






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):
	# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
	snake_case__				:	List[str]	      =    (ScoreSdeVeScheduler,)
	snake_case__				:	Any	      =    ()





	@property
	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Any		=      4
		UpperCamelCase	:Any		=      3
		UpperCamelCase	:Union[str, Any]		=      8
		UpperCamelCase	:Any		=      8

		UpperCamelCase	:Union[str, Any]		=      torch.rand((batch_size, num_channels, height, width)      )

		return sample





	@property
	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      4
		UpperCamelCase	:int		=      3
		UpperCamelCase	:List[str]		=      8
		UpperCamelCase	:Optional[int]		=      8

		UpperCamelCase	:Optional[int]		=      batch_size * num_channels * height * width
		UpperCamelCase	:Optional[Any]		=      torch.arange(__lowerCamelCase      )
		UpperCamelCase	:Optional[int]		=      sample.reshape(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )
		UpperCamelCase	:int		=      sample / num_elems
		UpperCamelCase	:Optional[int]		=      sample.permute(3					,    0					,    1					,    2      )

		return sample





	def 		_A					(						self							:   int      ):
		def model(__lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Any					,    *__lowerCamelCase							:   List[str]      ):
			return sample * t / (t + 1)

		return model





	def 		_A					(						self							:   Dict					,    **__lowerCamelCase							:   Tuple      ):
		UpperCamelCase	:Dict		=      {
		    """num_train_timesteps""": 2_000,
		    """snr""": 0.15,
		    """sigma_min""": 0.01,
		    """sigma_max""": 1_348,
		    """sampling_eps""": 1E-5,
		}

		config.update(**__lowerCamelCase      )
		return config





	def 		_A					(						self							:   Union[str, Any]					,    __lowerCamelCase							:   Any=0					,    **__lowerCamelCase							:   Optional[Any]      ):
		UpperCamelCase	:Tuple		=      dict(self.forward_default_kwargs      )

		for scheduler_class in self.scheduler_classes:
			UpperCamelCase	:Union[str, Any]		=      self.dummy_sample
			UpperCamelCase	:List[Any]		=      0.1 * sample

			UpperCamelCase	:List[Any]		=      self.get_scheduler_config(**__lowerCamelCase      )
			UpperCamelCase	:Any		=      scheduler_class(**__lowerCamelCase      )

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__lowerCamelCase      )
				UpperCamelCase	:Tuple		=      scheduler_class.from_pretrained(__lowerCamelCase      )

			UpperCamelCase	:Optional[int]		=      scheduler.step_pred(
			    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    generator=torch.manual_seed(0      )					,    **__lowerCamelCase      ).prev_sample
			UpperCamelCase	:Optional[Any]		=      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"

			UpperCamelCase	:List[str]		=      scheduler.step_correct(__lowerCamelCase					,    __lowerCamelCase					,    generator=torch.manual_seed(0      )					,    **__lowerCamelCase      ).prev_sample
			UpperCamelCase	:Union[str, Any]		=      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 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   Tuple=0					,    **__lowerCamelCase							:   int      ):
		UpperCamelCase	:Any		=      dict(self.forward_default_kwargs      )
		kwargs.update(__lowerCamelCase      )

		for scheduler_class in self.scheduler_classes:
			UpperCamelCase	:Tuple		=      self.dummy_sample
			UpperCamelCase	:Dict		=      0.1 * sample

			UpperCamelCase	:Optional[int]		=      self.get_scheduler_config()
			UpperCamelCase	:Optional[int]		=      scheduler_class(**__lowerCamelCase      )

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__lowerCamelCase      )
				UpperCamelCase	:Dict		=      scheduler_class.from_pretrained(__lowerCamelCase      )

			UpperCamelCase	:Optional[int]		=      scheduler.step_pred(
			    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    generator=torch.manual_seed(0      )					,    **__lowerCamelCase      ).prev_sample
			UpperCamelCase	:Union[str, Any]		=      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"

			UpperCamelCase	:int		=      scheduler.step_correct(__lowerCamelCase					,    __lowerCamelCase					,    generator=torch.manual_seed(0      )					,    **__lowerCamelCase      ).prev_sample
			UpperCamelCase	:int		=      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 		_A					(						self							:   str      ):
		for timesteps in [10, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=__lowerCamelCase      )





	def 		_A					(						self							:   List[Any]      ):
		for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]					,    [1, 100, 1_000]      ):
			self.check_over_configs(sigma_min=__lowerCamelCase					,    sigma_max=__lowerCamelCase      )





	def 		_A					(						self							:   Dict      ):
		for t in [0.1, 0.5, 0.75]:
			self.check_over_forward(time_step=__lowerCamelCase      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Any		=      dict(self.forward_default_kwargs      )

		UpperCamelCase	:Dict		=      self.scheduler_classes[0]
		UpperCamelCase	:List[str]		=      self.get_scheduler_config()
		UpperCamelCase	:List[Any]		=      scheduler_class(**__lowerCamelCase      )

		UpperCamelCase	:str		=      3

		UpperCamelCase	:int		=      self.dummy_model()
		UpperCamelCase	:Any		=      self.dummy_sample_deter

		scheduler.set_sigmas(__lowerCamelCase      )
		scheduler.set_timesteps(__lowerCamelCase      )
		UpperCamelCase	:int		=      torch.manual_seed(0      )

		for i, t in enumerate(scheduler.timesteps      ):
			UpperCamelCase	:Union[str, Any]		=      scheduler.sigmas[i]

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

			with torch.no_grad():
				UpperCamelCase	:Optional[Any]		=      model(__lowerCamelCase					,    __lowerCamelCase      )

			UpperCamelCase	:Dict		=      scheduler.step_pred(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    generator=__lowerCamelCase					,    **__lowerCamelCase      )
			UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      output.prev_sample, output.prev_sample_mean

		UpperCamelCase	:Optional[int]		=      torch.sum(torch.abs(__lowerCamelCase      )      )
		UpperCamelCase	:Union[str, Any]		=      torch.mean(torch.abs(__lowerCamelCase      )      )

		assert np.isclose(result_sum.item()					,    14372758528.0      )
		assert np.isclose(result_mean.item()					,    18714530.0      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Tuple		=      dict(self.forward_default_kwargs      )

		UpperCamelCase	:Optional[Any]		=      kwargs.pop("""num_inference_steps"""					,    __lowerCamelCase      )

		for scheduler_class in self.scheduler_classes:
			UpperCamelCase	:str		=      self.get_scheduler_config()
			UpperCamelCase	:Dict		=      scheduler_class(**__lowerCamelCase      )

			UpperCamelCase	:Optional[Any]		=      self.dummy_sample
			UpperCamelCase	:Dict		=      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"""      ):
				UpperCamelCase	:List[str]		=      num_inference_steps

			UpperCamelCase	:List[str]		=      scheduler.step_pred(__lowerCamelCase					,    0					,    __lowerCamelCase					,    generator=torch.manual_seed(0      )					,    **__lowerCamelCase      ).prev_sample
			UpperCamelCase	:List[str]		=      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      )








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
