												_lowerCAmelCase    :    List[Any]										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												_lowerCAmelCase    :    List[str]										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												self.assertEqual(output_a.shape		,	sample.shape      )
												self.assertEqual(output_a.shape		,	output_a.shape      )

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												self.assertEqual(output_a.shape		,	sample.shape      )
												self.assertEqual(output_a.shape		,	output_a.shape      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




								for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=_UpperCAmelCase      )



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





								'''simple docstring'''




								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								scheduler.set_timesteps(10      )
								assert torch.equal(
								    scheduler.timesteps		,	torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCAmelCase      )



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								for t in [1, 5, 10]:
												self.check_over_forward(time_step=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]      ):
												self.check_over_forward(num_inference_steps=_UpperCAmelCase      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.step_plms(self.dummy_sample		,	1		,	self.dummy_sample      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

