





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					A__								=     (ScoreSdeVeScheduler,)
					A__								=     ()





					@property
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							4
										lowerCamelCase__      =							3
										lowerCamelCase__      =							8
										lowerCamelCase__      =							8

										lowerCamelCase__      =							torch.rand((batch_size, num_channels, height, width)						)

										return sample





					@property
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							4
										lowerCamelCase__      =							3
										lowerCamelCase__      =							8
										lowerCamelCase__      =							8

										lowerCamelCase__      =							batch_size * num_channels * height * width
										lowerCamelCase__      =							torch.arange(_lowerCAmelCase						)
										lowerCamelCase__      =							sample.reshape(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)
										lowerCamelCase__      =							sample / num_elems
										lowerCamelCase__      =							sample.permute(3						,	0						,	1						,	2						)

										return sample





					def        __magic_name__			(			self						):





										def model(_lowerCAmelCase						,	_lowerCAmelCase						,	*_lowerCAmelCase						):
															return sample * t / (t + 1)

										return model





					def        __magic_name__			(			self						,	**_lowerCAmelCase						):
										lowerCamelCase__      =							{
										    """num_train_timesteps""": 2000,
										    """snr""": 0.15,
										    """sigma_min""": 0.01,
										    """sigma_max""": 1348,
										    """sampling_eps""": 1E-5,
										}

										config.update(**_lowerCAmelCase						)
										return config





					def        __magic_name__			(			self						,	_lowerCAmelCase=0						,	**_lowerCAmelCase						):
										lowerCamelCase__      =							dict(self.forward_default_kwargs						)

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

															lowerCamelCase__      =							self.get_scheduler_config(**_lowerCAmelCase						)
															lowerCamelCase__      =							scheduler_class(**_lowerCAmelCase						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(_lowerCAmelCase						)
																				lowerCamelCase__      =							scheduler_class.from_pretrained(_lowerCAmelCase						)

															lowerCamelCase__      =							scheduler.step_pred(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample
															lowerCamelCase__      =							new_scheduler.step_pred(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample

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

															lowerCamelCase__      =							scheduler.step_correct(_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample
															lowerCamelCase__      =							new_scheduler.step_correct(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample

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





					def        __magic_name__			(			self						,	_lowerCAmelCase=0						,	**_lowerCAmelCase						):
										lowerCamelCase__      =							dict(self.forward_default_kwargs						)
										kwargs.update(_lowerCAmelCase						)

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

															lowerCamelCase__      =							self.get_scheduler_config()
															lowerCamelCase__      =							scheduler_class(**_lowerCAmelCase						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(_lowerCAmelCase						)
																				lowerCamelCase__      =							scheduler_class.from_pretrained(_lowerCAmelCase						)

															lowerCamelCase__      =							scheduler.step_pred(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample
															lowerCamelCase__      =							new_scheduler.step_pred(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample

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

															lowerCamelCase__      =							scheduler.step_correct(_lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample
															lowerCamelCase__      =							new_scheduler.step_correct(
															    _lowerCAmelCase						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample

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





					def        __magic_name__			(			self						):





										for timesteps in [10, 100, 1000]:
															self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)





					def        __magic_name__			(			self						):





										for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]						,	[1, 100, 1000]						):
															self.check_over_configs(sigma_min=_lowerCAmelCase						,	sigma_max=_lowerCAmelCase						)





					def        __magic_name__			(			self						):





										for t in [0.1, 0.5, 0.75]:
															self.check_over_forward(time_step=_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							dict(self.forward_default_kwargs						)

										lowerCamelCase__      =							self.scheduler_classes[0]
										lowerCamelCase__      =							self.get_scheduler_config()
										lowerCamelCase__      =							scheduler_class(**_lowerCAmelCase						)

										lowerCamelCase__      =							3

										lowerCamelCase__      =							self.dummy_model()
										lowerCamelCase__      =							self.dummy_sample_deter

										scheduler.set_sigmas(_lowerCAmelCase						)
										scheduler.set_timesteps(_lowerCAmelCase						)
										lowerCamelCase__      =							torch.manual_seed(0						)

										for i, t in enumerate(scheduler.timesteps						):
															lowerCamelCase__      =							scheduler.sigmas[i]

															for _ in range(scheduler.config.correct_steps						):
																				with torch.no_grad():
																									lowerCamelCase__      =							model(_lowerCAmelCase						,	_lowerCAmelCase						)
																				lowerCamelCase__      =							scheduler.step_correct(_lowerCAmelCase						,	_lowerCAmelCase						,	generator=_lowerCAmelCase						,	**_lowerCAmelCase						).prev_sample

															with torch.no_grad():
																				lowerCamelCase__      =							model(_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							scheduler.step_pred(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	generator=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							output.prev_sample, output.prev_sample_mean

										lowerCamelCase__      =							torch.sum(torch.abs(_lowerCAmelCase						)						)
										lowerCamelCase__      =							torch.mean(torch.abs(_lowerCAmelCase						)						)

										assert np.isclose(result_sum.item()						,	143_7275_8528.0						)
										assert np.isclose(result_mean.item()						,	1871_4530.0						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							dict(self.forward_default_kwargs						)

										lowerCamelCase__      =							kwargs.pop("num_inference_steps"						,	_lowerCAmelCase						)

										for scheduler_class in self.scheduler_classes:
															lowerCamelCase__      =							self.get_scheduler_config()
															lowerCamelCase__      =							scheduler_class(**_lowerCAmelCase						)

															lowerCamelCase__      =							self.dummy_sample
															lowerCamelCase__      =							0.1 * sample

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

															lowerCamelCase__      =							scheduler.step_pred(_lowerCAmelCase						,	0						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample
															lowerCamelCase__      =							scheduler.step_pred(_lowerCAmelCase						,	1						,	_lowerCAmelCase						,	generator=torch.manual_seed(0						)						,	**_lowerCAmelCase						).prev_sample

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



'''simple docstring'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



