


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      int  				=			(IPNDMScheduler,)
							_lowercase     :      Optional[int]  				=			(('''num_inference_steps''', 50),)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						**UpperCamelCase_:     Optional[Any]							)      ->       int:





									"""simple docstring"""







									lowercase__						=				{'''num_train_timesteps''': 1_000}
									config.update(**UpperCamelCase_							)
									return config

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     int=0   ,						**UpperCamelCase_:     str							)      ->       List[str]:





									"""simple docstring"""







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

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

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

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				new_scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				new_scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Tuple:





									"""simple docstring"""







									pass

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[int]=0   ,						**UpperCamelCase_:     List[Any]							)      ->       List[str]:





									"""simple docstring"""







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

									for scheduler_class in self.scheduler_classes:
											lowercase__						=				self.get_scheduler_config()
											lowercase__						=				scheduler_class(**UpperCamelCase_							)
											scheduler.set_timesteps(UpperCamelCase_							)

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

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

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

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				new_scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				new_scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

							def 				lowerCamelCase_			(							self:     str   ,						**UpperCamelCase_:     Any							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.scheduler_classes[0]
									lowercase__						=				self.get_scheduler_config(**UpperCamelCase_							)
									lowercase__						=				scheduler_class(**UpperCamelCase_							)

									lowercase__						=				10
									lowercase__						=				self.dummy_model()
									lowercase__						=				self.dummy_sample_deter
									scheduler.set_timesteps(UpperCamelCase_							)

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

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

									return sample

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				dict(self.forward_default_kwargs							)

									lowercase__						=				kwargs.pop('''num_inference_steps'''   ,						UpperCamelCase_							)

									for scheduler_class in self.scheduler_classes:
											lowercase__						=				self.get_scheduler_config()
											lowercase__						=				scheduler_class(**UpperCamelCase_							)

											lowercase__						=				self.dummy_sample
											lowercase__						=				0.1 * sample

											if num_inference_steps is not None and hasattr(UpperCamelCase_   ,						'''set_timesteps'''							):
													scheduler.set_timesteps(UpperCamelCase_							)
											elif num_inference_steps is not None and not hasattr(UpperCamelCase_   ,						'''set_timesteps'''							):
													lowercase__						=				num_inference_steps

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

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample
											lowercase__						=				scheduler.step(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						**UpperCamelCase_							).prev_sample

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

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									for timesteps in [100, 1_000]:
											self.check_over_configs(num_train_timesteps=UpperCamelCase_   ,						time_step=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									for t, num_inference_steps in zip([1, 5, 10]   ,						[10, 50, 100]							):
											self.check_over_forward(num_inference_steps=UpperCamelCase_   ,						time_step=UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.full_loop()
									lowercase__						=				torch.mean(torch.abs(UpperCamelCase_							)							)

									assert abs(result_mean.item() - 2_540_529							) < 10






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class 	_a							:
							_lowercase     :      int
							_lowercase     :      TreeNode | None  				=			None
							_lowercase     :      TreeNode | None  				=			None
lowerCAmelCase	=							namedtuple('CoinsDistribResult', 'moves excess')
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if root is None:
				return 0

		# Validation
		def count_nodes(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_nodes(node.left					) + count_nodes(node.right					) + 1

		def count_coins(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_coins(node.left					) + count_coins(node.right					) + node.data

		if count_nodes(SCREAMING_SNAKE_CASE					) != count_coins(SCREAMING_SNAKE_CASE					):
				raise ValueError('''The nodes number should be same as the number of coins'''					)

		# Main calculation
		def get_distrib(SCREAMING_SNAKE_CASE					) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0						,    1					)

				lowercase__					,		lowercase__						=				get_distrib(node.left					)
				lowercase__					,		lowercase__						=				get_distrib(node.right					)

				lowercase__						=				1 - left_distrib_excess
				lowercase__						=				1 - right_distrib_excess

				lowercase__						=				(
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(SCREAMING_SNAKE_CASE					)
				    + abs(SCREAMING_SNAKE_CASE					)
				)
				lowercase__						=				node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		return get_distrib(SCREAMING_SNAKE_CASE					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


