





"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 					_UpperCAmelCase   (	unittest.TestCase ):


					def 	lowerCamelCase       (					self			:int		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					@property
					def 	lowerCamelCase       (					self			:str		):
												torch.manual_seed(0		)
												A		=  UNetaDModel(
												    sample_size=(32, 64)     ,       in_channels=1     ,       out_channels=1     ,       layers_per_block=2     ,       block_out_channels=(1_28, 1_28)     ,       down_block_types=("AttnDownBlock2D", "DownBlock2D")     ,       up_block_types=("UpBlock2D", "AttnUpBlock2D")     ,       )
												return model


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												torch.manual_seed(0		)
												A		=  UNetaDConditionModel(
												    sample_size=(64, 32)     ,       in_channels=1     ,       out_channels=1     ,       layers_per_block=2     ,       block_out_channels=(1_28, 1_28)     ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")     ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")     ,       cross_attention_dim=10     ,       )
												return model


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												torch.manual_seed(0		)
												A		=  AutoencoderKL(
												    sample_size=(1_28, 64)     ,       in_channels=1     ,       out_channels=1     ,       latent_channels=1     ,       layers_per_block=2     ,       block_out_channels=(1_28, 1_28)     ,       down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")     ,       up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")     ,       )
												A		=  UNetaDModel(
												    sample_size=(64, 32)     ,       in_channels=1     ,       out_channels=1     ,       layers_per_block=2     ,       block_out_channels=(1_28, 1_28)     ,       down_block_types=("AttnDownBlock2D", "DownBlock2D")     ,       up_block_types=("UpBlock2D", "AttnUpBlock2D")     ,       )
												return vqvae, unet


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  "cpu"  # ensure determinism for the device-dependent torch.Generator
												A		=  Mel(
												    x_res=self.dummy_unet.config.sample_size[1]     ,       y_res=self.dummy_unet.config.sample_size[0]     ,       )

												A		=  DDPMScheduler()
												A		=  AudioDiffusionPipeline(vqvae=__UpperCamelCase     ,       unet=self.dummy_unet     ,       mel=__UpperCamelCase     ,       scheduler=__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(42		)
												A		=  pipe(generator=__UpperCamelCase     ,       steps=4		)
												A		=  output.audios[0]
												A		=  output.images[0]

												A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(42		)
												A		=  pipe(generator=__UpperCamelCase     ,       steps=4     ,       return_dict=__UpperCamelCase		)
												A		=  output[0][0]

												assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
												assert (
												    image.height == self.dummy_unet.config.sample_size[0]
												    and image.width == self.dummy_unet.config.sample_size[1]
												)
												A		=  np.frombuffer(image.tobytes()     ,       dtype="uint8"		)[:10]
												A		=  np.frombuffer(image_from_tuple.tobytes()     ,       dtype="uint8"		)[:10]
												A		=  np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

												A		=  Mel(
												    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]     ,       y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]     ,       )

												A		=  DDIMScheduler()
												A		=  self.dummy_vqvae_and_unet
												A		=  AudioDiffusionPipeline(
												    vqvae=self.dummy_vqvae_and_unet[0]     ,       unet=dummy_vqvae_and_unet[1]     ,       mel=__UpperCamelCase     ,       scheduler=__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												np.random.seed(0		)
												A		=  np.random.uniform(-1     ,       1     ,       ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
												A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(42		)
												A		=  pipe(raw_audio=__UpperCamelCase     ,       generator=__UpperCamelCase     ,       start_step=5     ,       steps=10		)
												A		=  output.images[0]

												assert (
												    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
												    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
												)
												A		=  np.frombuffer(image.tobytes()     ,       dtype="uint8"		)[:10]
												A		=  np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

												A		=  self.dummy_unet_condition
												A		=  AudioDiffusionPipeline(
												    vqvae=self.dummy_vqvae_and_unet[0]     ,       unet=__UpperCamelCase     ,       mel=__UpperCamelCase     ,       scheduler=__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												np.random.seed(0		)
												A		=  torch.rand((1, 1, 10)		)
												A		=  pipe(generator=__UpperCamelCase     ,       encoding=__UpperCamelCase		)
												A		=  output.images[0]
												A		=  np.frombuffer(image.tobytes()     ,       dtype="uint8"		)[:10]
												A		=  np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() == 0







@slow
@require_torch_gpu
class 					_UpperCAmelCase   (	unittest.TestCase ):


					def 	lowerCamelCase       (					self			:str		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					def 	lowerCamelCase       (					self			:Any		):
												A		=  torch_device

												A		=  DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(42		)
												A		=  pipe(generator=__UpperCamelCase		)
												A		=  output.audios[0]
												A		=  output.images[0]

												assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
												assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
												A		=  np.frombuffer(image.tobytes()     ,       dtype="uint8"		)[:10]
												A		=  np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() == 0









"""simple docstring"""
from __future__ import annotations

_snake_case     :		str       						= []






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														if board[row][i] == 1:
																					return False
							for i in range(len(UpperCamelCase       )       ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  -1					,  -1       )       ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  len(UpperCamelCase       )       )       ):
														if board[i][j] == 1:
																					return False
							return True






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							if row >= len(UpperCamelCase       ):
														solution.append(UpperCamelCase       )
														printboard(UpperCamelCase       )
														print()
														return True
							for i in range(len(UpperCamelCase       )       ):
														if is_safe(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
																					A		=  1
																					solve(UpperCamelCase					,  row + 1       )
																					A		=  0
							return False






def 					A__				(			UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														for j in range(len(UpperCamelCase       )       ):
																					if board[i][j] == 1:
																												print("Q"					,  end=" "       )
																					else:
																												print("."					,  end=" "       )
														print()


# n=int(input("The no. of queens"))
_snake_case     :		List[str]       						= 8
_snake_case     :		List[str]       						= [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


