



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(ScoreSdeVeScheduler,)
			UpperCamelCase      						=	()




			@property
			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     4
					__UpperCAmelCase   :					Optional[int]													=     3
					__UpperCAmelCase   :					Any													=     8
					__UpperCAmelCase   :					Union[str, Any]													=     8

					__UpperCAmelCase   :					Tuple													=     torch.rand((batch_size, num_channels, height, width)			)

					return sample




			@property
			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     4
					__UpperCAmelCase   :					List[str]													=     3
					__UpperCAmelCase   :					Optional[Any]													=     8
					__UpperCAmelCase   :					Tuple													=     8

					__UpperCAmelCase   :					Union[str, Any]													=     batch_size * num_channels * height * width
					__UpperCAmelCase   :					str													=     torch.arange(a_			)
					__UpperCAmelCase   :					Tuple													=     sample.reshape(a_	,	a_	,	a_	,	a_			)
					__UpperCAmelCase   :					Optional[Any]													=     sample / num_elems
					__UpperCAmelCase   :					List[str]													=     sample.permute(3	,	0	,	1	,	2			)

					return sample




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					def model(a_							:			Optional[int]	,	a_							:			Optional[Any]	,	*a_							:			Any			):
							return sample * t / (t + 1)

					return model




			def 			snake_case__					(       self							:			Dict	,	**a_							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     {
					    '''num_train_timesteps''': 20_00,
					    '''snr''': 0.1_5,
					    '''sigma_min''': 0.0_1,
					    '''sigma_max''': 13_48,
					    '''sampling_eps''': 1e-5,
					}

					config.update(**a_			)
					return config




			def 			snake_case__					(       self							:			Dict	,	a_							:			Union[str, Any]=0	,	**a_							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     dict(self.forward_default_kwargs			)

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

							__UpperCAmelCase   :					Optional[int]													=     self.get_scheduler_config(**a_			)
							__UpperCAmelCase   :					Tuple													=     scheduler_class(**a_			)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(a_			)
									__UpperCAmelCase   :					List[str]													=     scheduler_class.from_pretrained(a_			)

							__UpperCAmelCase   :					List[Any]													=     scheduler.step_pred(
							    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
							__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_pred(
							    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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

							__UpperCAmelCase   :					Dict													=     scheduler.step_correct(a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
							__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_correct(
							    a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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




			def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			Any=0	,	**a_							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     dict(self.forward_default_kwargs			)
					kwargs.update(a_			)

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

							__UpperCAmelCase   :					int													=     self.get_scheduler_config()
							__UpperCAmelCase   :					Optional[Any]													=     scheduler_class(**a_			)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(a_			)
									__UpperCAmelCase   :					str													=     scheduler_class.from_pretrained(a_			)

							__UpperCAmelCase   :					Union[str, Any]													=     scheduler.step_pred(
							    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
							__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_pred(
							    a_	,	a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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

							__UpperCAmelCase   :					List[Any]													=     scheduler.step_correct(a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
							__UpperCAmelCase   :					Optional[int]													=     new_scheduler.step_correct(
							    a_	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]	,	[1, 1_00, 10_00]			):
							self.check_over_configs(sigma_min=a_	,	sigma_max=a_			)




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					for t in [0.1, 0.5, 0.7_5]:
							self.check_over_forward(time_step=a_			)




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     dict(self.forward_default_kwargs			)

					__UpperCAmelCase   :					int													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					List[str]													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Union[str, Any]													=     scheduler_class(**a_			)

					__UpperCAmelCase   :					str													=     3

					__UpperCAmelCase   :					str													=     self.dummy_model()
					__UpperCAmelCase   :					List[Any]													=     self.dummy_sample_deter

					scheduler.set_sigmas(a_			)
					scheduler.set_timesteps(a_			)
					__UpperCAmelCase   :					int													=     torch.manual_seed(0			)

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

							for _ in range(scheduler.config.correct_steps			):
									with torch.no_grad():
											__UpperCAmelCase   :					Union[str, Any]													=     model(a_	,	a_			)
									__UpperCAmelCase   :					Optional[int]													=     scheduler.step_correct(a_	,	a_	,	generator=a_	,	**a_			).prev_sample

							with torch.no_grad():
									__UpperCAmelCase   :					int													=     model(a_	,	a_			)

							__UpperCAmelCase   :					int													=     scheduler.step_pred(a_	,	a_	,	a_	,	generator=a_	,	**a_			)
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     output.prev_sample, output.prev_sample_mean

					__UpperCAmelCase   :					Any													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Optional[int]													=     torch.mean(torch.abs(a_			)			)

					assert np.isclose(result_sum.item()	,	1_4_3_7_2_7_5_8_5_2_8.0			)
					assert np.isclose(result_mean.item()	,	1_8_7_1_4_5_3_0.0			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     dict(self.forward_default_kwargs			)

					__UpperCAmelCase   :					int													=     kwargs.pop('''num_inference_steps'''	,	a_			)

					for scheduler_class in self.scheduler_classes:
							__UpperCAmelCase   :					Union[str, Any]													=     self.get_scheduler_config()
							__UpperCAmelCase   :					str													=     scheduler_class(**a_			)

							__UpperCAmelCase   :					List[str]													=     self.dummy_sample
							__UpperCAmelCase   :					Dict													=     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   :					Tuple													=     num_inference_steps

							__UpperCAmelCase   :					Dict													=     scheduler.step_pred(a_	,	0	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample
							__UpperCAmelCase   :					Dict													=     scheduler.step_pred(a_	,	1	,	a_	,	generator=torch.manual_seed(0			)	,	**a_			).prev_sample

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










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





