

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =(PNDMScheduler,)
			lowerCamelCase__							     =(('num_inference_steps', 50),)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   **a				:			Optional[int]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							{
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**a						)
				return config
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			List[str]=0   ,   **a				:			int						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							dict(self.forward_default_kwargs						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							kwargs.pop("num_inference_steps"   ,   a						)
				SCREAMING_SNAKE_CASE :   str					  =							self.dummy_sample
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							0.1 * sample
				SCREAMING_SNAKE_CASE :   Any					  =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_scheduler_config(**a						)
					SCREAMING_SNAKE_CASE :   Any					  =							scheduler_class(**a						)
					scheduler.set_timesteps(a						)
					# copy over dummy past residuals
					SCREAMING_SNAKE_CASE :   str					  =							dummy_past_residuals[:]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(a						)
						SCREAMING_SNAKE_CASE :   List[Any]					  =							scheduler_class.from_pretrained(a						)
						new_scheduler.set_timesteps(a						)
						# copy over dummy past residuals
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dummy_past_residuals[:]

					SCREAMING_SNAKE_CASE :   Dict					  =							scheduler.step_prk(a   ,   a   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   int					  =							new_scheduler.step_prk(a   ,   a   ,   a   ,   **a						).prev_sample

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

					SCREAMING_SNAKE_CASE :   List[Any]					  =							scheduler.step_plms(a   ,   a   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							new_scheduler.step_plms(a   ,   a   ,   a   ,   **a						).prev_sample

					assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Any:

				"""simple docstring"""

				pass
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Optional[int]=0   ,   **a				:			Dict						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							dict(self.forward_default_kwargs						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							kwargs.pop("num_inference_steps"   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.dummy_sample
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							0.1 * sample
				SCREAMING_SNAKE_CASE :   Any					  =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_scheduler_config()
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							scheduler_class(**a						)
					scheduler.set_timesteps(a						)

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

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

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

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							scheduler.step_prk(a   ,   a   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							new_scheduler.step_prk(a   ,   a   ,   a   ,   **a						).prev_sample

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

					SCREAMING_SNAKE_CASE :   str					  =							scheduler.step_plms(a   ,   a   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   List[str]					  =							new_scheduler.step_plms(a   ,   a   ,   a   ,   **a						).prev_sample

					assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   **a				:			int						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.get_scheduler_config(**a						)
				SCREAMING_SNAKE_CASE :   str					  =							scheduler_class(**a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							10
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.dummy_model()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.dummy_sample_deter
				scheduler.set_timesteps(a						)

				for i, t in enumerate(scheduler.prk_timesteps						):
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(a   ,   a						)
					SCREAMING_SNAKE_CASE :   Any					  =							scheduler.step_prk(a   ,   a   ,   a						).prev_sample

				for i, t in enumerate(scheduler.plms_timesteps						):
					SCREAMING_SNAKE_CASE :   Dict					  =							model(a   ,   a						)
					SCREAMING_SNAKE_CASE :   List[str]					  =							scheduler.step_plms(a   ,   a   ,   a						).prev_sample

				return sample
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							dict(self.forward_default_kwargs						)

				SCREAMING_SNAKE_CASE :   int					  =							kwargs.pop("num_inference_steps"   ,   a						)

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE :   Dict					  =							self.get_scheduler_config()
					SCREAMING_SNAKE_CASE :   str					  =							scheduler_class(**a						)

					SCREAMING_SNAKE_CASE :   List[str]					  =							self.dummy_sample
					SCREAMING_SNAKE_CASE :   Any					  =							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"						):
						SCREAMING_SNAKE_CASE :   str					  =							num_inference_steps

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

					SCREAMING_SNAKE_CASE :   Any					  =							scheduler.step_prk(a   ,   0   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   str					  =							scheduler.step_prk(a   ,   1   ,   a   ,   **a						).prev_sample

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

					SCREAMING_SNAKE_CASE :   List[str]					  =							scheduler.step_plms(a   ,   0   ,   a   ,   **a						).prev_sample
					SCREAMING_SNAKE_CASE :   int					  =							scheduler.step_plms(a   ,   1   ,   a   ,   **a						).prev_sample

					self.assertEqual(output_a.shape   ,   sample.shape						)
					self.assertEqual(output_a.shape   ,   output_a.shape						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Union[str, Any]:

				"""simple docstring"""

				for timesteps in [100, 1000]:
					self.check_over_configs(num_train_timesteps=a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Union[str, Any]:

				"""simple docstring"""

				for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_scheduler_config(steps_offset=1						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							scheduler_class(**a						)
				scheduler.set_timesteps(10						)
				assert torch.equal(
				    scheduler.timesteps   ,   torch.LongTensor(
				        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]						)   ,   )
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Union[str, Any]:

