
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class        lowerCAmelCase__      (   __UpperCAmelCase	):
						UpperCamelCase_							: int	=		["""vqvae"""]

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__()
								self.register_modules(unet=UpperCAmelCase_       ,							scheduler=UpperCAmelCase_       ,							mel=UpperCAmelCase_       ,							vqvae=UpperCAmelCase_				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								return 50 if isinstance(self.scheduler       ,							UpperCAmelCase_				) else 10_00

						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = None       ,							a = None       ,							a = 0       ,							a = 0       ,							a = None       ,							a = None       ,							a = 0       ,							a = 0       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a=True       ,							)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	steps or self.get_default_steps()
								self.scheduler.set_timesteps(UpperCAmelCase_				)
								_UpperCamelCase						    =	step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size				) == int:
										_UpperCamelCase						    =	(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
										_UpperCamelCase						    =	randn_tensor(
										    (
										        batch_size,
										        self.unet.config.in_channels,
										        self.unet.config.sample_size[0],
										        self.unet.config.sample_size[1],
										    )       ,							generator=UpperCAmelCase_       ,							device=self.device       ,							)
								_UpperCamelCase						    =	noise
								_UpperCamelCase						    =	None

								if audio_file is not None or raw_audio is not None:
										self.mel.load_audio(UpperCAmelCase_       ,							UpperCAmelCase_				)
										_UpperCamelCase						    =	self.mel.audio_slice_to_image(UpperCAmelCase_				)
										_UpperCamelCase						    =	np.frombuffer(input_image.tobytes()       ,							dtype="""uint8"""				).reshape(
										    (input_image.height, input_image.width)				)
										_UpperCamelCase						    =	(input_image / 2_55) * 2 - 1
										_UpperCamelCase						    =	torch.tensor(input_image[np.newaxis, :, :]       ,							dtype=torch.float				).to(self.device				)

										if self.vqvae is not None:
												_UpperCamelCase						    =	self.vqvae.encode(torch.unsqueeze(UpperCAmelCase_       ,							0				)				).latent_dist.sample(
												    generator=UpperCAmelCase_				)[0]
												_UpperCamelCase						    =	self.vqvae.config.scaling_factor * input_images

										if start_step > 0:
												_UpperCamelCase						    =	self.scheduler.add_noise(UpperCAmelCase_       ,							UpperCAmelCase_       ,							self.scheduler.timesteps[start_step - 1]				)

										_UpperCamelCase						    =	(
										    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
										)
										_UpperCamelCase						    =	int(mask_start_secs * pixels_per_second				)
										_UpperCamelCase						    =	int(mask_end_secs * pixels_per_second				)
										_UpperCamelCase						    =	self.scheduler.add_noise(UpperCAmelCase_       ,							UpperCAmelCase_       ,							torch.tensor(self.scheduler.timesteps[start_step:]				)				)

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
										if isinstance(self.unet       ,							UpperCAmelCase_				):
												_UpperCamelCase						    =	self.unet(UpperCAmelCase_       ,							UpperCAmelCase_       ,							UpperCAmelCase_				)["""sample"""]
										else:
												_UpperCamelCase						    =	self.unet(UpperCAmelCase_       ,							UpperCAmelCase_				)["""sample"""]

										if isinstance(self.scheduler       ,							UpperCAmelCase_				):
												_UpperCamelCase						    =	self.scheduler.step(
												    model_output=UpperCAmelCase_       ,							timestep=UpperCAmelCase_       ,							sample=UpperCAmelCase_       ,							eta=UpperCAmelCase_       ,							generator=UpperCAmelCase_       ,							)["""prev_sample"""]
										else:
												_UpperCamelCase						    =	self.scheduler.step(
												    model_output=UpperCAmelCase_       ,							timestep=UpperCAmelCase_       ,							sample=UpperCAmelCase_       ,							generator=UpperCAmelCase_       ,							)["""prev_sample"""]

										if mask is not None:
												if mask_start > 0:
														_UpperCamelCase						    =	mask[:, step, :, :mask_start]
												if mask_end > 0:
														_UpperCamelCase						    =	mask[:, step, :, -mask_end:]

								if self.vqvae is not None:
										# 0.18215 was scaling factor used in training to ensure unit variance
										_UpperCamelCase						    =	1 / self.vqvae.config.scaling_factor * images
										_UpperCamelCase						    =	self.vqvae.decode(UpperCAmelCase_				)["""sample"""]

								_UpperCamelCase						    =	(images / 2 + 0.5).clamp(0       ,							1				)
								_UpperCamelCase						    =	images.cpu().permute(0       ,							2       ,							3       ,							1				).numpy()
								_UpperCamelCase						    =	(images * 2_55).round().astype("""uint8"""				)
								_UpperCamelCase						    =	list(
								    (Image.fromarray(_[:, :, 0]				) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(UpperCAmelCase_       ,							mode="""RGB"""				).convert("""L"""				) for _ in images)				)

								_UpperCamelCase						    =	[self.mel.image_to_audio(UpperCAmelCase_				) for _ in images]
								if not return_dict:
										return images, (self.mel.get_sample_rate(), audios)

								return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase_				)[:, np.newaxis, :]				)       ,							**ImagePipelineOutput(UpperCAmelCase_				)				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a = 50				)					->      Optional[int]:


								'''simple docstring'''

								assert isinstance(self.scheduler       ,							UpperCAmelCase_				)
								self.scheduler.set_timesteps(UpperCAmelCase_				)
								_UpperCamelCase						    =	np.array(
								    [np.frombuffer(image.tobytes()       ,							dtype="""uint8"""				).reshape((1, image.height, image.width)				) for image in images]				)
								_UpperCamelCase						    =	(sample / 2_55) * 2 - 1
								_UpperCamelCase						    =	torch.Tensor(UpperCAmelCase_				).to(self.device				)

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,							(0,)				)				):
										_UpperCamelCase						    =	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
										_UpperCamelCase						    =	self.scheduler.alphas_cumprod[t]
										_UpperCamelCase						    =	(
										    self.scheduler.alphas_cumprod[prev_timestep]
										    if prev_timestep >= 0
										    else self.scheduler.final_alpha_cumprod
										)
										_UpperCamelCase						    =	1 - alpha_prod_t
										_UpperCamelCase						    =	self.unet(UpperCAmelCase_       ,							UpperCAmelCase_				)["""sample"""]
										_UpperCamelCase						    =	(1 - alpha_prod_t_prev) ** 0.5 * model_output
										_UpperCamelCase						    =	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
										_UpperCamelCase						    =	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample







						@staticmethod
						def  A_				(			a       ,							a       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	acos(torch.dot(torch.flatten(UpperCAmelCase_				)       ,							torch.flatten(UpperCAmelCase_				)				) / torch.norm(UpperCAmelCase_				) / torch.norm(UpperCAmelCase_				)				)
								return sin((1 - alpha) * theta				) * xa / sin(UpperCAmelCase_				) + sin(alpha * theta				) * xa / sin(UpperCAmelCase_				)



from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# 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(lowerCAmelCase ):
				# 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]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



