import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class   __lowercase						(_a     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	(HeunDiscreteScheduler,)
							_UpperCAmelCase						     =	1_0






							def 				UpperCamelCase__    (			self ,	**lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				{
										    'num_train_timesteps': 1_1_0_0,
										    'beta_start': 0.0_001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**lowerCAmelCase__					)
										return config






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]					):
													self.check_over_configs(beta_start=lowerCAmelCase__ ,	beta_end=lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for schedule in ["linear", "scaled_linear", "exp"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for clip_sample_range in [1.0, 2.0, 3.0]:
													self.check_over_configs(clip_sample_range=lowerCAmelCase__ ,	clip_sample=lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




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






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.get_scheduler_config()
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				scheduler_class(**lowerCAmelCase__					)

										scheduler.set_timesteps(self.num_inference_steps					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.dummy_model()
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.dummy_sample_deter * scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE_		:    Any 		=				sample.to(lowerCAmelCase__					)

										for i, t in enumerate(scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    int 		=				output.prev_sample

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				torch.sum(torch.abs(lowerCAmelCase__					)					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				torch.mean(torch.abs(lowerCAmelCase__					)					)

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 0.1_233					) < 1E-2
													assert abs(result_mean.item() - 0.0_002					) < 1E-3
										else:
													# CUDA
													assert abs(result_sum.item() - 0.1_233					) < 1E-2
													assert abs(result_mean.item() - 0.0_002					) < 1E-3






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.get_scheduler_config(prediction_type='v_prediction'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				scheduler_class(**lowerCAmelCase__					)

										scheduler.set_timesteps(self.num_inference_steps					)

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.dummy_model()
										SCREAMING_SNAKE_CASE_		:    Dict 		=				self.dummy_sample_deter * scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE_		:    int 		=				sample.to(lowerCAmelCase__					)

										for i, t in enumerate(scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    str 		=				scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    str 		=				model(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    int 		=				output.prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.sum(torch.abs(lowerCAmelCase__					)					)
										SCREAMING_SNAKE_CASE_		:    int 		=				torch.mean(torch.abs(lowerCAmelCase__					)					)

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_934E-07					) < 1E-2
													assert abs(result_mean.item() - 6.1_112E-10					) < 1E-3
										else:
													# CUDA
													assert abs(result_sum.item() - 4.693_428_650_170_972E-07					) < 1E-2
													assert abs(result_mean.item() - 0.0_002					) < 1E-3






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.get_scheduler_config()
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				scheduler_class(**lowerCAmelCase__					)

										scheduler.set_timesteps(self.num_inference_steps ,	device=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				self.dummy_model()
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.dummy_sample_deter.to(lowerCAmelCase__					) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    Any 		=				model(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    Any 		=				output.prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.sum(torch.abs(lowerCAmelCase__					)					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.mean(torch.abs(lowerCAmelCase__					)					)

										if str(lowerCAmelCase__					).startswith('cpu'					):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 0.1_233					) < 1E-2
													assert abs(result_mean.item() - 0.0_002					) < 1E-3
										elif str(lowerCAmelCase__					).startswith('mps'					):
													# Larger tolerance on mps
													assert abs(result_mean.item() - 0.0_002					) < 1E-2
										else:
													# CUDA
													assert abs(result_sum.item() - 0.1_233					) < 1E-2
													assert abs(result_mean.item() - 0.0_002					) < 1E-3






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.get_scheduler_config()
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				scheduler_class(**lowerCAmelCase__ ,	use_karras_sigmas=lowerCAmelCase__					)

										scheduler.set_timesteps(self.num_inference_steps ,	device=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				self.dummy_model()
										SCREAMING_SNAKE_CASE_		:    Dict 		=				self.dummy_sample_deter.to(lowerCAmelCase__					) * scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE_		:    Any 		=				sample.to(lowerCAmelCase__					)

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE_		:    int 		=				scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    Dict 		=				model(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    int 		=				scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				output.prev_sample

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.sum(torch.abs(lowerCAmelCase__					)					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				torch.mean(torch.abs(lowerCAmelCase__					)					)

										assert abs(result_sum.item() - 0.00_015					) < 1E-2
										assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07					) < 1E-2









from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

lowerCAmelCase__ :			str         ={
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				{start}
			SCREAMING_SNAKE_CASE_		:    int 		=				[start]
			SCREAMING_SNAKE_CASE_		:    Queue 		=				Queue()
			queue.put(A__							)
			while not queue.empty():
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.get()
						for w in graph[v]:
									if w not in explored:
												explored.add(A__							)
												result.append(A__							)
												queue.put(A__							)
			return result






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				{start}
			SCREAMING_SNAKE_CASE_		:    str 		=				[start]
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				deque([start]							)
			while queue:
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.popleft()
						for child in graph[v]:
									if child not in visited:
												visited.add(A__							)
												result.append(A__							)
												queue.append(A__							)
			return result






def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				F'''from __main__ import G, {name}'''
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				1_0_0_0_0
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				timeit(F'''{name}(G, \'A\')''',			setup=A__,			number=A__							)
			print(F'''{name:<35} finished {number} runs in {res:.5f} seconds'''							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				benchmark_function('breadth_first_search')
				benchmark_function('breadth_first_search_with_deque')
				# breadth_first_search                finished 10000 runs in 0.20999 seconds
				# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