				"""simple docstring"""

				for beta_start, beta_end in zip([0.0001, 0.001]   ,   [0.002, 0.02]						):
					self.check_over_configs(beta_start=a   ,   beta_end=a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							int:

				"""simple docstring"""

				for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[int]:

				"""simple docstring"""

				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=a						)
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[int]:

				"""simple docstring"""

				for t in [1, 5, 10]:
					self.check_over_forward(time_step=a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[str]:

				"""simple docstring"""

				for t, num_inference_steps in zip([1, 5, 10]   ,   [10, 50, 100]						):
					self.check_over_forward(num_inference_steps=a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							27

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

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.get_scheduler_config()
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							scheduler_class(**a						)

					scheduler.set_timesteps(a						)

					# before power of 3 fix, would error on first step, so we only need to do two
					for i, t in enumerate(scheduler.prk_timesteps[:2]						):
						SCREAMING_SNAKE_CASE :   Dict					  =							scheduler.step_prk(a   ,   a   ,   a						).prev_sample
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Optional[int]:

				"""simple docstring"""

				with self.assertRaises(a						):
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.get_scheduler_config()
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							scheduler_class(**a						)

					scheduler.step_plms(self.dummy_sample   ,   1   ,   self.dummy_sample						).prev_sample
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.full_loop()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.sum(torch.abs(a						)						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.mean(torch.abs(a						)						)

				assert abs(result_sum.item() - 198.1318						) < 1e-2
				assert abs(result_mean.item() - 0.2580						) < 1e-3
			def 				__UpperCamelCase			(      self				:			Any						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.full_loop(prediction_type="v_prediction"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.sum(torch.abs(a						)						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.mean(torch.abs(a						)						)

				assert abs(result_sum.item() - 67.3986						) < 1e-2
				assert abs(result_mean.item() - 0.0878						) < 1e-3
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.full_loop(set_alpha_to_one=a   ,   beta_start=0.01						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.sum(torch.abs(a						)						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.mean(torch.abs(a						)						)

				assert abs(result_sum.item() - 230.0399						) < 1e-2
				assert abs(result_mean.item() - 0.2995						) < 1e-3






			def 				__UpperCamelCase			(      self				:			int						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.full_loop(set_alpha_to_one=a   ,   beta_start=0.01						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.sum(torch.abs(a						)						)
				SCREAMING_SNAKE_CASE :   Dict					  =							torch.mean(torch.abs(a						)						)

				assert abs(result_sum.item() - 186.9482						) < 1e-2
				assert abs(result_mean.item() - 0.2434						) < 1e-3


from __future__ import annotations

import math






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			int						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							size
				# approximate the overall size of segment tree with given value
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]
				# create array to store lazy update
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[0 for i in range(0   ,   4 * size						)]
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]  # flag for lazy update
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2 + 1
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			list[int]						)				->							None:

				"""simple docstring"""

				if left_element == right_element:
					SCREAMING_SNAKE_CASE :   int					  =							a[left_element - 1]
				else:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							(left_element + right_element) // 2
					self.build(self.left(a						)   ,   a   ,   a   ,   a						)
					self.build(self.right(a						)   ,   mid + 1   ,   a   ,   a						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							max(
					    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							bool:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   Any					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[str]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Any					  =							True
						SCREAMING_SNAKE_CASE :   List[Any]					  =							True

				if right_element < a or left_element > b:
					return True
				if left_element >= a and right_element <= b:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							val
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   Tuple					  =							True
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
					return True
				SCREAMING_SNAKE_CASE :   int					  =							(left_element + right_element) // 2
				self.update(self.left(a						)   ,   a   ,   a   ,   a   ,   a   ,   a						)
				self.update(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max(
				    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
				return True
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							int | float:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[Any]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
				if right_element < a or left_element > b:
					return -math.inf
				if left_element >= a and right_element <= b:
					return self.segment_tree[idx]
				SCREAMING_SNAKE_CASE :   Dict					  =							(left_element + right_element) // 2
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.left(a						)   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a						)
				return max(a   ,   a						)






			def __str__(      self				:			str						)				->							str:

				"""simple docstring"""

				return str([self.query(1   ,   1   ,   self.size   ,   a   ,   a						) for i in range(1   ,   self.size + 1						)]						)


if __name__ == "__main__":
			a_      =     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			a_      =     15
			a_      =     SegmentTree(size)
			segt.build(1, 1, size, A)
			print(segt.query(1, 1, size, 4, 6))
			print(segt.query(1, 1, size, 7, 11))
			print(segt.query(1, 1, size, 7, 12))
			segt.update(1, 1, size, 1, 3, 111)
			print(segt.query(1, 1, size, 1, 15))
			segt.update(1, 1, size, 7, 8, 235)
			print(segt)