

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       (UnCLIPScheduler,)


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			{
							    "num_train_timesteps": 1000,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**lowercase       )
							return config


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=lowercase       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
															if prev_timestep is not None and prev_timestep >= time_step:
																			continue

															self.check_over_forward(time_step=lowercase      ,      prev_timestep=lowercase       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.scheduler_classes[0]
							A__        =			self.get_scheduler_config(variance_type="fixed_small_log"       )
							A__        =			scheduler_class(**lowercase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.00_00e-10       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.054_9625       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.999_4987       )       ) < 1e-5


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.scheduler_classes[0]
							A__        =			self.get_scheduler_config(variance_type="learned_range"       )
							A__        =			scheduler_class(**lowercase       )

							A__        =			0.5

							assert scheduler._get_variance(1      ,      predicted_variance=lowercase       ) - -10.171_2790 < 1e-5
							assert scheduler._get_variance(487      ,      predicted_variance=lowercase       ) - -5.799_8052 < 1e-5
							assert scheduler._get_variance(999      ,      predicted_variance=lowercase       ) - -0.001_0011 < 1e-5


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.scheduler_classes[0]
							A__        =			self.get_scheduler_config()
							A__        =			scheduler_class(**lowercase       )

							A__        =			scheduler.timesteps

							A__        =			self.dummy_model()
							A__        =			self.dummy_sample_deter
							A__        =			torch.manual_seed(0       )

							for i, t in enumerate(lowercase       ):
											# 1. predict noise residual
											A__        =			model(lowercase      ,      lowercase       )

											# 2. predict previous mean of sample x_t-1
											A__        =			scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      generator=lowercase       ).prev_sample

											A__        =			pred_prev_sample

							A__        =			torch.sum(torch.abs(lowercase       )       )
							A__        =			torch.mean(torch.abs(lowercase       )       )

							assert abs(result_sum.item() - 252.268_2495       ) < 1e-2
							assert abs(result_mean.item() - 0.328_4743       ) < 1e-3


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.scheduler_classes[0]
							A__        =			self.get_scheduler_config()
							A__        =			scheduler_class(**lowercase       )

							scheduler.set_timesteps(25       )

							A__        =			scheduler.timesteps

							A__        =			self.dummy_model()
							A__        =			self.dummy_sample_deter
							A__        =			torch.manual_seed(0       )

							for i, t in enumerate(lowercase       ):
											# 1. predict noise residual
											A__        =			model(lowercase      ,      lowercase       )

											if i + 1 == timesteps.shape[0]:
															A__        =			None
											else:
															A__        =			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											A__        =			scheduler.step(
											    lowercase      ,      lowercase      ,      lowercase      ,      prev_timestep=lowercase      ,      generator=lowercase       ).prev_sample

											A__        =			pred_prev_sample

							A__        =			torch.sum(torch.abs(lowercase       )       )
							A__        =			torch.mean(torch.abs(lowercase       )       )

							assert abs(result_sum.item() - 258.204_4983       ) < 1e-2
							assert abs(result_mean.item() - 0.336_2038       ) < 1e-3


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							pass




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# If row is equal to the size of the board it means there are a queen in each row in
				# the current board (possible_board)
				if row == n:
								# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
								# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
								boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board]      )
								return

				# We iterate each column in the row to find all possible results in each row
				for col in range(SCREAMING_SNAKE_CASE_      ):
								# We apply that we learned previously. First we check that in the current board
								# (possible_board) there are not other same value because if there is it means
								# that there are a collision in vertical. Then we apply the two formulas we
								# learned before:
								#
								# 45º: y - x = b or 45: row - col = b
								# 135º: y + x = b or row + col = b.
								#
								# And we verify if the results of this two formulas not exist in their variables
								# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
								#
								# If any or these are True it means there is a collision so we continue to the
								# next value in the for loop.
								if (
								    col in possible_board
								    or row - col in diagonal_right_collisions
								    or row + col in diagonal_left_collisions
								):
												continue

								# If it is False we call dfs function again and we update the inputs
								depth_first_search(
								    [*possible_board, col]	,       [*diagonal_right_collisions, row - col]	,       [*diagonal_left_collisions, row + col]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

