import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     (PNDMScheduler,)
							UpperCAmelCase				   =     (("num_inference_steps", 50),)







							def  UpperCamelCase_						(   self      :     str ,			**_A      :     Dict		):
												_UpperCamelCase			     =     {
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												}

												config.update(**_A		)
												return config







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Tuple=0 ,			**_A      :     Optional[int]		):
												_UpperCamelCase			     =     dict(self.forward_default_kwargs		)
												_UpperCamelCase			     =     kwargs.pop('''num_inference_steps''' ,			_A		)
												_UpperCamelCase			     =     self.dummy_sample
												_UpperCamelCase			     =     0.1 * sample
												_UpperCamelCase			     =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	_UpperCamelCase			     =     self.get_scheduler_config(**_A		)
																	_UpperCamelCase			     =     scheduler_class(**_A		)
																	scheduler.set_timesteps(_A		)
																	# copy over dummy past residuals
																	_UpperCamelCase			     =     dummy_past_residuals[:]

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

																	_UpperCamelCase			     =     scheduler.step_prk(_A ,			_A ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     new_scheduler.step_prk(_A ,			_A ,			_A ,			**_A		).prev_sample

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

																	_UpperCamelCase			     =     scheduler.step_plms(_A ,			_A ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     new_scheduler.step_plms(_A ,			_A ,			_A ,			**_A		).prev_sample

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







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												pass







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     str=0 ,			**_A      :     str		):
												_UpperCamelCase			     =     dict(self.forward_default_kwargs		)
												_UpperCamelCase			     =     kwargs.pop('''num_inference_steps''' ,			_A		)
												_UpperCamelCase			     =     self.dummy_sample
												_UpperCamelCase			     =     0.1 * sample
												_UpperCamelCase			     =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	_UpperCamelCase			     =     self.get_scheduler_config()
																	_UpperCamelCase			     =     scheduler_class(**_A		)
																	scheduler.set_timesteps(_A		)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_A		)
																						_UpperCamelCase			     =     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)
																						_UpperCamelCase			     =     dummy_past_residuals[:]

																	_UpperCamelCase			     =     scheduler.step_prk(_A ,			_A ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     new_scheduler.step_prk(_A ,			_A ,			_A ,			**_A		).prev_sample

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

																	_UpperCamelCase			     =     scheduler.step_plms(_A ,			_A ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     new_scheduler.step_plms(_A ,			_A ,			_A ,			**_A		).prev_sample

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







							def  UpperCamelCase_						(   self      :     str ,			**_A      :     Any		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(**_A		)
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase			     =     10
												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter
												scheduler.set_timesteps(_A		)

												for i, t in enumerate(scheduler.prk_timesteps		):
																	_UpperCamelCase			     =     model(_A ,			_A		)
																	_UpperCamelCase			     =     scheduler.step_prk(_A ,			_A ,			_A		).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps		):
																	_UpperCamelCase			     =     model(_A ,			_A		)
																	_UpperCamelCase			     =     scheduler.step_plms(_A ,			_A ,			_A		).prev_sample

												return sample







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     dict(self.forward_default_kwargs		)

												_UpperCamelCase			     =     kwargs.pop('''num_inference_steps''' ,			_A		)

												for scheduler_class in self.scheduler_classes:
																	_UpperCamelCase			     =     self.get_scheduler_config()
																	_UpperCamelCase			     =     scheduler_class(**_A		)

																	_UpperCamelCase			     =     self.dummy_sample
																	_UpperCamelCase			     =     0.1 * sample

																	if num_inference_steps is not None and hasattr(_A ,			'''set_timesteps'''		):
																						scheduler.set_timesteps(_A		)
																	elif num_inference_steps is not None and not hasattr(_A ,			'''set_timesteps'''		):
																						_UpperCamelCase			     =     num_inference_steps

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

																	_UpperCamelCase			     =     scheduler.step_prk(_A ,			0 ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     scheduler.step_prk(_A ,			1 ,			_A ,			**_A		).prev_sample

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

																	_UpperCamelCase			     =     scheduler.step_plms(_A ,			0 ,			_A ,			**_A		).prev_sample
																	_UpperCamelCase			     =     scheduler.step_plms(_A ,			1 ,			_A ,			**_A		).prev_sample

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







							def  UpperCamelCase_						(   self      :     Any		):
												for timesteps in [100, 1000]:
																	self.check_over_configs(num_train_timesteps=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=_A		)

												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(steps_offset=1		)
												_UpperCamelCase			     =     scheduler_class(**_A		)
												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  UpperCamelCase_						(   self      :     List[str]		):
												for beta_start, beta_end in zip([0.0001, 0.001] ,			[0.002, 0.02]		):
																	self.check_over_configs(beta_start=_A ,			beta_end=_A		)







							def  UpperCamelCase_						(   self      :     str		):
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for t in [1, 5, 10]:
																	self.check_over_forward(time_step=_A		)







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







							def  UpperCamelCase_						(   self      :     Dict		):
												# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
												_UpperCamelCase			     =     27

												for scheduler_class in self.scheduler_classes:
																	_UpperCamelCase			     =     self.dummy_sample
																	_UpperCamelCase			     =     0.1 * sample

																	_UpperCamelCase			     =     self.get_scheduler_config()
																	_UpperCamelCase			     =     scheduler_class(**_A		)

																	scheduler.set_timesteps(_A		)

																	# 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]		):
																						_UpperCamelCase			     =     scheduler.step_prk(_A ,			_A ,			_A		).prev_sample







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												with self.assertRaises(_A		):
																	_UpperCamelCase			     =     self.scheduler_classes[0]
																	_UpperCamelCase			     =     self.get_scheduler_config()
																	_UpperCamelCase			     =     scheduler_class(**_A		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.full_loop()
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     self.full_loop(prediction_type='''v_prediction'''		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     str		):
												# We specify different beta, so that the first alpha is 0.99
												_UpperCamelCase			     =     self.full_loop(set_alpha_to_one=_A ,			beta_start=0.01		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												# We specify different beta, so that the first alpha is 0.99
												_UpperCamelCase			     =     self.full_loop(set_alpha_to_one=_A ,			beta_start=0.01		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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

import math
class     lowerCAmelCase_     :







							def __init__(   self      :     Tuple ,			_A      :     int=0		):  # a graph with Node 0,1,...,N-1
												_UpperCamelCase			     =     n
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # adjacency matrix for weight
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # dp[i][j] stores minimum distance from i to j







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     w







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for k in range(0 ,			self.n		):
																	for i in range(0 ,			self.n		):
																						for j in range(0 ,			self.n		):
																											_UpperCamelCase			     =     min(self.dp[i][j] ,			self.dp[i][k] + self.dp[k][j]		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int] ,			_A      :     Optional[int]		):
												return self.dp[u][v]


if __name__ == "__main__":
					_lowerCAmelCase       =		Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)
