"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						(IPNDMScheduler,)
				_lowerCAmelCase     :    Any			  =						(("""num_inference_steps""", 50),)

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			{'num_train_timesteps': 10_00}
										config.update(**lowerCAmelCase    )
										return config

				def 		snake_case (				self		,    lowerCAmelCase=0		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			dict(self.forward_default_kwargs    )
										snake_case								=			kwargs.pop('num_inference_steps'		,    lowerCAmelCase    )
										snake_case								=			self.dummy_sample
										snake_case								=			0.1 * sample
										snake_case								=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

																if time_step is None:
																						snake_case								=			scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			new_scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			new_scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self		,    lowerCAmelCase=0		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			dict(self.forward_default_kwargs    )
										snake_case								=			kwargs.pop('num_inference_steps'		,    lowerCAmelCase    )
										snake_case								=			self.dummy_sample
										snake_case								=			0.1 * sample
										snake_case								=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																snake_case								=			self.get_scheduler_config()
																snake_case								=			scheduler_class(**lowerCAmelCase    )
																scheduler.set_timesteps(lowerCAmelCase    )

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

																if time_step is None:
																						snake_case								=			scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

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

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			new_scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			new_scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.scheduler_classes[0]
										snake_case								=			self.get_scheduler_config(**lowerCAmelCase    )
										snake_case								=			scheduler_class(**lowerCAmelCase    )

										snake_case								=			10
										snake_case								=			self.dummy_model()
										snake_case								=			self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase    )

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

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

										return sample

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			dict(self.forward_default_kwargs    )

										snake_case								=			kwargs.pop('num_inference_steps'		,    lowerCAmelCase    )

										for scheduler_class in self.scheduler_classes:
																snake_case								=			self.get_scheduler_config()
																snake_case								=			scheduler_class(**lowerCAmelCase    )

																snake_case								=			self.dummy_sample
																snake_case								=			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'    ):
																						snake_case								=			num_inference_steps

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

																snake_case								=			scheduler.timesteps[5]
																snake_case								=			scheduler.timesteps[6]

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample
																snake_case								=			scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										for timesteps in [1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase		,    time_step=lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for t, num_inference_steps in zip([1, 5, 10]		,    [10, 50, 1_00]    ):
																self.check_over_forward(num_inference_steps=lowerCAmelCase		,    time_step=lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.full_loop()
										snake_case								=			torch.mean(torch.abs(lowerCAmelCase    )    )

										assert abs(result_mean.item() - 2_54_05_29    ) < 10



"""simple docstring"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


